skill-comply: Automated Compliance Measurement Measures whether coding agents actually follow skills, rules, or agent definitions by: Auto-generating expected behavioral sequences (specs) from any .md file Auto-generating scenarios with decreasing prompt strictness (supportive → neutral → competing) Running claude -p and capturing tool call traces via stream-json Classifying tool calls against spec steps using LLM (not regex) Checking temporal ordering deterministically 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 User asks "is this rule actually being followed?" After adding new rules/skills, to verify agent compliance Periodically as part of quality maintenance Usage

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: Expected behavioral sequence (auto-generated spec) Scenario prompts (what was asked at each strictness level) Compliance scores per scenario 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.