aipoch/medical-research-skills: 550+ SKILL.md Packages for Medical Research Agents

What the aipoch/medical-research-skills library actually contains

The repository is not a program. It is a catalogue of SKILL.md packages, 554 according to the README's own count, grouped into five categories: Evidence Insight, Protocol Design, Data Analysis, Academic Writing, and Others. Each skill is a text artefact that a compatible agent loads, which means the library's value is the prompt engineering and domain framing inside those files, not runtime code you execute. The README states that every skill is originally designed and developed in-house, and that each one passes through MedSkillAudit, described as a domain-specific audit framework for medical research agent skills, before going live. The audience is narrow and stated plainly: medical and biomedical researchers. If your work is a literature review, a study protocol, a differential expression analysis or an SCI manuscript, the category structure maps onto your workflow. If it is not, the library has little to offer beyond its general skills bucket.

How skills are organised across the research pipeline

The README breaks the Evidence Insight area into sub-groups rather than a flat list: search and retrieval, structured paper reading, evidence landscape, gap identification, citation and integrity, and research intelligence. Concrete entries include a PubMed Boolean query builder, a multi-database search strategy designer, preprint surveillance across bioRxiv and medRxiv, a retraction watcher, and a systematic review screener with PRISMA workflow support. Study Design and Protocol Development is listed at 68 skills, with sample size calculators and what the README calls 30+ disease- and method-specific study planners that generate full protocol drafts. The Data Analysis category mentions R and Python bioinformatics code generation, statistical modelling, data cleaning pipelines and machine learning workflows. This is a breadth-first design. The trade-off is that 554 skills means discovery is the hard part: nothing in the README indicates a searchable index or a dependency map, so you find what you need by reading category headings and skill names.

Installing medical research skills into Claude Code or another agent

The repository does not ship an installer. The README frames the library as working with Claude Code, Codex, Open Code, Hermes Agent, OpenClaw and other SKILL.md-compatible platforms, so the practical step is cloning the repository and pointing your agent at the skill files it should read. The most direct route is a git clone:

git clone https://github.com/aipoch/medical-research-skills.git
cd medical-research-skills
ls scientific-skills

After that command you should see the skill directories that make up the library. The top-level repository layout also includes awesome-med-research-skills/, skill-auditor/, scripts/ and tests/, so the skill content and the audit tooling live in separate trees. If your agent supports a project-level skills directory, place or symlink the individual skill folders your project needs rather than loading all 554 at once; the README does not document a bulk-load command, and it gives no environment variables or configuration keys to set. For a first real use, pick one narrow task, such as a PubMed query, and load only the corresponding skill so you can judge its output against your own search.

The skill-auditor and what MedSkillAudit does not prove

The repository includes a skill-auditor/ directory, and the README links to it as the place to try the auditor, describing MedSkillAudit as an audit framework for medical research agent skills. This is the project's own quality gate, applied by the same organisation that writes the skills. That is worth stating plainly: an internal audit framework is a consistency mechanism, not independent validation. The README does not publish audit results per skill, does not describe the audit criteria, and does not report inter-rater agreement or error rates. There is also a tests/ directory at the top level, but the README does not explain what those tests cover or how to run them. None of this makes the skills bad, but it means the quality claim rests on documentation you cannot yet verify from the repository alone. If a skill produces a statistical power calculation or a bioinformatics pipeline, treat the output as a draft that a domain expert reviews, not as a validated result.

Where this library is the wrong tool

Several cases argue against it. If you need an application with an interface, job queue or reproducible pipeline, this is the wrong layer: the repository contains skill definitions, scripts and tests, but the README describes no runtime service, no CLI entry point and no API. If your research is outside medical and biomedical work, the domain framing that makes these skills useful becomes overhead. If you cannot inspect prompt content before it reaches your agent, adopting a 554-skill library from a third party is a supply-chain decision, not a convenience decision, and the README offers no integrity manifest or signing scheme. Finally, if your institution requires documented provenance for methods, a skill file is not a citation; the README positions skills as assistance across the research workflow, not as a replacement for methodological justification in a manuscript or ethics submission.

Compared with writing your own SKILL.md files

The realistic alternative is not another skill library; it is your own SKILL.md files. A researcher who writes a handful of prompts for PubMed searching, differential expression with DESeq2-style workflows and manuscript drafting ends up with maybe ten files tuned to their exact data conventions, journal formats and terminology. The difference in approach is coverage versus fit. AIPOCH gives you breadth across evidence synthesis, protocol design and writing without the upfront authoring work, at the cost of adapting generic skills to your lab's conventions. Self-authored skills cost more time initially and give you full control over every instruction your agent follows. For a team that already has strong prompt discipline, the in-house route is defensible. For a single researcher who needs a PRISMA-aware screener or a study design selector today, the library saves the authoring effort. The two are not mutually exclusive: load a library skill, then fork it into your own repository once you know what to change.

Maintenance, releases and the MIT licence

The last push to the default branch was on 2026-09-13, three days before this writing, so the repository is not dormant. The release history tells a different story about cadence: Release-2026-3-20 is tagged 0.1.3, Rlease-2026-3-13 (the tag's spelling, not a typo introduced here) is 0.1.2, and a release named latest, described as Skill Packages (auto-updated), is dated 2026-03-25. Version numbers in the 0.1.x range after more than 500 skills suggests the project does not treat semantic versioning as a stability contract, and the auto-updated package release implies skill content can change without a version bump. Budget for re-reading skills you depend on after you pull. The licence is MIT, which permits commercial and academic use with attribution and carries no warranty; the repository does not state a separate licence for the skill content, so assume the top-level LICENSE covers it. That is a description of the licence text, not legal advice.

Editorial conclusion

Adopt it if you already run Claude Code, Codex, Open Code, Hermes Agent or OpenClaw and want domain-specific prompts instead of writing your own. Do not adopt it if you need a standalone application with a UI, or if your work falls outside medical and biomedical research. Before relying on any single skill, read its SKILL.md and check the skill-auditor output, because the repository's quality signal is its own MedSkillAudit framework rather than external validation.

Frequently asked questions

Official sources

1. aipoch/medical-research-skills on GitHub
2. License: MIT
3. Project website
4. README
5. Releases