MedSci Skills

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Created & maintained by Yoojin Nam, MD

Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea

MedSci Skills is a set of Agent Skills for clinical research: literature and references, study design, statistics, figures, manuscript drafting, reporting-guideline checks and journal submission, plus a lane for building and validating medical-imaging AI models. It is for physicians and biomedical or medical-engineering researchers who work in Claude Code, Codex, Cursor or GitHub Copilot. The skills draft and check, bundled scripts recompute what can be recomputed, and every output still needs review by a qualified researcher; it is not a diagnostic tool or an autonomous author.

Installation

Without a terminal (Windows or macOS): download the classroom installer, unzip it and double-click the installer inside. It also turns on update reminders and puts an Update MedSci Skills icon on your Desktop. If you have not installed Claude Code, Python or Node yet, the setup guides for Mac and Windows go step by step.

In a terminal, with Node 18+ and Python 3.9+ installed:

npx medsci-skills install

This copies every skill into ~/.claude/skills/ (read by Claude Code, Cursor and Copilot in VS Code) and ~/.agents/skills/ (read by Codex, Cursor and GitHub Copilot). Restart your agent, type /orchestrate, and describe what you want to do; it routes the request to the right skill. To be told when a new version ships, add --enable-update-notify: a one-line notice at Claude Code session start, off by default, no telemetry.

Other ways to install, each described in docs/install.md:

• Claude Code plugin: /plugin marketplace add Aperivue/medsci-skills, then enable the plugins you want; skills are then namespaced, e.g. /medsci-analysis:analyze-stats.
• GitHub CLI 2.90+: gh skill install Aperivue/medsci-skills --all --agent claude-code --scope user, or name a single skill. The two flags put the skills where Claude Code looks; the linked section gives the values for other hosts.
• git: clone the repository and copy skills/* into ~/.claude/skills/.
• A fixed version, for a paper that cites the version it used: npx medsci-skills@6.1.0 install; the plugin and gh equivalents are in docs/install.md.

Updating, what individual skills need (pandoc, R, PyTorch), where the files go and the optional routing block for plain-language requests are on the same page.

Three workflows to start with

Call a skill by name, or describe the task to /orchestrate.

Check a manuscript against its reporting guideline — /check-reporting

• You give: your manuscript and, if you know it, the guideline — it covers 49 reporting guidelines and risk-of-bias tools, from STROBE, STARD and CONSORT to PRISMA 2020 and TRIPOD+AI.
• You get: an item-by-item audit (PRESENT, PARTIAL, MISSING or N/A, with where each item was found), a list of fixes, and a JSON summary in qc/; it is a working audit, not the checklist you upload to the journal.

Analyse a dataset — /analyze-stats

• You give: a de-identified data file (CSV, Excel or TSV) and your research question.
• You get: an analysis plan to approve, then Python (or R) code that it runs, tables as CSV and Markdown, figures as PDF and 300-dpi PNG, and a manifest that /make-figures and /write-paper read.

Verify references — /verify-refs

• You give: a manuscript or bibliography (.md, .docx, .bib, .txt or .tsv).
• You get: qc/reference_audit.json, marking each reference OK, MISMATCH, UNVERIFIED or FABRICATED after lookup in PubMed, CrossRef and OpenAlex; it reports and never edits your references.

Longer chains (pre-submission audit, data to manuscript, systematic review) and step-by-step command lists for common tasks are in docs/workflows.md.

Demos

Five worked examples on public datasets, with their code, outputs and QC reports committed to the repository:

Demo	Data	What it shows
Diagnostic accuracy	Wisconsin breast cancer (sklearn)	Analysis to manuscript draft, STARD 2015 audit
Meta-analysis	BCG vaccine trials (metafor::dat.bcg)	Pooled analysis to manuscript draft, PRISMA 2020 audit
Survey epidemiology	NHANES