by Continuum-AI-Corp · AI Tool · ★ 175
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/plugin marketplace add Continuum-AI-Corp/orca-code-review
/plugin install orca-code-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 | 175 |
| Forks | 63 |
| Language | JavaScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 72/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-21 |
| Created | 2026-06-25 |
| Platforms | node |
| Est. Tokens | ~15k |
OrcaCode Review AI code review that catches serious issues before they merge — powered by OrcaRouter. Automatically review every pull request, post findings directly on the affected lines, and block serious issues from merging. P0/P1 → ❌ Block · Findings → 💬 Comment · Clean → ✅ Pass How it works Every push gets one review. Findings post on the affected lines, and P0/P1 blocks the merge.
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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: