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/zamushwani/biomedical-ai-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/zamushwani/biomedical-ai-skills/run-gsea)<a href="https://agentmods.dev/commands/zamushwani/biomedical-ai-skills/run-gsea"><img src="https://agentmods.dev/badge/commands/zamushwani/biomedical-ai-skills/run-gsea/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/zamushwani/biomedical-ai-skills/run-gsea"><img src="https://agentmods.dev/badge/commands/zamushwani/biomedical-ai-skills/run-gsea.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 | $0.00041 | $0.00300 |
| Opus 5 | $0.00020 | $0.00150 |
| Sonnet 5 | $0.00008 | $0.00060 |
| Haiku 4.5 | $0.00004 | $0.00030 |
Grade A, and why
run-gsea 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.
What it actually says
Run pathway enrichment on $0 against the $1 collection (default: MSigDB Hallmark).
Follow the cancer-multiomics skill. Decide the method first:
ranked list, all genes -> GSEA (fgsea). Uses the whole ranking.
a cut list of DEGs -> ORA (enrichGO/enrichKEGG). Needs a background.
The parts that are usually got wrong:
- Rank by the test statistic or shrunk LFC, not by p-value. A p-value is unsigned, so ranking by it puts strong up- and down-regulated genes at the same end.
- ORA needs an explicit universe — the genes you actually tested, not every gene in the genome. The wrong background inflates every p-value.
- Do not mix ID types. Convert once, and report how many genes failed to map rather than letting them vanish silently.
- Report the FDR and the leading-edge genes, not just pathway names.
If $0 is empty, ask for the DE result file first.
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 · 24 lines · 0 tokens per session scan A 68264d72af46
run-gsea is a command published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 13d ago), licensed MIT. It adds 41 tokens to every session and 300 once invoked, about $0.0002 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 2026-08-31.
Other commands, from other repositories
read
Accepts: local PDF path, DOI, journal URL, or a pasted abstract with basic metadata.
feed
Execute all steps without asking for user confirmation at intermediate stages. Report results at the end.
critic
Deep-review a specific cluster with CellTypePilot's Annotation Critic.
ctp-inspect
Inspect single-cell data — auto-detect species, tissue, clusters, embeddings.
faostat-country-profile
Generate a food security and agricultural profile for a country.
vehicle-comparison
Side-by-side comparison of launch vehicles — cost, payload, reusability.