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principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files.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

{

"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.