---

name: rules-distill description: "Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files" 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principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files.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 injectio