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 Davey2Waveyy/gitgauge --skill skillgit clone --depth 1 https://github.com/Davey2Waveyy/gitgaugeWrote 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/davey2waveyy/gitgauge/skill)<a href="https://agentmods.dev/skills/davey2waveyy/gitgauge/skill"><img src="https://agentmods.dev/badge/skills/davey2waveyy/gitgauge/skill/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/davey2waveyy/gitgauge/skill"><img src="https://agentmods.dev/badge/skills/davey2waveyy/gitgauge/skill.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 | $0.00069 | $0.01383 |
| Opus 5.5 | $0.00028 | $0.00553 |
| Sonnet 5.5 | $0.00014 | $0.00277 |
| Haiku 4.5 | $0.00007 | $0.00138 |
Grade A, and why
gitgauge 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 1mo 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitGauge
Score GitHub repos for authenticity. Mirrors the GitGauge Chrome extension algorithm exactly.
The GitGauge output line is the announcement — no separate announce needed. Run the scorer first, then engage with the repo.
When to run
| Trigger | Output mode |
|---|---|
| GitHub URL appears passively in conversation | Compact one-liner, then continue |
| User asks to review / analyze a repo | Compact score + brief breakdown, then review |
User runs /gitgauge owner/repo |
Full report: sub-scores, flags, metadata |
Do not run on:
- GitHub profile URLs, org pages, gist links, issue links, PR links, or commit links
- npm package references (
@scope/package), file paths, or bareowner/repowith no GitHub URL context - Repos already scored in the current conversation
Running the scorer
python ~/.claude/skills/gitgauge/scripts/score.py owner/repo
# macOS / Linux: use python3 instead of python
With a token (removes rate limit):
python ~/.claude/skills/gitgauge/scripts/score.py owner/repo --token ghp_yourtoken
For the full report mode (parse JSON to build structured output):
python ~/.claude/skills/gitgauge/scripts/score.py owner/repo --json
Pass full GitHub URLs too — the script strips the github.com/ prefix automatically.
Output formats
Compact (passive trigger)
GitGauge: 🟢 4.1/5.0 — Likely Authentic
Standard (review / analyze trigger)
GitGauge: 🟢 4.1/5.0 — Likely Authentic
⭐ 2,400 stars 🍴 310 forks 👁 47 watchers 🐛 18 issues
Fork ratio: 12.9% | Watcher ratio: 1.96% | Issue ratio: 0.75%
Full report (/gitgauge command)
Use --json output. Show score, label, all ratios, sub-scores, flags, and metadata (language, description, topics).
Score labels
| Score | Label | Emoji |
|---|---|---|
| 1.0–1.4 | Likely Fake | 🔴 |
| 1.5–2.4 | Suspicious | 🟠 |
| 2.5–3.4 | Mixed Signals | 🟡 |
| 3.5–4.4 | Likely Authentic | 🟢 |
| 4.5–5.0 | Highly Authentic | ✅ |
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.
- 1mo ago First seen · 136 lines · 69 tokens per session scan A e573b1f5167d
gitgauge is a skill published in the GitHub repository Davey2Waveyy/gitgauge (4 stars, last pushed 5mo ago), licensed MIT. It adds 69 tokens to every session and 1,383 once invoked, about $0.0003 per session on Opus 5.5. 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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Comprehensive PR/issue review - analyzes architecture, tests, identifies unrelated changes mixed in, drafts review comment or issue comment. Use when user asks to review a PR, check a PR, look at PR changes, or comment on an issue.
pr-threads-address
Address PR review feedback by systematically working through every unresolved PR review thread on the current branch's PR - analyze each comment, make the requested code changes (with tests where useful), commit, and optionally reply and resolve.
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deploy-landed
A deployment verification procedure that checks whether the intended code revision is actually running, its environment settings are present, and the product works. A deployment is the process of putting new software on a server or hosting platform.