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.
git clone --depth 1 https://github.com/Qualimetry/claude-code-qualimetry-ai-appWrote 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/commands/qualimetry/claude-code-qualimetry-ai-app/compliance-check)<a href="https://agentmods.dev/commands/qualimetry/claude-code-qualimetry-ai-app/compliance-check"><img src="https://agentmods.dev/badge/commands/qualimetry/claude-code-qualimetry-ai-app/compliance-check/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/commands/qualimetry/claude-code-qualimetry-ai-app/compliance-check"><img src="https://agentmods.dev/badge/commands/qualimetry/claude-code-qualimetry-ai-app/compliance-check.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 | $0.00016 | $0.00277 |
| Opus 5 | $0.00008 | $0.00138 |
| Sonnet 5 | $0.00003 | $0.00055 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
compliance-check 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 10d 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.
What it actually says
Compliance: Check
Run the bundled review-check skill on the target file.
- If the user passed a
$ARGUMENTSvalue, treat it as the path of the file to check. - If not, infer "the current file" from the most recent file the agent has read or edited in this session, or ask the user which file to check if it cannot be inferred.
Then invoke the review-check skill with that file path. The skill calls get_all_review_issues (with per-pillar fallbacks) and get_standards_compliant_example, and presents findings grouped by pillar (Coding Standards, Design & Best Practice, General Principles, Secure Principles, Policies) and severity (High → Medium → Low).
If the Qualimetry MCP server is not configured (auth failure or unreachable), surface the verbatim message:
Qualimetry isn't configured yet. To finish setup, type the following in the chat input box and press Enter:
/qualimetry-setupYou'll be asked for your Qualimetry server URL and your access token.
Do not attempt to fix issues in this command — that is what /compliance-fix is for.
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.
- 10d ago First seen · 24 lines · 16 tokens per session scan A a2c63b1419f1
compliance-check is a command published in the GitHub repository Qualimetry/claude-code-qualimetry-ai-app (1 stars, last pushed 5d ago), licensed Apache-2.0. It adds 16 tokens to every session and 277 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
ship
Branch, commit, open PR, gather Claude + every enabled AI reviewer (Copilot, CodeRabbit, etc.), fix/justify/resolve every finding, loop until clean, then merge. Run only when implementation is finished AND the owner has said to ship (e.g. "ship it") — never self-invoke just because the work looks done. To design and…
review
Multi-agent code review with parallel validation.
code-review-teach
Educational code review that teaches while reviewing. Provides constructive feedback, explains best practices, discusses trade-offs, and helps developers improve their coding skills.
hidden-dependencies
Discover hidden dependencies, implicit contracts, and non-obvious couplings in the codebase.
dead-code-clean
Actively find and remove dead code, unused imports, duplicates, and zombie code.
dead-code-scan
Scan for dead code, unused imports, duplicates, and zombie code across the project.