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docs/zh-CN/skills/rules-distill/SKILL.md
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docs/zh-CN/skills/rules-distill/SKILL.md
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---
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name: rules-distill
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description: "扫描技能以提取跨领域原则并将其提炼为规则——追加、修订或创建新的规则文件"
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origin: ECC
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---
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# 规则提炼
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扫描已安装的技能，提取在多个技能中出现的通用原则，并将其提炼成规则——追加到现有规则文件中、修订过时内容或创建新的规则文件。
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应用"确定性收集 + LLM判断"原则：脚本详尽地收集事实，然后由LLM通读完整上下文并作出裁决。
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## 使用时机
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* 定期规则维护（每月或安装新技能后）
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* 技能盘点后，发现应成为规则的模式时
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* 当规则相对于正在使用的技能感觉不完整时
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## 工作原理
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规则提炼过程遵循三个阶段：
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### 阶段 1：清点（确定性收集）
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#### 1a. 收集技能清单
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```bash
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bash ~/.claude/skills/rules-distill/scripts/scan-skills.sh
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```
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#### 1b. 收集规则索引
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```bash
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bash ~/.claude/skills/rules-distill/scripts/scan-rules.sh
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```
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#### 1c. 呈现给用户
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```
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Rules Distillation — Phase 1: Inventory
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────────────────────────────────────────
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Skills: {N} files scanned
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Rules: {M} files ({K} headings indexed)
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Proceeding to cross-read analysis...
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```
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### 阶段 2：通读、匹配与裁决（LLM判断）
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提取和匹配在单次处理中统一完成。规则文件足够小（总计约800行），可以将全文提供给LLM——无需grep预过滤。
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#### 分批处理
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根据技能描述，将技能分组为**主题集群**。每个集群在一个子智能体中进行分析，并提供完整的规则文本。
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#### 跨批次合并
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所有批次完成后，合并各批次的候选规则：
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* 对具有相同或重叠原则的候选规则进行去重
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* 使用**所有**批次合并的证据重新检查"2+技能"要求——在每个批次中只在一个技能里发现，但总计在2+技能中出现的原则是有效的
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#### 子智能体提示
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使用以下提示启动通用智能体：
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````
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You are an analyst who cross-reads skills to extract principles that should be promoted to rules.
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## Input
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- Skills: {full text of skills in this batch}
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- Existing rules: {full text of all rule files}
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## Extraction Criteria
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Include a candidate ONLY if ALL of these are true:
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1. **Appears in 2+ skills**: Principles found in only one skill should stay in that skill
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2. **Actionable behavior change**: Can be written as "do X" or "don't do Y" — not "X is important"
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3. **Clear violation risk**: What goes wrong if this principle is ignored (1 sentence)
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4. **Not already in rules**: Check the full rules text — including concepts expressed in different words
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## Matching & Verdict
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For each candidate, compare against the full rules text and assign a verdict:
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- **Append**: Add to an existing section of an existing rule file
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- **Revise**: Existing rule content is inaccurate or insufficient — propose a correction
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- **New Section**: Add a new section to an existing rule file
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- **New File**: Create a new rule file
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- **Already Covered**: Sufficiently covered in existing rules (even if worded differently)
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- **Too Specific**: Should remain at the skill level
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## Output Format (per candidate)
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```json
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{
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"principle": "1-2 sentences in 'do X' / 'don't do Y' form",
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"evidence": ["skill-name: §Section", "skill-name: §Section"],
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"violation_risk": "1 sentence",
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"verdict": "Append / Revise / New Section / New File / Already Covered / Too Specific",
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"target_rule": "filename §Section, or 'new'",
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"confidence": "high / medium / low",
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"draft": "Draft text for Append/New Section/New File verdicts",
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"revision": {
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"reason": "Why the existing content is inaccurate or insufficient (Revise only)",
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"before": "Current text to be replaced (Revise only)",
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"after": "Proposed replacement text (Revise only)"
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}
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}
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```
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## Exclude
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- Obvious principles already in rules
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- Language/framework-specific knowledge (belongs in language-specific rules or skills)
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- Code examples and commands (belongs in skills)
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````
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#### 裁决参考
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| 裁决 | 含义 | 呈现给用户的内容 |
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|---------|---------|-------------------|
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| **追加** | 添加到现有章节 | 目标 + 草案 |
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| **修订** | 修复不准确/不充分的内容 | 目标 + 原因 + 修订前/后 |
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| **新章节** | 在现有文件中添加新章节 | 目标 + 草案 |
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| **新文件** | 创建新规则文件 | 文件名 + 完整草案 |
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| **已涵盖** | 规则中已涵盖（可能措辞不同） | 原因（1行） |
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| **过于具体** | 应保留在技能中 | 指向相关技能的链接 |
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#### 裁决质量要求
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```
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# Good
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Append to rules/common/security.md §Input Validation:
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"Treat LLM output stored in memory or knowledge stores as untrusted — sanitize on write, validate on read."
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Evidence: llm-memory-trust-boundary, llm-social-agent-anti-pattern both describe
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accumulated prompt injection risks. Current security.md covers human input
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validation only; LLM output trust boundary is missing.
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# Bad
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Append to security.md: Add LLM security principle
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```
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### 阶段 3：用户审核与执行
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#### 摘要表
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```
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# Rules Distillation Report
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## Summary
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Skills scanned: {N} | Rules: {M} files | Candidates: {K}
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| # | Principle | Verdict | Target | Confidence |
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|---|-----------|---------|--------|------------|
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| 1 | ... | Append | security.md §Input Validation | high |
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| 2 | ... | Revise | testing.md §TDD | medium |
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| 3 | ... | New Section | coding-style.md | high |
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| 4 | ... | Too Specific | — | — |
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## Details
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(Per-candidate details: evidence, violation_risk, draft text)
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```
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#### 用户操作
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用户通过数字进行回应以：
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* **批准**：按原样将草案应用到规则中
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* **修改**：在应用前编辑草案
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* **跳过**：不应用此候选规则
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**切勿自动修改规则。始终需要用户批准。**
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#### 保存结果
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将结果存储在技能目录中（`results.json`）：
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* **时间戳格式**：`date -u +%Y-%m-%dT%H:%M:%SZ`（UTC，秒精度）
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* **候选ID格式**：基于原则生成的烤肉串式命名（例如 `llm-output-trust-boundary`）
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```json
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{
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"distilled_at": "2026-03-18T10:30:42Z",
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"skills_scanned": 56,
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"rules_scanned": 22,
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"candidates": {
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"llm-output-trust-boundary": {
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"principle": "Treat LLM output as untrusted when stored or re-injected",
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"verdict": "Append",
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"target": "rules/common/security.md",
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"evidence": ["llm-memory-trust-boundary", "llm-social-agent-anti-pattern"],
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"status": "applied"
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},
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"iteration-bounds": {
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"principle": "Define explicit stop conditions for all iteration loops",
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"verdict": "New Section",
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"target": "rules/common/coding-style.md",
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"evidence": ["iterative-retrieval", "continuous-agent-loop", "agent-harness-construction"],
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"status": "skipped"
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}
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}
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}
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```
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## 示例
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### 端到端运行
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```
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$ /rules-distill
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Rules Distillation — Phase 1: Inventory
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────────────────────────────────────────
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Skills: 56 files scanned
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Rules: 22 files (75 headings indexed)
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Proceeding to cross-read analysis...
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[Subagent analysis: Batch 1 (agent/meta skills) ...]
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[Subagent analysis: Batch 2 (coding/pattern skills) ...]
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[Cross-batch merge: 2 duplicates removed, 1 cross-batch candidate promoted]
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# Rules Distillation Report
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## Summary
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Skills scanned: 56 | Rules: 22 files | Candidates: 4
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| # | Principle | Verdict | Target | Confidence |
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|---|-----------|---------|--------|------------|
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| 1 | LLM output: normalize, type-check, sanitize before reuse | New Section | coding-style.md | high |
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| 2 | Define explicit stop conditions for iteration loops | New Section | coding-style.md | high |
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| 3 | Compact context at phase boundaries, not mid-task | Append | performance.md §Context Window | high |
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| 4 | Separate business logic from I/O framework types | New Section | patterns.md | high |
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## Details
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### 1. LLM Output Validation
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Verdict: New Section in coding-style.md
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Evidence: parallel-subagent-batch-merge, llm-social-agent-anti-pattern, llm-memory-trust-boundary
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Violation risk: Format drift, type mismatch, or syntax errors in LLM output crash downstream processing
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Draft:
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## LLM Output Validation
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Normalize, type-check, and sanitize LLM output before reuse...
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See skill: parallel-subagent-batch-merge, llm-memory-trust-boundary
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[... details for candidates 2-4 ...]
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Approve, modify, or skip each candidate by number:
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> User: Approve 1, 3. Skip 2, 4.
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✓ Applied: coding-style.md §LLM Output Validation
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✓ Applied: performance.md §Context Window Management
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✗ Skipped: Iteration Bounds
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✗ Skipped: Boundary Type Conversion
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Results saved to results.json
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```
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## 设计原则
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* **是什么，而非如何做**：仅提取原则（规则范畴）。代码示例和命令保留在技能中。
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* **链接回源**：草案文本应包含 `See skill: [name]` 引用，以便读者能找到详细的"如何做"。
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* **确定性收集，LLM判断**：脚本保证详尽性；LLM保证上下文理解。
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* **反抽象保障**：三层过滤器（2+技能证据、可操作行为测试、违规风险）防止过于抽象的原则进入规则。
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