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 Neurons-AI · Codex Skill · ★ 162
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is crawbot safe to install? View the security audit →
CrawBot Personal Agentic AI Assistant for non-tech users powered by OpenClaw Features • Why CrawBot • Getting Started • Architecture • Community English Overview CrawBot bridges the gap between powerful AI agents and everyday users. Built on top of OpenClaw, it transforms command-line AI orchestration into an accessible, beautiful desktop experience—
| Stars | 162 |
| Forks | 53 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 64.3906081067363/100 |
| Open Issues | 5 |
| Last Updated | 2026-05-06 |
| Created | 2026-02-22 |
| Platforms | node |
| Est. Tokens | ~565k |
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crawbot is Personal Agentic AI Assistant for non-tech user powered by OpenClaw. It is categorized as a Codex Skill with 162 GitHub stars.
crawbot is primarily written in TypeScript. It covers topics such as agent-skills, agentic-ai, ai.
You can find installation instructions and usage details in the crawbot GitHub repository at github.com/Neurons-AI/crawbot. The project has 162 stars and 53 forks, indicating an active community.
crawbot is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to crawbot on Agent Skills Hub include ai-maestro, agent-skills, awesome-openclaw. 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: