skill-comply
Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines
Best use case
skill-comply is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines
Teams using skill-comply should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/skill-comply/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How skill-comply Compares
| Feature / Agent | skill-comply | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 `gemini -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 Use - 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 ~/.gemini/rules/common/testing.md # Dry run (no cost, spec + scenarios only) uv run python -m scripts.run --dry-run ~/.gemini/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.
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