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Skill Comply

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Use when 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. Triggers on \"skill-comply\", \"skill comply\", \"comply\".

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  • Added September 19, 2026
ai-agentspythonbashtestinggitsecurity

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Scanned September 19, 2026

npx -y skills add majinmagros/magros.ai-skills --skill skill-comply --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: skill-comply
description: "Use when 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. Triggers on \"skill-comply\", \"skill comply\", \"comply\"."
metadata:
  origin: ECC
tools: Read, Bash
---

# 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.

Files in this skill

  • .gitignore76 B
  • SKILL.md2.4 KB
  • fixtures/compliant_trace.jsonl1 KB
  • fixtures/noncompliant_trace.jsonl629 B
  • fixtures/tdd_spec.yaml1.2 KB
  • prompts/classifier.md1 KB
  • prompts/scenario_generator.md1.9 KB
  • prompts/spec_generator.md1.6 KB
  • pyproject.toml299 B
  • scripts/classifier.py2.4 KB
  • scripts/grader.py4.3 KB
  • scripts/parser.py2.7 KB
  • scripts/report.py6.3 KB
  • scripts/run.py4.2 KB
  • scripts/runner.py6.9 KB
  • scripts/scenario_generator.py1.8 KB
  • scripts/spec_generator.py2.2 KB
  • scripts/utils.py411 B
  • tests/test_grader.py8 KB

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