/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
$ npx -y skills add loulanyue/awesome-claude-notes --skill skill-comply --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/skill-comply
Context preview
The summary Claude sees to decide when to auto-load this skill.
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
SKILL.md
skill-comply.SKILL.mdname: skill-comply
description: 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
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
# 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.
Read more
name: skill-comply description: 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 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
# 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.
Community-maintained distribution of reusable AI coding agents, commands, skills, hooks, and cross-harness workflows.
Repo: loulanyue/awesome-claude-notes
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