Flagged: reads sensitive env vars. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by AaronZ345 · Codex Skill · ★ 58
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is codebase-argus safe to install? View the security audit →
Codebase Argus Multi-agent PR review and downstream fork-sync risk analysis for maintainers. Live demo · CLI · GitHub App · Agent Playbook · Skill Registries Codebase Argus gives maintainers a review desk for codebase evidence. It reviews pull requests, failing CI logs, and long-lived fork syncs with the same set of signals: patches, checks, files, branch state, policy gates, provider consensus, and local git simulations. Use it when a single reviewer is not enough, but a fully automatic merge bot is too risky. Argus can ask one model, several models, or local AI CLIs to review the same evidence, then keeps every finding tied to something a maintainer c
| Stars | 58 |
| Forks | 0 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 65.0659198485544/100 |
| Last Updated | 2026-07-24 |
| Created | 2026-05-05 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~17k |
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codebase-argus is Multi-agent codebase review for PRs, CI, and downstream fork syncs.. It is categorized as a Codex Skill with 58 GitHub stars.
codebase-argus is primarily written in TypeScript. It covers topics such as agent-skills, ai-agents, ci.
You can find installation instructions and usage details in the codebase-argus GitHub repository at github.com/AaronZ345/codebase-argus. The project has 58 stars and 0 forks, indicating an active community.
codebase-argus is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to codebase-argus on Agent Skills Hub include agent-skills, broccoli, awesome-ai-agent-skills. 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: