skill-comply

# skill-comply: Automated Compliance Measurement Measures whether coding agents actually follow skills, rules, or agent definitions by: 1. Auto-generating expected behavioral sequences (specs) from any .md file 2. Auto-generating scenarios with decreasing prompt strictness (supportive → neutral → competing) 3. Running `claude -p` and capturing tool call traces via stream-json 4. Classifying tool calls against spec steps using LLM (not regex) 5. Checking temporal ordering deterministically 6. Generating self-contained reports with spec, prompts, and timelines ## Supported Targets - **Skills** (`skills/*/SKILL.md`): Workflow skills like search-first, TDD guides - **Rules** (`rules/common/*.md`): Mandatory rules like testing.md, security.md, git-workflow.md - **Agent definitions** (`agents/*.md`): Whether an agent gets invoked when expected (internal workflow verification not yet supported) ## When to Activate - User runs `/skill-comply <path>` - User asks "is this rule actually being followed?" - After adding new rules/skills, to verify agent compliance - Periodically as part of quality maintenance ## Usage ```bash # Full run uv run python -m scripts.run ~/.claude/rules/common/testing.md # Dry run (no cost, spec + scenarios only) uv run python -m scripts.run --dry-run ~/.claude/skills/search-first/SKILL.md # Custom models uv run python -m scripts.run --gen-model haiku --model sonnet <path> ``` ## Key Concept: Prompt Independence Measures whether a skill/rule is followed even when the prompt doesn't explicitly support it. ## Report Contents Reports are self-contained and include: 1. Expected behavioral sequence (auto-generated spec) 2. Scenario prompts (what was asked at each strictness level) 3. Compliance scores per scenario 4. Tool call timelines with LLM classification labels ### Advanced (optional) For users familiar with hooks, reports also include hook promotion recommendations for steps with low compliance. This is informational — the main value is the compliance visibility itself.