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principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files.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