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. Use when checking whether agents actually follow the skills
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SKILL.md
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 -pand 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 <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
# 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.
Files
19- SKILL.md
e26cbbc30a2.4 KB - fixtures/tdd_spec.yaml
0bd9ce7e261.2 KB - prompts/classifier.md
8374ccb93b1.0 KB - prompts/scenario_generator.md
ba508c95601.9 KB - prompts/spec_generator.md
dd702f91351.6 KB - pyproject.toml
df59c2204d360 B - scripts/__init__.py
e3b0c442980 B - scripts/classifier.py
f9847e0cb52.4 KB - scripts/grader.py
ce606a03a17.6 KB - scripts/parser.py
ccb2a27ccc2.7 KB - scripts/report.py
fc188fb8c66.3 KB - scripts/run.py
9bd9af2a984.2 KB - scripts/runner.py
1901b908478.9 KB - scripts/scenario_generator.py
b900df39ea1.8 KB - scripts/spec_generator.py
3d22f61dd72.2 KB - scripts/utils.py
7a5b1509ac411 B - tests/test_grader.py
cb4d693b1517.7 KB - tests/test_parser.py
c56055fd093.1 KB - tests/test_runner.py
a4b8009cfe12.0 KB
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