by in-the-loop-labs · AI Tool · ★ 64
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npx @in-the-loop-labs/pair-review <PR-number-or-URL or --local>
npm install -g @in-the-loop-labs/pair-reviewTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 64 |
| Forks | 33 |
| Language | JavaScript |
| Category | AI Tool |
| License | Apache-2.0 |
| Quality Score | 69/100 |
| Open Issues | 86 |
| Last Updated | 2026-10-04 |
| Created | 2025-08-30 |
| Platforms | node |
| Est. Tokens | ~25k |
pair-review Your AI-powered code review partner - Close the feedback loop between you and AI coding agents GitHub Repository Table of Contents What is pair-review? Why pair-review? Workflows Local Review Meta-Review AI-Guided Review Quick Start Command Line Interface Local review scope Headless analysis mode Configuration Global Settings Page Environment Variables GitHub Token AI Provider...
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: