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 Orchestra-Research · Claude Skill · ★ 12.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AI-Research-SKILLs safe to install? View the security audit →
AI Research Engineering Library The most comprehensive open-source library of AI research engineering skills for AI agents 85 Skills Powering AI Research in 2026 View All 21 Categories Fine-Tunin
| Stars | 12,821 |
| Forks | 917 |
| Language | TeX |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 67.837798842408/100 |
| Open Issues | 21 |
| Last Updated | 2026-06-16 |
| Created | 2025-11-03 |
| Platforms | claude-code, codex, gemini |
| Est. Tokens | ~24k |
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AI-Research-SKILLs is Comprehensive open-source library of AI research and engineering skills for any AI model. Package the skills and your claude code/codex/gemini agent will be an AI research agent with full horsepower. . It is categorized as a Claude Skill with 12.8k GitHub stars.
AI-Research-SKILLs is primarily written in TeX. It covers topics such as ai, ai-research, claude.
You can find installation instructions and usage details in the AI-Research-SKILLs GitHub repository at github.com/Orchestra-Research/AI-Research-SKILLs. The project has 12.8k stars and 917 forks, indicating an active community.
AI-Research-SKILLs is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AI-Research-SKILLs on Agent Skills Hub include ai-guide, LibreChat, chatgpt-on-wechat. 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: