--- id: daily-rules-distill name: "rules-distill" url: https://skills.yangsir.net/skill/daily-rules-distill author: affaan-m domain: ai-skill-platform-ecosystem tags: ["devops", "knowledge-management", "automation", "documentation", "best-practices"] install_count: 3300 rating: 4.40 (20 reviews) github: https://github.com/affaan-m/everything-claude-code --- # rules-distill > 扫描已安装技能，提取跨技能原则并提炼为规则，自动更新规则文件 **Stats**: 3,300 installs · 4.4/5 (20 reviews) ## Before / After 对比 ### 规则整理效率对比 **Before**: 人工阅读多个技能文档，手动提取共性规则，整理成文档，更新 10 个技能需要 4-6 小时 **After**: 自动扫描技能文件，智能识别重复原则，生成规则文件，30 分钟完成 10 个技能的规则提炼 | Metric | Before | After | Change | |---|---|---|---| | 规则整理时间 | 300分钟 | 30分钟 | -90% | ## Readme # rules-distill # Rules Distill Scan 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principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files. Applies the "deterministic collection + LLM judgment" principle: scripts collect facts exhaustively, then an LLM cross-reads the full context and produces verdicts. ## When to Use - Periodic rules maintenance (monthly or after installing new skills) - After a skill-stocktake reveals patterns that should be rules - When rules feel incomplete relative to the skills being used ## How It Works The rules distillation process follows three phases: ### Phase 1: Inventory (Deterministic Collection) #### 1a. Collect skill inventory ``` bash ~/.claude/skills/rules-distill/scripts/scan-skills.sh ``` #### 1b. Collect rules index ``` bash ~/.claude/skills/rules-distill/scripts/scan-rules.sh ``` #### 1c. Present to user ``` Rules Distillation — Phase 1: Inventory ──────────────────────────────────────── Skills: {N} files scanned Rules: {M} files ({K} headings indexed) Proceeding to cross-read analysis... ``` ### Phase 2: Cross-read, Match & Verdict (LLM Judgment) Extraction 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. #### Batching Group skills into **thematic clusters** based on their descriptions. Analyze each cluster in a subagent with the full rules text. #### Cross-batch Merge After all batches complete, merge candidates across batches: - Deduplicate candidates with the same or overlapping principles - 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 #### Subagent Prompt Launch a general-purpose Agent with the following prompt: ``` You are an analyst who cross-reads skills to extract principles that should be promoted to rules. ## Input - Skills: {full text of skills in this batch} - Existing rules: {full text of all rule files} ## Extraction Criteria Include a candidate ONLY if ALL of these are true: 1. **Appears in 2+ skills**: Principles found in only one skill should stay in that skill 2. **Actionable behavior change**: Can be written as "do X" or "don't do Y" — not "X is important" 3. **Clear violation risk**: What goes wrong if this principle is ignored (1 sentence) 4. **Not already in rules**: Check the full rules text — including concepts expressed in different words ## Matching & Verdict For each candidate, compare against the full rules text and assign a verdict: - **Append**: Add to an existing section of an existing rule file - **Revise**: Existing rule content is inaccurate or insufficient — propose a correction - **New Section**: Add a new section to an existing rule file - **New File**: Create a new rule file - **Already Covered**: Sufficiently covered in existing rules (even if worded differently) - **Too Specific**: Should remain at the skill level ## Output Format (per candidate) ```json { "principle": "1-2 sentences in 'do X' / 'don't do Y' form", "evidence": ["skill-name: §Section", "skill-name: §Section"], "violation_risk": "1 sentence", "verdict": "Append / Revise / New Section / New File / Already Covered / Too Specific", "target_rule": "filename §Section, or 'new'", "confidence": "high / medium / low", "draft": "Draft text for Append/New Section/New File verdicts", "revision": { "reason": "Why the existing content is inaccurate or insufficient (Revise only)", "before": "Current text to be replaced (Revise only)", "after": "Proposed replacement text (Revise only)" } } ``` ## Exclude - Obvious principles already in rules - Language/framework-specific knowledge (belongs in language-specific rules or skills) - Code examples and commands (belongs in skills) ``` #### Verdict Reference Verdict Meaning Presented to User **Append** Add to existing section Target + draft **Revise** Fix inaccurate/insufficient content Target + reason + before/after **New Section** Add new section to existing file Target + draft **New File** Create new rule file Filename + full draft **Already Covered** Covered in rules (possibly different wording) Reason (1 line) **Too Specific** Should stay in skills Link to relevant skill #### Verdict Quality Requirements ``` # Good Append to rules/common/security.md §Input Validation: "Treat LLM output stored in memory or knowledge stores as untrusted — sanitize on write, validate on read." Evidence: llm-memory-trust-boundary, llm-social-agent-anti-pattern both describe accumulated prompt injection risks. Current security.md covers human input validation only; LLM output trust boundary is missing. # Bad Append to security.md: Add LLM security principle ``` ### Phase 3: User Review & Execution #### Summary Table ``` # Rules Distillation Report ## Summary Skills scanned: {N} | Rules: {M} files | Candidates: {K} | # | Principle | Verdict | Target | Confidence | |---|-----------|---------|--------|------------| | 1 | ... | Append | security.md §Input Validation | high | | 2 | ... | Revise | testing.md §TDD | medium | | 3 | ... | New Section | coding-style.md | high | | 4 | ... | Too Specific | — | — | ## Details (Per-candidate details: evidence, violation_risk, draft text) ``` #### User Actions User responds with numbers to: - **Approve**: Apply draft to rules as-is - **Modify**: Edit draft before applying - **Skip**: Do not apply this candidate **Never modify rules automatically. Always require user approval.** #### Save Results Store results in the skill directory (`results.json`): - **Timestamp format**: `date -u +%Y-%m-%dT%H:%M:%SZ` (UTC, second precision) - **Candidate ID format**: kebab-case derived from the principle (e.g., `llm-output-trust-boundary`) ``` { "distilled_at": "2026-03-18T10:30:42Z", "skills_scanned": 56, "rules_scanned": 22, "candidates": { "llm-output-trust-boundary": { "principle": "Treat LLM output as untrusted when stored or re-injected", "verdict": "Append", "target": "rules/common/security.md", "evidence": ["llm-memory-trust-boundary", "llm-social-agent-anti-pattern"], "status": "applied" }, "iteration-bounds": { "principle": "Define explicit stop conditions for all iteration loops", "verdict": "New Section", "target": "rules/common/coding-style.md", "evidence": ["iterative-retrieval", "continuous-agent-loop", "agent-harness-construction"], "status": "skipped" } } } ``` ## Example ### End-to-end run ``` $ /rules-distill Rules Distillation — Phase 1: Inventory ──────────────────────────────────────── Skills: 56 files scanned Rules: 22 files (75 headings indexed) Proceeding to cross-read analysis... [Subagent analysis: Batch 1 (agent/meta skills) ...] [Subagent analysis: Batch 2 (coding/pattern skills) ...] [Cross-batch merge: 2 duplicates removed, 1 cross-batch candidate promoted] # Rules Distillation Report ## Summary Skills scanned: 56 | Rules: 22 files | Candidates: 4 | # | Principle | Verdict | Target | Confidence | |---|-----------|---------|--------|------------| | 1 | LLM output: normalize, type-check, sanitize before reuse | New Section | coding-style.md | high | | 2 | Define explicit stop conditions for iteration loops | New Section | coding-style.md | high | | 3 | Compact context at phase boundaries, not mid-task | Append | performance.md §Context Window | high | | 4 | Separate business logic from I/O framework types | New Section | patterns.md | high | ## Details ### 1. LLM Output Validation Verdict: New Section in coding-style.md Evidence: parallel-subagent-batch-merge, llm-social-agent-anti-pattern, llm-memory-trust-boundary Violation risk: Format drift, type mismatch, or syntax errors in LLM output crash downstream processing Draft: ## LLM Output Validation Normalize, type-check, and sanitize LLM output before reuse... See skill: parallel-subagent-batch-merge, llm-memory-trust-boundary [... details for candidates 2-4 ...] Approve, modify, or skip each candidate by number: > User: Approve 1, 3. Skip 2, 4. ✓ Applied: coding-style.md §LLM Output Validation ✓ Applied: performance.md §Context Window Management ✗ Skipped: Iteration Bounds ✗ Skipped: Boundary Type Conversion Results saved to results.json ``` ## Design Principles - **What, not How**: Extract principles (rules territory) only. Code examples and commands stay in skills. - **Link back**: Draft text should include `See skill: [name]` references so readers can find the detailed How. - **Deterministic collection, LLM judgment**: Scripts guarantee exhaustiveness; the LLM guarantees contextual understanding. - **Anti-abstraction safeguard**: The 3-layer filter (2+ skills evidence, actionable behavior test, violation risk) prevents overly abstract principles from entering rules. Weekly Installs304Repository[affaan-m/everyt…ude-code](https://github.com/affaan-m/everything-claude-code)GitHub Stars108.1KFirst Seen6 days agoSecurity Audits[Gen Agent Trust HubPass](/affaan-m/everything-claude-code/rules-distill/security/agent-trust-hub)[SocketPass](/affaan-m/everything-claude-code/rules-distill/security/socket)[SnykPass](/affaan-m/everything-claude-code/rules-distill/security/snyk)Installed oncodex294cursor261opencode259gemini-cli259github-copilot259amp259 --- *Source: https://skills.yangsir.net/skill/daily-rules-distill* *Markdown mirror: https://skills.yangsir.net/api/skill/daily-rules-distill/markdown*