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principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files.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