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.
Translation reviewedRead the original Chinese text
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.
Read the original Chinese text
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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This record was collected automatically. Its documentation, suitability and runtime behavior have not been manually verified.
Author: affaan-m/ECC · original repository metadata. Documentation review: Not reviewed. Last source check: 2026-11-09T15:01:42.619Z.
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