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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aipoch/medical-research-skillsnpx agentmods add skills/aipoch/medical-research-skills/immune-pathway-analysisWrote 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/immune-pathway-analysis)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/immune-pathway-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/immune-pathway-analysis/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/immune-pathway-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/immune-pathway-analysis.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 | $0.00050 | $0.02981 |
| Opus 5 | $0.00025 | $0.01491 |
| Sonnet 5 | $0.00010 | $0.00596 |
| Haiku 4.5 | $0.00005 | $0.00298 |
Grade A, and why
immune-pathway-analysis 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Immune Pathway Analysis
When to Use
Use this skill when the goal is to quantify immune-related pathway activity from bulk expression data and compare pathway enrichment between two sample groups.
Typical requests:
- "Run immune pathway GSVA for these samples."
- "Score immune Reactome pathways and compare case versus control."
- "Generate an immune pathway heatmap from a saved result."
- "Use a local immune gene-set table for pathway scoring."
This skill is appropriate for:
- Bulk RNA-seq or microarray-like expression matrices
- Local immune Reactome gene-set tables prepared in advance
- Two-group pathway differential analysis with
limma - Reproducible CLI execution with append-only provenance files
Execution Model
This is a hybrid skill.
- Confirm the request is in scope with this
SKILL.md. - Ask only for missing file paths or missing group labels.
- Run
scripts/main.Rwith the appropriate mode. - Use
--mode analyzeto score pathways and export tables. - Use
--mode visualizeto regenerate a heatmap from a saved result object. - Use
--mode fullto run analysis and visualization in one pass. - Read reference files only when you need deeper algorithm, troubleshooting, or CLI details.
- After execution, report the output directory, scoring method, comparison groups, and the primary output files.
Completion Format
After a successful run, summarize the outcome in 3 short parts:
- Mode and method used, plus the compared groups.
- Output directory and key files written.
- Important warnings that affect interpretation, such as no pathways meeting
fdr_thresholdand fallback to|t|ranking.
Example completion summary:
Completed immune pathway analysis in
./output/run_001usinggsvaforCaseversusControl. Key outputs:table/immune_pathway_diff.csv,table/immune_pathway_scores.csv,data/immune_pathway_result.rds, andplot/immune_pathway_heatmap.pdf. No pathways passedFDR <= 0.05, so the workflow used the documented fallback ranking by|t|for the top-pathway export and heatmap subset.
What ships with it
19 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.
- eval_report_immune-pathway-analysis_result.json 14 KB
- references/algorithm.md 3.4 KB
- references/cli-guide.md 6.2 KB
- references/troubleshooting.md 6.3 KB
- scripts/cli_options.R 7.0 KB
- scripts/functions.R 5.7 KB
- scripts/io.R 4.1 KB
- scripts/main.R 4.5 KB
- scripts/plot_helpers.R 1.2 KB
- scripts/recording.R 3.5 KB
- scripts/run_analysis.R 3.1 KB
- scripts/utils.R 5.1 KB
- scripts/visualization.R 2.5 KB
- tests/data/expression_matrix.csv 39 KB
- tests/data/group_info.csv 202 B
- tests/data/immune_genesets_minimal.csv 530 B
- tests/data/immune_genesets.csv 2133 KB
- tests/run_tests.R 2.6 KB
- tests/run_unit_tests.R 5.2 KB
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 · 212 lines · 50 tokens per session scan A e5de1cb06314
immune-pathway-analysis is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 2,981 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 2026-08-30.
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.