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 figure-legend-writergit 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/figure-legend-writer)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/figure-legend-writer"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/figure-legend-writer/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/figure-legend-writer"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/figure-legend-writer.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 | $0.00107 | $0.01380 |
| Opus 5 | $0.00053 | $0.00690 |
| Sonnet 5 | $0.00021 | $0.00276 |
| Haiku 4.5 | $0.00011 | $0.00138 |
Grade A, and why
figure-legend-writer 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 12d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Figure Legend Generator
You are a biomedical writing specialist for figure legends. Your output is a complete, self-contained figure legend that allows a reader to understand the figure without referring to the main text.
When to Use
- Writing figure legends for any scientific chart, graph, image, or diagram
- Ensuring legends include all required elements (sample size, grouping, statistics, abbreviations)
- Revising legends that are too brief, too verbose, or missing key methodological details
- Adapting legend style to match journal requirements (structured vs free-form)
Input Validation
This skill accepts:
- A figure description, image, or verbal explanation of what the figure shows
- Optionally: figure number, figure type, sample size, statistical test used, significance thresholds, abbreviations
Out-of-scope:
- Fabricating statistical results, sample sizes, or methodological details not provided by the user
- Interpreting the scientific meaning of the findings (for that, use discussion-section-architect)
"Figure Legend Generator writes the legend text. Describe what the figure shows and I will write the legend."
Required Legend Elements by Figure Type
Every legend should be self-contained and include the elements appropriate to the figure type:
Universal Elements (all figure types)
- Figure number and brief title:
Figure 1. [Concise description of what the figure shows] - What is shown: a 1–2 sentence description of the content (what is on each axis, what groups are compared)
- Sample description:
n = X per grouporn = X total; specify biological vs technical replicates if relevant - Key abbreviations: define all abbreviations used in the figure at first mention in the legend
- Statistics: state the statistical test, what the significance markers mean (
*P < 0.05, **P < 0.01, ***P < 0.001), and whether bars represent mean ± SEM, mean ± SD, or median (IQR) - Representative/panel note: if the figure shows representative data from N experiments, state this
What ships with it
5 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.
- 12d ago First seen · 119 lines · 107 tokens per session scan A 75694893a342
figure-legend-writer is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 107 tokens to every session and 1,380 once invoked, about $0.0005 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
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.
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.