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 LHL3341 · Codex Skill · ★ 92
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-claws safe to install? View the security audit →
Awesome Claws 中文 | English A curated list of OpenClaw-related products, skills, communities, and ecosystem resources — organized by use case. OpenClaw sits at the intersection of personal AI assistants, research workflows, coding products, automation, skills ecosystems, and agent communities. This list focuses on products people can actually use, not low-level implementation details. Featured The best representative from each category. Start here for a quick overview of the ecosystem. openclaw/openclaw — The flagship personal AI assistant. CherryHQ/cherry-studio — AI productivity studio with smart chat, autonomous agents, and 300+ assistant presets. Unified access to frontier LLMs. HKUDS/nanobot . Avai
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awesome-claws is 🦞 A curated list of OpenClaw-related products, skills, communities, and ecosystem resources. OpenClaw 入门教程及项目集锦：按使用场景整理的产品、技能、社区与生态资源。. It is categorized as a Codex Skill with 92 GitHub stars.
awesome-claws is primarily written in HTML. It covers topics such as ai-agents, ai-assistant, ai-coding.
You can find installation instructions and usage details in the awesome-claws GitHub repository at github.com/LHL3341/awesome-claws. The project has 92 stars and 29 forks, indicating an active community.
awesome-claws is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-claws on Agent Skills Hub include awesome-ai-coding-techniques, kindly-web-search-mcp-server, agentor. 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: