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/propose)<a href="https://agentmods.dev/commands/aipoch/medical-research-skills/propose"><img src="https://agentmods.dev/badge/commands/aipoch/medical-research-skills/propose/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/propose"><img src="https://agentmods.dev/badge/commands/aipoch/medical-research-skills/propose.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 | $0.01875 |
| Opus 5 | $0.00000 | $0.00937 |
| Sonnet 5 | $0.00000 | $0.00375 |
| Haiku 4.5 | $0.00000 | $0.00187 |
Grade A, and why
propose 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/propose — Research Foundation Document
Synthesizes accumulated literature notes into a structured Research Foundation Document (RFD).
The RFD serves as the standardized upstream input for any downstream protocol design skill (e.g., clinical-cohort-protocol-designer, translational-study-blueprint, statistical-analysis-plan-writer). It can also be used independently as a research proposal outline.
⚠️ Literature Integrity (LITERATURE_HARD_RULES.md applies throughout) Every citation in the RFD must correspond to a paper in
reading_list.mdmarked as read[x]. Never fabricate PMIDs, DOIs, author names, citation counts, or study findings. If a claim requires literature support that the user has not yet read, insert a gap marker:[GAP: supporting literature needed — suggested search: <keywords>]
Steps
1. Collect Source Material
[Tier C / B] Read config.json to get data_dir. Then read:
memory/MEMORY.md— research direction, framework, Reading Progressdata_dir/notes/reading_list.md— complete reading record- All read paper notes (
.mdfiles marked[x]): extract Section III (Transferable Elements), Section IV (How to Use), and Discussion Log entries - Most recent recap snapshot in
data_dir/notes/recaps/(if available)
[Tier A] Ask the user to paste their latest Session Card. Optionally ask them to paste key note content from important papers.
2. Show Summary and Ask Clarifying Questions
Display a materials overview:
You currently have:
- N papers analyzed: [list with one-line core contribution each]
- Theoretical framework: [extracted from MEMORY.md — or "not yet defined" if absent]
- Key discussion insights: [top 3 most actionable entries from all Discussion Logs]
I will now guide you through building your Research Foundation Document. We will go section by section. After each section, you can revise before we proceed. Type "skip" to skip a section, or "back" to revise a previous one.
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 · 198 lines · 0 tokens per session scan A 164f83f1eb2f
propose 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 1,875 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.
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.
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.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.