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 AlterLab-IEU · MCP Server · ★ 63
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AlterLab-Academic-Skills safe to install? View the security audit →
<img src="https://img.shields.io/github/stars/AlterLa
| Stars | 63 |
| Forks | 13 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 54.3143882402646/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-04 |
| Created | 2026-03-16 |
| Platforms | claude-code, cli, codex, gemini, mcp, python |
| Est. Tokens | ~24k |
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AlterLab-Academic-Skills is 239 evaluated academic Claude/agent skills across 17 research domains (bioinformatics, data science, clinical, social-science methods, Turkish academia & more). Executable eval per skill, deterministi. It is categorized as a MCP Server with 63 GitHub stars.
AlterLab-Academic-Skills is primarily written in Python. It covers topics such as academic-research, agent-skills, anthropic.
You can find installation instructions and usage details in the AlterLab-Academic-Skills GitHub repository at github.com/AlterLab-IEU/AlterLab-Academic-Skills. The project has 63 stars and 13 forks, indicating an active community.
AlterLab-Academic-Skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AlterLab-Academic-Skills on Agent Skills Hub include Auto-Research-Skills, Agent-Loop-Skills, claude-skills. 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: