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 shuolucs · Codex Skill · ★ 339
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-OpenClaw-Research safe to install? View the security audit →
Awesome-OpenClaw-Research 🦞 OpenClaw launched Nov 2025, hit 200k GitHub stars in 84 days, and surpassed 330k stars by March 2026. This repo collects papers studying or built upon the OpenClaw ecosystem — covering agent infrastructure, learning, safety, embodiment, social dynamics & domain applications. The questions are universal; OpenClaw is the lens. Table of Contents Papers —
| Stars | 339 |
| Forks | 6 |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 54.2059790126201/100 |
| Open Issues | 1 |
| Last Updated | 2026-07-05 |
| Created | 2026-03-25 |
| Est. Tokens | ~21k |
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Awesome-OpenClaw-Research is 🦞 Research-oriented collection of papers, analyses & resources on the OpenClaw ecosystem. It is categorized as a Codex Skill with 339 GitHub stars.
You can find installation instructions and usage details in the Awesome-OpenClaw-Research GitHub repository at github.com/shuolucs/Awesome-OpenClaw-Research. The project has 339 stars and 6 forks, indicating an active community.
Awesome-OpenClaw-Research is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Awesome-OpenClaw-Research on Agent Skills Hub include Canopy, claworc, 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: