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Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/linnefromice/claude-code-workspace --skill rules-distill命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| 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 |
インストール済みのスキルをスキャンし、複数のスキルに共通する横断的な原則を抽出し、それらをルールとして蒸留します — 既存のルールファイルへの追記、古くなった内容の修正、または新規ルールファイルの作成を行います。
「決定的な収集 + LLM 判断」の原則を適用します: スクリプトが網羅的に事実を収集し、その後 LLM が全コンテキストを横断的に読み取って判定を行います。
ルール蒸留プロセスは3つのフェーズに従います:
bash ~/.claude/skills/rules-distill/scripts/scan-skills.sh
bash ~/.claude/skills/rules-distill/scripts/scan-rules.sh
Rules Distillation — Phase 1: Inventory
────────────────────────────────────────
Skills: {N} files scanned
Rules: {M} files ({K} headings indexed)
Proceeding to cross-read analysis...
抽出とマッチングは単一パスで統合されます。ルールファイルは十分に小さい（合計約800行）ため、全文を LLM に提供できます — grep による事前フィルタリングは不要です。
スキルを説明に基づいたテーマクラスターにグループ化します。各クラスターをルール全文と共にサブエージェントで分析します。
すべてのバッチ完了後、バッチ間で候補をマージします:
以下のプロンプトで汎用 Agent を起動します:
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)
| 判定 | 意味 | ユーザーへの提示内容 |
|---|---|---|
| Append | 既存セクションに追加 | 対象 + ドラフト |
| Revise | 不正確/不十分な内容を修正 | 対象 + 理由 + 修正前/修正後 |
| New Section | 既存ファイルに新規セクションを追加 | 対象 + ドラフト |
| New File | 新規ルールファイルを作成 | ファイル名 + 完全なドラフト |
| Already Covered | ルールでカバー済み（表現が異なる可能性あり） | 理由（1行） |
| Too Specific | スキルに留めるべき | 関連スキルへのリンク |
# 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
# 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)
ユーザーは番号で以下を回答します:
ルールを自動的に変更しないでください。常にユーザーの承認を必要とします。
結果をスキルディレクトリに保存します（results.json）:
date -u +%Y-%m-%dT%H:%M:%SZ（UTC、秒精度）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"
}
}
}
$ /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
See skill: [name] の参照を含め、読者が詳細な How を見つけられるようにします。