Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add JunMystery/Agent-Guidance-Python --skill skill-complygit clone --depth 1 https://github.com/JunMystery/Agent-Guidance-PythonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/junmystery/agent-guidance-python/skill-comply)<a href="https://agentmods.dev/skills/junmystery/agent-guidance-python/skill-comply"><img src="https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/skill-comply/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/junmystery/agent-guidance-python/skill-comply"><img src="https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/skill-comply.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.00506 |
| Opus 5 | $0.00023 | $0.00253 |
| Sonnet 5 | $0.00009 | $0.00101 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
skill-comply scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to skill-comply — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 59 lines · 47 tokens per session scan A 0861edfb6f07
skill-comply is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 506 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to skill-comply, differing in 0 lines, and is treated as a copy.
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