Medical Research Agent Skills is a library of agent instructions for medical and biomedical research, covering evidence analysis, study protocol design, data analysis, and academic writing. Researchers use it to guide compatible coding agents through common scientific workflows. The catalogue contains many of the library's skills and commands.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/aipoch/medical-research-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/aipoch/medical-research-skills/read)<a href="https://agentmods.dev/commands/aipoch/medical-research-skills/read"><img src="https://agentmods.dev/badge/commands/aipoch/medical-research-skills/read/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/aipoch/medical-research-skills/read"><img src="https://agentmods.dev/badge/commands/aipoch/medical-research-skills/read.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01408 |
| Opus 5 | $0.00000 | $0.00704 |
| Sonnet 5 | $0.00000 | $0.00282 |
| Haiku 4.5 | $0.00000 | $0.00141 |
Grade A, and why
read scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/read — Deep Paper Analysis
Accepts: local PDF path, DOI, journal URL, or a pasted abstract with basic metadata.
Environment Detection
Read mode from config.json. If "auto": file-readable → Tier B/C; bash available → Tier C; otherwise → Tier A.
Steps
1. Determine Today's Dated Folder
[Tier C / B] Read config.json to get data_dir (notes_dir = data_dir/notes). Today's folder: data_dir/notes/YYYY-MM-DD/. Create it if it does not exist.
[Tier A] No folder needed. Notes will be output as an Artifact.
2. Retrieve Paper Metadata
Use WebFetch to query Semantic Scholar.
If DOI is available:
GET https://api.semanticscholar.org/graph/v1/paper/DOI:{doi}
?fields=title,year,citationCount,journal,authors,externalIds,abstract,publicationVenue
Headers: x-api-key: {s2_api_key if set}
If no DOI, use title search:
GET https://api.semanticscholar.org/graph/v1/paper/search
?query={title keywords}&fields=title,year,citationCount,journal,authors,externalIds,abstract,publicationVenue&limit=1
For the top 5 authors, query each individually:
GET https://api.semanticscholar.org/graph/v1/author/{authorId}
?fields=name,hIndex,citationCount,affiliations
If any query fails (404, network error, timeout), mark affected fields as [Query failed — please verify manually] and continue. Do not abort the analysis.
[Tier C optimization] If Python is available, run python lookup_paper.py --doi "DOI" or python lookup_paper.py --title "keywords" instead of the WebFetch calls above.
3. Read Paper Content
- PDF path provided: Use the Read tool to read the PDF directly. Claude reads PDFs natively — no pdftotext required.
- PDF uploaded (Tier A): Read the uploaded file content directly.
- DOI / URL provided: Attempt to fetch open-access content via WebFetch. If unavailable, ask the user to provide the PDF.
- Abstract pasted: Proceed with the abstract. Mark the note as
[Abstract only — full text not available].
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 157 lines · 0 tokens per session scan A 5c484a3387d4
read is a command published in the GitHub repository aipoch/medical-research-skills (1,848 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,408 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
run-gsea
Pathway enrichment on a ranked gene list (GSEA or ORA). Use when the user asks which pathways are enriched, wants GSEA/fgsea, or has DE results to interpret biologically.
query-tcga
Query TCGA/GDC for projects, mutations, or clinical data. Use when the user asks for TCGA cohorts, mutation counts, or clinical variables from the Genomic Data Commons.
analyze-degs
Differential expression from a count matrix with DESeq2. Use when the user has bulk RNA-seq counts and wants DEGs, a volcano plot, or asks which genes differ between conditions.
fit-dose-response
Fit dose-response curves and compute IC50/AUC. Use when the user has viability data across drug concentrations, or asks about IC50, EC50, or drug sensitivity.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.