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.
npx skills add aipoch/medical-research-skills --skill lay-summary-for-cross-disciplinary-teamsgit 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/skills/aipoch/medical-research-skills/lay-summary-for-cross-disciplinary-teams)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/lay-summary-for-cross-disciplinary-teams"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/lay-summary-for-cross-disciplinary-teams/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/skills/aipoch/medical-research-skills/lay-summary-for-cross-disciplinary-teams"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/lay-summary-for-cross-disciplinary-teams.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00156 | $0.00925 |
| Opus 5 | $0.00078 | $0.00463 |
| Sonnet 5 | $0.00031 | $0.00185 |
| Haiku 4.5 | $0.00016 | $0.00093 |
Grade A, and why
lay-summary-for-cross-disciplinary-teams 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 13d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lay Summary for Cross-Disciplinary Teams
Converts technical research into a structured summary that clinical, wet-lab, bioinformatics, product, and management teams can rapidly read and act on.
Position in the Research Pipeline
This skill sits midstream:
- Upstream (should exist first): Clear research question, defined objectives, structured results, result narrative
- This skill: Translates that clarified content for non-specialist readers
- Downstream (natural next steps): Slide Deck for Lab Meeting, Graphical Abstract Generator, Reviewer Response Drafter
If the user's research content is still vague or unstructured, prompt them to clarify objectives and key findings first. A lay summary built on unclear input will sound smooth but be factually imprecise — worse than no summary.
Step 1 — Gather Input
Ask the user to provide any of:
- Abstract, introduction, or results section
- Key findings in their own words
- A study summary or internal report
Also ask: Who is the primary audience?
mixed(default) — all teams listedclinical— clinicians, medical staffwet-lab— bench scientists, experimentalistsbioinformatics— computational scientists, data analystsproduct— product managers, translational teamsmanagement— leadership, funders, executives
If unspecified, use mixed and include all relevant audience bullets.
Step 2 — Extract Core Structure
Before writing, internally map the input to these five elements:
| Element | What to find |
|---|---|
| Study goal | Why was this done? What problem does it address? |
| System / population | What was studied? (patients, cells, datasets, samples…) |
| Main finding | What did the data show? Be specific — avoid vague positives. |
| Evidence boundary | What can this support? What remains uncertain or untested? |
| Next action | What should each team know or do because of this? |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 110 lines · 156 tokens per session scan A 0f109e401f3f
lay-summary-for-cross-disciplinary-teams is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 156 tokens to every session and 925 once invoked, about $0.0008 per session on Opus 5. 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 2026-08-30.
Other skills, from other repositories
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
microbiome
Microbiome analysis — compositional data handling, 16S/ITS amplicon, shotgun metagenomics, diversity, differential abundance, and functional profiling.
statistical-modeling
Statistical modeling and machine learning for biomarker discovery, survival analysis, classification, regression, and model interpretation.
bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
chromatin-regulation
Chromatin regulation analysis from called peaks and count matrices — differential binding, signal summarisation, peak annotation, and scATAC-seq.
spatial-omics
Spatial transcriptomics and spatial proteomics analysis covering technology-specific workflows, spatial statistics, deconvolution, and niche analysis.