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 KbWen · Claude Skill · ★ 199
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agentic-os safe to install? View the security audit →
Agentic OS A governance-first layer for AI coding agents. Portable workflows, delivery gates, engineering guardrails, and 14 professional skills for Claude Code, OpenAI Codex, Google Antigravity, Cursor, GitHub Copilot, and other coding agents. <
| Stars | 199 |
| Forks | 42 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 70.3003225094592/100 |
| Open Issues | 10 |
| Last Updated | 2026-09-29 |
| Created | 2026-04-12 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~19k |
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agentic-os is Governance framework for AI coding agents. It runs them through a five-step workflow (plan, build, review, test, ship) where no step counts as done without evidence. Drop-in rules and guardrails for C. It is categorized as a Claude Skill with 199 GitHub stars.
agentic-os is primarily written in Python. It covers topics such as agent-framework, agent-orchestration, agentic-development.
You can find installation instructions and usage details in the agentic-os GitHub repository at github.com/KbWen/agentic-os. The project has 199 stars and 42 forks, indicating an active community.
agentic-os is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to agentic-os on Agent Skills Hub include palot, agent-deck, jat. 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: