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/recap)<a href="https://agentmods.dev/commands/aipoch/medical-research-skills/recap"><img src="https://agentmods.dev/badge/commands/aipoch/medical-research-skills/recap/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/recap"><img src="https://agentmods.dev/badge/commands/aipoch/medical-research-skills/recap.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 | $0.00643 |
| Opus 5 | $0.00000 | $0.00321 |
| Sonnet 5 | $0.00000 | $0.00129 |
| Haiku 4.5 | $0.00000 | $0.00064 |
Grade A, and why
recap 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/recap — Reading Progress Summary
Generates a global view of the current reading state: what has been read, what is missing, and how complete the theoretical framework is.
Steps
1. Read Data
[Tier C / B] Read config.json to get data_dir (notes_dir = data_dir/notes). Read:
data_dir/notes/reading_list.md— authoritative complete reading record (use this as the basis for the read-papers overview, not MEMORY.md)memory/MEMORY.md— research design, theoretical framework, recent Reading Progress- The most recent 3 dated folders' paper notes — extract core concepts to supplement what MEMORY.md does not cover
[Tier A] Ask the user to paste their latest Session Card.
2. Generate Four-Part Summary
① Papers Read (overview)
List all papers marked [x] in reading_list.md, organized by theory layer (not by date). One sentence per paper stating its core contribution.
② Framework Completeness
Cross-reference the research design in MEMORY.md against the theory layers. For each layer:
- ✓ Supported by at least 2 papers
- ⚠ Only one paper — recommended to supplement
- ✗ No papers yet — gap
③ Downloaded but Unread
List papers in reading_list.md that have a PDF but are still marked [ ]. Sorted by priority: core literature > methods > other.
④ Next-Step Recommendations 2–3 specific suggestions based on framework gaps and the unread list:
- Which theory layer most needs more papers
- High-priority downloaded papers not yet read
- Whether running
/feedin a particular direction would help
3. Proposal Readiness Assessment
After the next-step recommendations, evaluate the following conditions:
- Papers read: ≥ 5
MEMORY.mdtheoretical framework is non-empty (has a specific description, not "exploring")- At least 2 papers have discussion log entries
If all three conditions are met, add:
✅ Your literature base is ready for research proposal development. Run
/proposeto start building your Research Foundation Document.
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 · 87 lines · 0 tokens per session scan A cc0851cca065
recap is a command published in the GitHub repository aipoch/medical-research-skills (1,855 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 643 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.