rules-distill

name rules-distill description Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files. Use when the same principle keeps recurring across skills and belongs in a rule file instead. metadata {"origin":"ECC"} 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principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files.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.