No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by taovc · Codex Skill · ★ 492
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is pr-cockpit safe to install? View the security audit →
PR Cockpit Review a repo's whole PR queue with Claude or Codex — locally. Your code never leaves your machine, it runs on your own Claude/Codex subscription, and nothing gets posted to GitHub until you check the box. English · 中文 · Français <img src="docs/media/demo.gif" width="900" alt="Pull a repo's PR
| Stars | 492 |
| Forks | 2 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 65.4846499301761/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-22 |
| Created | 2026-05-29 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~17k |
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pr-cockpit is Local AI PR workbench — Claude/Codex review in isolated git worktrees, human-gated GitHub comments. It is categorized as a Codex Skill with 492 GitHub stars.
pr-cockpit is primarily written in TypeScript. It covers topics such as ai, ai-agents, claude.
You can find installation instructions and usage details in the pr-cockpit GitHub repository at github.com/taovc/pr-cockpit. The project has 492 stars and 2 forks, indicating an active community.
pr-cockpit is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to pr-cockpit on Agent Skills Hub include atlassian-mcp-server, Overture, ai-review. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: