[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M730SS4F4VCD44CFFTYHV3KB":3,"skill-same:01M730SS4F4VCD44CFFTYHV3KB":58},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":25,"commitSha":26,"skillDirectory":27,"forksCount":28,"repositoryHtmlUrl":29,"repositoryHomepageUrl":6,"ownerHtmlUrl":30,"ownerBlogUrl":31,"ownerDescription":32,"updatedAt":33,"install":34,"resources":47},"01M730SS4F4VCD44CFFTYHV3KB","rules-distill",null,"Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files","SriramGandhiS\u002FSkills","SriramGandhiS","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F211849986?v=4","01M72WQCW12BYX71C6WN584TTA","Sriram S","01M72WQCWTJ2RD0ET1X31AF1RC",1,"MIT","https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fskills\u002Feverything-claude-code\u002Fskills\u002Frules-distill\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: rules-distill\ndescription: \"Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files\"\norigin: ECC\n---\n\n# Rules Distill\n\nScan installed skills, extract cross-cutting principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files.\n\nApplies the \"deterministic collection + LLM judgment\" principle: scripts collect facts exhaustively, then an LLM cross-reads the full context and produces verdicts.\n\n## When to Use\n\n- Periodic rules maintenance (monthly or after installing new skills)\n- After a skill-stocktake reveals patterns that should be rules\n- When rules feel incomplete relative to the skills being used\n\n## How It Works\n\nThe rules distillation process follows three phases:\n\n### Phase 1: Inventory (Deterministic Collection)\n\n#### 1a. Collect skill inventory\n\n```bash\nbash ~\u002F.claude\u002Fskills\u002Frules-distill\u002Fscripts\u002Fscan-skills.sh\n```\n\n#### 1b. Collect rules index\n\n```bash\nbash ~\u002F.claude\u002Fskills\u002Frules-distill\u002Fscripts\u002Fscan-rules.sh\n```\n\n#### 1c. Present to user\n\n```\nRules Distillation — Phase 1: Inventory\n────────────────────────────────────────\nSkills: {N} files scanned\nRules:  {M} files ({K} headings indexed)\n\nProceeding to cross-read analysis...\n```\n\n### Phase 2: Cross-read, Match & Verdict (LLM Judgment)\n\nExtraction and matching are unified in a single pass. Rules files are small enough (~800 lines total) that the full text can be provided to the LLM — no grep pre-filtering needed.\n\n#### Batching\n\nGroup skills into **thematic clusters** based on their descriptions. Analyze each cluster in a subagent with the full rules text.\n\n#### Cross-batch Merge\n\nAfter all batches complete, merge candidates across batches:\n- Deduplicate candidates with the same or overlapping principles\n- Re-check the \"2+ skills\" requirement using evidence from **all** batches combined — a principle found in 1 skill per batch but 2+ skills total is valid\n\n#### Subagent Prompt\n\nLaunch a general-purpose Agent with the following prompt:\n\n````\nYou are an analyst who cross-reads skills to extract principles that should be promoted to rules.\n\n## Input\n- Skills: {full text of skills in this batch}\n- Existing rules: {full text of all rule files}\n\n## Extraction Criteria\n\nInclude a candidate ONLY if ALL of these are true:\n\n1. **Appears in 2+ skills**: Principles found in only one skill should stay in that skill\n2. **Actionable behavior change**: Can be written as \"do X\" or \"don't do Y\" — not \"X is important\"\n3. **Clear violation risk**: What goes wrong if this principle is ignored (1 sentence)\n4. **Not already in rules**: Check the full rules text — including concepts expressed in different words\n\n## Matching & Verdict\n\nFor each candidate, compare against the full rules text and assign a verdict:\n\n- **Append**: Add to an existing section of an existing rule file\n- **Revise**: Existing rule content is inaccurate or insufficient — propose a correction\n- **New Section**: Add a new section to an existing rule file\n- **New File**: Create a new rule file\n- **Already Covered**: Sufficiently covered in existing rules (even if worded differently)\n- **Too Specific**: Should remain at the skill level\n\n## Output Format (per candidate)\n\n```json\n{\n  \"principle\": \"1-2 sentences in 'do X' \u002F 'don't do Y' form\",\n  \"evidence\": [\"skill-name: §Section\", \"skill-name: §Section\"],\n  \"violation_risk\": \"1 sentence\",\n  \"verdict\": \"Append \u002F Revise \u002F New Section \u002F New File \u002F Already Covered \u002F Too Specific\",\n  \"target_rule\": \"filename §Section, or 'new'\",\n  \"confidence\": \"high \u002F medium \u002F low\",\n  \"draft\": \"Draft text for Append\u002FNew Section\u002FNew File verdicts\",\n  \"revision\": {\n    \"reason\": \"Why the existing content is inaccurate or insufficient (Revise only)\",\n    \"before\": \"Current text to be replaced (Revise only)\",\n    \"after\": \"Proposed replacement text (Revise only)\"\n  }\n}\n```\n\n## Exclude\n\n- Obvious principles already in rules\n- Language\u002Fframework-specific knowledge (belongs in language-specific rules or skills)\n- Code examples and commands (belongs in skills)\n````\n\n#### Verdict Reference\n\n| Verdict | Meaning | Presented to User |\n|---------|---------|-------------------|\n| **Append** | Add to existing section | Target + draft |\n| **Revise** | Fix inaccurate\u002Finsufficient content | Target + reason + before\u002Fafter |\n| **New Section** | Add new section to existing file | Target + draft |\n| **New File** | Create new rule file | Filename + full draft |\n| **Already Covered** | Covered in rules (possibly different wording) | Reason (1 line) |\n| **Too Specific** | Should stay in skills | Link to relevant skill |\n\n#### Verdict Quality Requirements\n\n```\n# Good\nAppend to rules\u002Fcommon\u002Fsecurity.md §Input Validation:\n\"Treat LLM output stored in memory or knowledge stores as untrusted — sanitize on write, validate on read.\"\nEvidence: llm-memory-trust-boundary, llm-social-agent-anti-pattern both describe\naccumulated prompt injection risks. Current security.md covers human input\nvalidation only; LLM output trust boundary is missing.\n\n# Bad\nAppend to security.md: Add LLM security principle\n```\n\n### Phase 3: User Review & Execution\n\n#### Summary Table\n\n```\n# Rules Distillation Report\n\n## Summary\nSkills scanned: {N} | Rules: {M} files | Candidates: {K}\n\n| # | Principle | Verdict | Target | Confidence |\n|---|-----------|---------|--------|------------|\n| 1 | ... | Append | security.md §Input Validation | high |\n| 2 | ... | Revise | testing.md §TDD | medium |\n| 3 | ... | New Section | coding-style.md | high |\n| 4 | ... | Too Specific | — | — |\n\n## Details\n(Per-candidate details: evidence, violation_risk, draft text)\n```\n\n#### User Actions\n\nUser responds with numbers to:\n- **Approve**: Apply draft to rules as-is\n- **Modify**: Edit draft before applying\n- **Skip**: Do not apply this candidate\n\n**Never modify rules automatically. Always require user approval.**\n\n#### Save Results\n\nStore results in the skill directory (`results.json`):\n\n- **Timestamp format**: `date -u +%Y-%m-%dT%H:%M:%SZ` (UTC, second precision)\n- **Candidate ID format**: kebab-case derived from the principle (e.g., `llm-output-trust-boundary`)\n\n```json\n{\n  \"distilled_at\": \"2026-03-18T10:30:42Z\",\n  \"skills_scanned\": 56,\n  \"rules_scanned\": 22,\n  \"candidates\": {\n    \"llm-output-trust-boundary\": {\n      \"principle\": \"Treat LLM output as untrusted when stored or re-injected\",\n      \"verdict\": \"Append\",\n      \"target\": \"rules\u002Fcommon\u002Fsecurity.md\",\n      \"evidence\": [\"llm-memory-trust-boundary\", \"llm-social-agent-anti-pattern\"],\n      \"status\": \"applied\"\n    },\n    \"iteration-bounds\": {\n      \"principle\": \"Define explicit stop conditions for all iteration loops\",\n      \"verdict\": \"New Section\",\n      \"target\": \"rules\u002Fcommon\u002Fcoding-style.md\",\n      \"evidence\": [\"iterative-retrieval\", \"continuous-agent-loop\", \"agent-harness-construction\"],\n      \"status\": \"skipped\"\n    }\n  }\n}\n```\n\n## Example\n\n### End-to-end run\n\n```\n$ \u002Frules-distill\n\nRules Distillation — Phase 1: Inventory\n────────────────────────────────────────\nSkills: 56 files scanned\nRules:  22 files (75 headings indexed)\n\nProceeding to cross-read analysis...\n\n[Subagent analysis: Batch 1 (agent\u002Fmeta skills) ...]\n[Subagent analysis: Batch 2 (coding\u002Fpattern skills) ...]\n[Cross-batch merge: 2 duplicates removed, 1 cross-batch candidate promoted]\n\n# Rules Distillation Report\n\n## Summary\nSkills scanned: 56 | Rules: 22 files | Candidates: 4\n\n| # | Principle | Verdict | Target | Confidence |\n|---|-----------|---------|--------|------------|\n| 1 | LLM output: normalize, type-check, sanitize before reuse | New Section | coding-style.md | high |\n| 2 | Define explicit stop conditions for iteration loops | New Section | coding-style.md | high |\n| 3 | Compact context at phase boundaries, not mid-task | Append | performance.md §Context Window | high |\n| 4 | Separate business logic from I\u002FO framework types | New Section | patterns.md | high |\n\n## Details\n\n### 1. LLM Output Validation\nVerdict: New Section in coding-style.md\nEvidence: parallel-subagent-batch-merge, llm-social-agent-anti-pattern, llm-memory-trust-boundary\nViolation risk: Format drift, type mismatch, or syntax errors in LLM output crash downstream processing\nDraft:\n  ## LLM Output Validation\n  Normalize, type-check, and sanitize LLM output before reuse...\n  See skill: parallel-subagent-batch-merge, llm-memory-trust-boundary\n\n[... details for candidates 2-4 ...]\n\nApprove, modify, or skip each candidate by number:\n> User: Approve 1, 3. Skip 2, 4.\n\n✓ Applied: coding-style.md §LLM Output Validation\n✓ Applied: performance.md §Context Window Management\n✗ Skipped: Iteration Bounds\n✗ Skipped: Boundary Type Conversion\n\nResults saved to results.json\n```\n\n## Design Principles\n\n- **What, not How**: Extract principles (rules territory) only. Code examples and commands stay in skills.\n- **Link back**: Draft text should include `See skill: [name]` references so readers can find the detailed How.\n- **Deterministic collection, LLM judgment**: Scripts guarantee exhaustiveness; the LLM guarantees contextual understanding.\n- **Anti-abstraction safeguard**: The 3-layer filter (2+ skills evidence, actionable behavior test, violation risk) prevents overly abstract principles from entering rules.\n","# Rules Distill\n\nScan installed skills, extract cross-cutting principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files.\n\nApplies the \"deterministic collection + LLM judgment\" principle: scripts collect facts exhaustively, then an LLM cross-reads the full context and produces verdicts.\n\n## When to Use\n\n- Periodic rules maintenance (monthly or after installing new skills)\n- After a skill-stocktake reveals patterns that should be rules\n- When rules feel incomplete relative to the skills being used\n\n## How It Works\n\nThe rules distillation process follows three phases:\n\n### Phase 1: Inventory (Deterministic Collection)\n\n#### 1a. Collect skill inventory\n\n```bash\nbash ~\u002F.claude\u002Fskills\u002Frules-distill\u002Fscripts\u002Fscan-skills.sh\n```\n\n#### 1b. Collect rules index\n\n```bash\nbash ~\u002F.claude\u002Fskills\u002Frules-distill\u002Fscripts\u002Fscan-rules.sh\n```\n\n#### 1c. Present to user\n\n```\nRules Distillation — Phase 1: Inventory\n────────────────────────────────────────\nSkills: {N} files scanned\nRules:  {M} files ({K} headings indexed)\n\nProceeding to cross-read analysis...\n```\n\n### Phase 2: Cross-read, Match & Verdict (LLM Judgment)\n\nExtraction and matching are unified in a single pass. Rules files are small enough (~800 lines total) that the full text can be provided to the LLM — no grep pre-filtering needed.\n\n#### Batching\n\nGroup skills into **thematic clusters** based on their descriptions. Analyze each cluster in a subagent with the full rules text.\n\n#### Cross-batch Merge\n\nAfter all batches complete, merge candidates across batches:\n- Deduplicate candidates with the same or overlapping principles\n- Re-check the \"2+ skills\" requirement using evidence from **all** batches combined — a principle found in 1 skill per batch but 2+ skills total is valid\n\n#### Subagent Prompt\n\nLaunch a general-purpose Agent with the following prompt:\n\n````\nYou are an analyst who cross-reads skills to extract principles that should be promoted to rules.\n\n## Input\n- Skills: {full text of skills in this batch}\n- Existing rules: {full text of all rule files}\n\n## Extraction Criteria\n\nInclude a candidate ONLY if ALL of these are true:\n\n1. **Appears in 2+ skills**: Principles found in only one skill should stay in that skill\n2. **Actionable behavior change**: Can be written as \"do X\" or \"don't do Y\" — not \"X is important\"\n3. **Clear violation risk**: What goes wrong if this principle is ignored (1 sentence)\n4. **Not already in rules**: Check the full rules text — including concepts expressed in different words\n\n## Matching & Verdict\n\nFor each candidate, compare against the full rules text and assign a verdict:\n\n- **Append**: Add to an existing section of an existing rule file\n- **Revise**: Existing rule content is inaccurate or insufficient — propose a correction\n- **New Section**: Add a new section to an existing rule file\n- **New File**: Create a new rule file\n- **Already Covered**: Sufficiently covered in existing rules (even if worded differently)\n- **Too Specific**: Should remain at the skill level\n\n## Output Format (per candidate)\n\n```json\n{\n  \"principle\": \"1-2 sentences in 'do X' \u002F 'don't do Y' form\",\n  \"evidence\": [\"skill-name: §Section\", \"skill-name: §Section\"],\n  \"violation_risk\": \"1 sentence\",\n  \"verdict\": \"Append \u002F Revise \u002F New Section \u002F New File \u002F Already Covered \u002F Too Specific\",\n  \"target_rule\": \"filename §Section, or 'new'\",\n  \"confidence\": \"high \u002F medium \u002F low\",\n  \"draft\": \"Draft text for Append\u002FNew Section\u002FNew File verdicts\",\n  \"revision\": {\n    \"reason\": \"Why the existing content is inaccurate or insufficient (Revise only)\",\n    \"before\": \"Current text to be replaced (Revise only)\",\n    \"after\": \"Proposed replacement text (Revise only)\"\n  }\n}\n```\n\n## Exclude\n\n- Obvious principles already in rules\n- Language\u002Fframework-specific knowledge (belongs in language-specific rules or skills)\n- Code examples and commands (belongs in skills)\n````\n\n#### Verdict Reference\n\n| Verdict | Meaning | Presented to User |\n|---------|---------|-------------------|\n| **Append** | Add to existing section | Target + draft |\n| **Revise** | Fix inaccurate\u002Finsufficient content | Target + reason + before\u002Fafter |\n| **New Section** | Add new section to existing file | Target + draft |\n| **New File** | Create new rule file | Filename + full draft |\n| **Already Covered** | Covered in rules (possibly different wording) | Reason (1 line) |\n| **Too Specific** | Should stay in skills | Link to relevant skill |\n\n#### Verdict Quality Requirements\n\n```\n# Good\nAppend to rules\u002Fcommon\u002Fsecurity.md §Input Validation:\n\"Treat LLM output stored in memory or knowledge stores as untrusted — sanitize on write, validate on read.\"\nEvidence: llm-memory-trust-boundary, llm-social-agent-anti-pattern both describe\naccumulated prompt injection risks. Current security.md covers human input\nvalidation only; LLM output trust boundary is missing.\n\n# Bad\nAppend to security.md: Add LLM security principle\n```\n\n### Phase 3: User Review & Execution\n\n#### Summary Table\n\n```\n# Rules Distillation Report\n\n## Summary\nSkills scanned: {N} | Rules: {M} files | Candidates: {K}\n\n| # | Principle | Verdict | Target | Confidence |\n|---|-----------|---------|--------|------------|\n| 1 | ... | Append | security.md §Input Validation | high |\n| 2 | ... | Revise | testing.md §TDD | medium |\n| 3 | ... | New Section | coding-style.md | high |\n| 4 | ... | Too Specific | — | — |\n\n## Details\n(Per-candidate details: evidence, violation_risk, draft text)\n```\n\n#### User Actions\n\nUser responds with numbers to:\n- **Approve**: Apply draft to rules as-is\n- **Modify**: Edit draft before applying\n- **Skip**: Do not apply this candidate\n\n**Never modify rules automatically. Always require user approval.**\n\n#### Save Results\n\nStore results in the skill directory (`results.json`):\n\n- **Timestamp format**: `date -u +%Y-%m-%dT%H:%M:%SZ` (UTC, second precision)\n- **Candidate ID format**: kebab-case derived from the principle (e.g., `llm-output-trust-boundary`)\n\n```json\n{\n  \"distilled_at\": \"2026-03-18T10:30:42Z\",\n  \"skills_scanned\": 56,\n  \"rules_scanned\": 22,\n  \"candidates\": {\n    \"llm-output-trust-boundary\": {\n      \"principle\": \"Treat LLM output as untrusted when stored or re-injected\",\n      \"verdict\": \"Append\",\n      \"target\": \"rules\u002Fcommon\u002Fsecurity.md\",\n      \"evidence\": [\"llm-memory-trust-boundary\", \"llm-social-agent-anti-pattern\"],\n      \"status\": \"applied\"\n    },\n    \"iteration-bounds\": {\n      \"principle\": \"Define explicit stop conditions for all iteration loops\",\n      \"verdict\": \"New Section\",\n      \"target\": \"rules\u002Fcommon\u002Fcoding-style.md\",\n      \"evidence\": [\"iterative-retrieval\", \"continuous-agent-loop\", \"agent-harness-construction\"],\n      \"status\": \"skipped\"\n    }\n  }\n}\n```\n\n## Example\n\n### End-to-end run\n\n```\n$ \u002Frules-distill\n\nRules Distillation — Phase 1: Inventory\n────────────────────────────────────────\nSkills: 56 files scanned\nRules:  22 files (75 headings indexed)\n\nProceeding to cross-read analysis...\n\n[Subagent analysis: Batch 1 (agent\u002Fmeta skills) ...]\n[Subagent analysis: Batch 2 (coding\u002Fpattern skills) ...]\n[Cross-batch merge: 2 duplicates removed, 1 cross-batch candidate promoted]\n\n# Rules Distillation Report\n\n## Summary\nSkills scanned: 56 | Rules: 22 files | Candidates: 4\n\n| # | Principle | Verdict | Target | Confidence |\n|---|-----------|---------|--------|------------|\n| 1 | LLM output: normalize, type-check, sanitize before reuse | New Section | coding-style.md | high |\n| 2 | Define explicit stop conditions for iteration loops | New Section | coding-style.md | high |\n| 3 | Compact context at phase boundaries, not mid-task | Append | performance.md §Context Window | high |\n| 4 | Separate business logic from I\u002FO framework types | New Section | patterns.md | high |\n\n## Details\n\n### 1. LLM Output Validation\nVerdict: New Section in coding-style.md\nEvidence: parallel-subagent-batch-merge, llm-social-agent-anti-pattern, llm-memory-trust-boundary\nViolation risk: Format drift, type mismatch, or syntax errors in LLM output crash downstream processing\nDraft:\n  ## LLM Output Validation\n  Normalize, type-check, and sanitize LLM output before reuse...\n  See skill: parallel-subagent-batch-merge, llm-memory-trust-boundary\n\n[... details for candidates 2-4 ...]\n\nApprove, modify, or skip each candidate by number:\n> User: Approve 1, 3. Skip 2, 4.\n\n✓ Applied: coding-style.md §LLM Output Validation\n✓ Applied: performance.md §Context Window Management\n✗ Skipped: Iteration Bounds\n✗ Skipped: Boundary Type Conversion\n\nResults saved to results.json\n```\n\n## Design Principles\n\n- **What, not How**: Extract principles (rules territory) only. Code examples and commands stay in skills.\n- **Link back**: Draft text should include `See skill: [name]` references so readers can find the detailed How.\n- **Deterministic collection, LLM judgment**: Scripts guarantee exhaustiveness; the LLM guarantees contextual understanding.\n- **Anti-abstraction safeguard**: The 3-layer filter (2+ skills evidence, actionable behavior test, violation risk) prevents overly abstract principles from entering rules.\n",{"name":5,"origin":24,"description":7},"ECC","master","d6ca01da0f24a3b0e26e4a8b7b66cbd25d429f5d","skills\u002Feverything-claude-code\u002Fskills\u002Frules-distill",0,"https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills","https:\u002F\u002Fgithub.com\u002FSriramGandhiS","https:\u002F\u002Fsriram.website\u002F","Curious guy who keeps tinkering until something works","2026-05-20 16:21:34",{"source":8,"skillName":5,"commands":35,"prompt":45,"zipAvailable":46},[36,39,42],{"tool":37,"command":38},"npx","npx skills add SriramGandhiS\u002FSkills --skill rules-distill",{"tool":40,"command":41},"bunx","bunx skills add SriramGandhiS\u002FSkills --skill rules-distill",{"tool":43,"command":44},"pnpm","pnpm dlx skills add SriramGandhiS\u002FSkills --skill rules-distill","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fskills\u002Feverything-claude-code\u002Fskills\u002Frules-distill\u002FSKILL.md",true,[48,54],{"path":49,"resourceType":50,"resourceTypeDesc":51,"sizeBytes":52,"sourceHtmlUrl":53},"scripts\u002Fscan-rules.sh","SCRIPT","脚本",1602,"https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fskills\u002Feverything-claude-code\u002Fskills\u002Frules-distill\u002Fscripts\u002Fscan-rules.sh",{"path":55,"resourceType":50,"resourceTypeDesc":51,"sizeBytes":56,"sourceHtmlUrl":57},"scripts\u002Fscan-skills.sh",3860,"https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fskills\u002Feverything-claude-code\u002Fskills\u002Frules-distill\u002Fscripts\u002Fscan-skills.sh",[59,66,71,77,84,91],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":63,"categories":64,"keywords":65,"publishedAt":19},"01M732609EPJWV13PVTX3GT4TM","webgpu-threejs-tsl","Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). 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Use this skill when working with Three.js WebGPU, TSL shaders, node materials, or GPU compute in Three.js.","https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fwebgpu-threejs-tsl\u002Fskills\u002Fwebgpu-threejs-tsl\u002FSKILL.md",[],[],{"skillNo":67,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"publishedAt":19},"01M7325XM47AGWB6K6C4VXKE6B","https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fwebgpu-threejs-tsl\u002FSKILL.md",[],[],{"skillNo":72,"name":73,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M7325WP6DES69JKC840VMFEP","verification-loop","https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fweb-assets\u002Fdocs\u002Fzh-TW\u002Fskills\u002Fverification-loop\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M7325TFQF1DZTQ6J3WGQQ1VR","tdd-workflow","Use this skill when writing new features, fixing bugs, or refactoring code. Enforces test-driven development with 80%+ coverage including unit, integration, and E2E tests.","https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fweb-assets\u002Fdocs\u002Fzh-TW\u002Fskills\u002Ftdd-workflow\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M7325RGV9AY9PK92D2V8KKPW","strategic-compact","Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.","https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fweb-assets\u002Fdocs\u002Fzh-TW\u002Fskills\u002Fstrategic-compact\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":19},"01M7325MZC28SE86E7NMCNEFS4","security-review","Use this skill when adding authentication, handling user input, working with secrets, creating API endpoints, or implementing payment\u002Fsensitive features. Provides comprehensive security checklist and patterns.","https:\u002F\u002Fgithub.com\u002FSriramGandhiS\u002FSkills\u002Fblob\u002Fmaster\u002Fweb-assets\u002Fdocs\u002Fzh-TW\u002Fskills\u002Fsecurity-review\u002FSKILL.md",[],[]]