Borrowing it
Nothing to install: this file belongs to fitlab-ai/agent-infra. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/fitlab-ai/agent-infra/main/.claude/commands/review-pr.mdgit clone --depth 1 https://github.com/fitlab-ai/agent-infraWrote 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/fitlab-ai/agent-infra/review-pr)<a href="https://agentmods.dev/commands/fitlab-ai/agent-infra/review-pr"><img src="https://agentmods.dev/badge/commands/fitlab-ai/agent-infra/review-pr/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/fitlab-ai/agent-infra/review-pr"><img src="https://agentmods.dev/badge/commands/fitlab-ai/agent-infra/review-pr.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 | $0.00013 | $0.00068 |
| Opus 5 | $0.00006 | $0.00034 |
| Sonnet 5 | $0.00003 | $0.00014 |
| Haiku 4.5 | $0.00001 | $0.00007 |
Grade A, and why
review-pr 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- review-pr.zh-CN — 100% identical, 0 lines differ
What it actually says
读取并执行 .agents/skills/review-pr/SKILL.md 中的 review-pr 技能。
严格按照技能中定义的所有步骤执行。
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 First seen · 9 lines · 13 tokens per session scan A 8c2c6100b2e3
review-pr is a command published in the GitHub repository fitlab-ai/agent-infra (83 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 68 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 2026-09-03.
Other commands, from other repositories
afrv
Revise the current feature worktree after code review — decide accept/revert/modify (shortcut for feature-code-revise).
request-review-plan
Request Code Review Phases 1-2: Planning scope and assembling reviewer context.
aigon-feature-code-revise
A reviewing agent has just committed fixes (or notes) on this feature branch. Your job — as the implementing agent — is to read what the reviewer did, then decide how to respond: accept, revert, or modify.
feature-code-review
Review feature - code review with fixes by a different agent.
aigon-feature-code-review
Perform a code review on another agent's implementation, making targeted fixes where needed. Use a different model than the implementer for best results.
feature-code-revise
Revise the current feature worktree after code review — decide accept/revert/modify.