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 aipoch · Agent Tool · ★ 1.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is medical-research-skills safe to install? View the security audit →
AIPOCH Medical Research Skills Add Skills. Run Your Research. 450+ medical research skills · Evidence Insights · Protocol Design · Data Analysis · Academic Writing 🤔 What it is? AIPOCH is a curated library of 450+ Medical Research Agent Skills, built to work with OpenClaw and other AI agent platforms, including OpenCode and Claude. It supports the research workflow across four core areas: Evidence Insights, Protocol Design, Data Analysis, and Academic Writing. Equip your AI agent with Medical Research Skills, and turn it into a capable medical research assistant.
| Stars | 1,889 |
| Forks | 175 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67.9931067454706/100 |
| Open Issues | 8 |
| Last Updated | 2026-09-17 |
| Created | 2026-02-04 |
| Platforms | python |
| Est. Tokens | ~15k |
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medical-research-skills is Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing.. It is categorized as a Agent Tool with 1.9k GitHub stars.
medical-research-skills is primarily written in Python. It covers topics such as ai, bioinformatics, biology.
You can find installation instructions and usage details in the medical-research-skills GitHub repository at github.com/aipoch/medical-research-skills. The project has 1.9k stars and 175 forks, indicating an active community.
medical-research-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to medical-research-skills on Agent Skills Hub include biomcp, ChatLab, ScienceClaw. 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: