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 academic-highlight-generatorgit 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/academic-highlight-generator)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/academic-highlight-generator"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/academic-highlight-generator/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/academic-highlight-generator"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/academic-highlight-generator.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 | $0.00060 | $0.01300 |
| Opus 5 | $0.00030 | $0.00650 |
| Sonnet 5 | $0.00012 | $0.00260 |
| Haiku 4.5 | $0.00006 | $0.00130 |
Grade A, and why
academic-highlight-generator 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 9d 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Academic Highlight Generator
Generate journal-ready Highlights that can be pasted directly into a submission system. This skill is for academic writing output, not for inventing missing results.
When to Use
- The user wants a
Highlightssection for a manuscript submission. - The source is an English manuscript, abstract, results summary, or extracted full text.
- The paper falls into one of these types: Original Research, Meta-analysis, Review, Case Report, Bioinformatics, Bibliometrics, or Technical Note.
- The user needs a deterministic, concise output with strict bullet-count and length limits.
When Not to Use
- The user asks you to fabricate results, novelty claims, study counts, effect sizes, or conclusions that are not in the source.
- The source text is too short to identify study type or key findings reliably.
- The document is a perspective, commentary, editorial, or otherwise unsuitable for formal submission highlights.
- The user provides a binary
.docfile. This package supports.txt,.pdf, and.docx; convert.docbefore continuing.
Required Inputs
Provide one of the following:
- Plain manuscript text, abstract, or structured study summary.
- A supported source file path for
scripts/extract_text.py:.txt,.pdf, or.docx.
Recommended metadata if available:
- Manuscript type or target journal.
- Core method, main findings, and significance sentence.
- Any wording constraints such as British/American spelling.
Output Contract
Always return:
Highlights
- <bullet 1>
- <bullet 2>
- <bullet 3>
[- <bullet 4>]
[- <bullet 5>]
Hard requirements:
- Exactly
3-5bullets. - English bullets only unless the user explicitly requests Chinese.
- Maximum
85characters per English bullet. - Objective third-person tone.
- No first person (
we,our). - No undefined abbreviations, citation markers, or figure/table references.
- Every bullet must be grounded in source material.
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.
- 9d ago First seen · 190 lines · 60 tokens per session scan A c9ba3c41ea06
academic-highlight-generator is a skill published in the GitHub repository aipoch/medical-research-skills (1,869 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 1,300 once invoked, about $0.0003 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 2026-09-03.
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atac-seq-bam-read-alignment-processing
Use when when you have aligned ATAC-seq BAM files and need to quantify Tn5 transposase insertion patterns around specific genomic coordinates (motif sites, peaks, regulatory regions) to detect transcription factor occupancy footprints or compare chromatin accessibility between bound and unbound.
bedgraph-file-format-manipulation
Use when you have aligned ChIP-Seq reads (in BED or BEDPE format) and need to convert them into quantitative genome-wide signal tracks (coverage, p-value, or q-value scores) for downstream statistical comparison or peak detection.