SKILL

skill-comply

Visualize whether skills, rules, and agent definitions are actually followed: automatically generate scenarios at 3 prompt strictness levels, run agents, classify behavior sequences, and report compliance rates with the complete tool-call timeline.

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可视化技能、规则和代理定义是否被实际遵循——自动生成3种提示严格级别的场景,运行代理,分类行为序列,并报告完整工具调用时间线的合规率

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Excerpt from the author’s documentation

Extracted from a fixed version of SKILL.md in its original language. This is the author’s documentation; installation, compatibility and runtime results have not been verified by this site.

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Topics in the source

  • skill-comply:自动化合规性测量
  • 支持的目标
  • 何时激活
  • 使用方法
  • 关键概念:提示独立性
  • 报告内容
  • 高级(可选)

通过以下方式测量编码代理是否实际遵循技能、规则或代理定义: 1. 从任意 .md 文件自动生成预期行为序列(规范) 2. 自动生成提示严格程度递减的场景(支持性 → 中性 → 竞争性) 3. 运行 claude -p 并通过 stream-json 捕获工具调用轨迹 4. 使用 LLM(而非正则表达式)将工具调用分类到规范步骤 5. 确定性检查时间顺序 6. 生成包含规范、提示和时间线的自包含报告 技能(skills//SKILL.md):工作流技能,如搜索优先、TDD 指南 规则(rules/common/.md):强制性规则,如 testing.md、security.md、git-workflow.md 代理定义(agents/.md):代理是否在预期时被调用(内部工作流验证尚不支持) 用户运行 /skill-comply 用户询问"这条规则是否真的被遵循?" 添加新规则/技能后,验证代理合规性 作为质量维护的一部分定期执行 测量技能/规则是否在提示未明确支持时仍被遵循。 报告是自包含的,包括: 1. 预期行为序列(自动生成的规范) 2. 场景提示(每个严格程度级别询问的内容) 3. 每个场景的合规性评分 4. 带有 LLM 分类标签的工具调用时间线 对于熟悉钩子的用户,报告还包含针对合规性较低的步骤的钩子提升建议。此为参考信息——主要价值在于合规性本身的可见性。

Source repository
affaan-m/ECC
Source file
docs/zh-CN/skills/skill-comply/SKILL.md
Fixed revision
d2ef2680594f9a17526a6e1a2e18b3d242c5d17b
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Author: affaan-m/ECC · original repository metadata. Documentation review: Not reviewed. Last source check: 2026-11-09T15:01:42.619Z.

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