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 open-gitagent · Codex Skill · ★ 212
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gitclaw safe to install? View the security audit →
Gitagent A universal git-native multimodal always learning AI Agent (TinyHuman) Your agent lives inside a git repo — identity, rules, memory, tools, and skills are all version-controlled files. Install • Quick Start • SDK • Architecture • Tools • Hooks • Skills • Plugins Why Gitagent? Most agent frameworks treat configuration as code scattered across your application. Gitagent flips this — your agent IS a git repository: — model, tools, runtime config — personality and identity — behavioral constraints memor
| Stars | 212 |
| Forks | 37 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.3678217107613/100 |
| Open Issues | 12 |
| Last Updated | 2026-04-22 |
| Created | 2026-03-04 |
| Platforms | node |
| Est. Tokens | ~260k |
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gitclaw is A universal git-native AI agent framework. Your agent lives inside a git repo — identity, rules, memory, tools, and skills are all version-controlled files.. It is categorized as a Codex Skill with 212 GitHub stars.
gitclaw is primarily written in TypeScript. It covers topics such as ai-agents, anthropic, assistant.
You can find installation instructions and usage details in the gitclaw GitHub repository at github.com/open-gitagent/gitclaw. The project has 212 stars and 37 forks, indicating an active community.
gitclaw is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to gitclaw on Agent Skills Hub include starpod, argo, vllora. 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: