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principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files.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.

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