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 Qualimetry/claude-code-qualimetry-ai-app --skill review-checkgit 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/skills/qualimetry/claude-code-qualimetry-ai-app/review-check)<a href="https://agentmods.dev/skills/qualimetry/claude-code-qualimetry-ai-app/review-check"><img src="https://agentmods.dev/badge/skills/qualimetry/claude-code-qualimetry-ai-app/review-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/skills/qualimetry/claude-code-qualimetry-ai-app/review-check"><img src="https://agentmods.dev/badge/skills/qualimetry/claude-code-qualimetry-ai-app/review-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 | $0.00085 | $0.01777 |
| Opus 5 | $0.00043 | $0.00889 |
| Sonnet 5 | $0.00017 | $0.00355 |
| Haiku 4.5 | $0.00009 | $0.00178 |
Grade A, and why
review-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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Issues Check
When working with a source file that may have been previously reviewed by Qualimetry, follow this workflow to retrieve and present any review issues found.
Step 1: Determine the Target File
Identify the file to check:
- If invoked with an argument (e.g.
review-check src/MyService.cs), use that file path. - Otherwise, use the file currently being discussed or edited in the conversation.
Step 2: Gather Repository Information
Determine the repositoryName and branchName:
repositoryName — the repository name in owner/repo-name format (e.g., organisation/my-project). The server is case-insensitive and handles .git suffixes automatically.
analysisName (optional, Qualimetry Enterprise only) — if the repository is a mono-repo with multiple analysis projects, provide the analysis project name to disambiguate. Case-insensitive. Leave empty for single-project repositories. If omitted and multiple projects are found, the server returns an error listing the available analysis names.
branchName — run this shell command:
git branch --show-current
This returns the branch name (e.g., main, develop, feature/login-page).
pullRequest (optional) — to limit the results to the issues raised on a pull request's new code, supply the PR number. Resolve it for the current branch using standard git only — no GitHub/Azure/Bitbucket CLI required. Every major host advertises the PR as a ref whose head equals the source-branch tip, so match the branch's remote head SHA against those refs:
BRANCH=$(git branch --show-current)
SHA=$(git ls-remote origin "refs/heads/$BRANCH" | cut -f1)
git ls-remote origin "refs/pull/*/head" "refs/merge-requests/*/head" "refs/pull-requests/*/from" \
| awk -v s="$SHA" '$1==s{print $2; exit}' | grep -oE '[0-9]+' | head -1
This covers GitHub (refs/pull/<n>/head), GitLab (refs/merge-requests/<n>/head) and Bitbucket Server (refs/pull-requests/<n>/from). If it prints nothing — the branch is not pushed, or the host does not expose PR refs over git (e.g. Bitbucket Cloud, Azure DevOps) — omit pullRequest and the tools return all review issues for the file.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago Changed · +1 lines 4af4d404a03a
- 10d ago First seen · 139 lines · 85 tokens per session scan A 5b9d4bdece44
review-check is a skill published in the GitHub repository Qualimetry/claude-code-qualimetry-ai-app (1 stars, last pushed 6d ago), licensed Apache-2.0. It adds 85 tokens to every session and 1,777 once invoked, about $0.0004 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.
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