Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill pathway-enrichmentgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-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/k-dense-ai/scientific-agent-skills/pathway-enrichment)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pathway-enrichment"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pathway-enrichment/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/k-dense-ai/scientific-agent-skills/pathway-enrichment"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pathway-enrichment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00239 | $0.03601 |
| Opus 5 | $0.00120 | $0.01801 |
| Sonnet 5 | $0.00048 | $0.00720 |
| Haiku 4.5 | $0.00024 | $0.00360 |
Grade A, and why
pathway-enrichment 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- pathway-enrichment — 86% identical, 19 lines differ
- pathway-enrichment — 86% identical, 21 lines differ
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.
Pathway Enrichment
Overview
Enrichment analysis answers "what biology is over-represented in my genes?" It is the standard last step after differential expression, a screen, or clustering. There are two core methods, and choosing correctly is the single most important decision:
- ORA (over-representation analysis) — take a thresholded gene list (e.g., padj < 0.05) and test which gene sets it overlaps more than chance, using Fisher's exact / hypergeometric tests. Tools: Enrichr, g:Profiler.
- GSEA (gene set enrichment analysis) — take the whole ranked list of genes (no threshold) and test whether each gene set is concentrated toward the top or bottom. Preranked GSEA uses a per-gene score (e.g., the DESeq2
stat). Better when effects are broad and subtle.
This skill orchestrates these analyses, the gene-set databases behind them, and the interpretation pitfalls that make results wrong or unpublishable.
When to Use This Skill
Use this skill when the user wants to:
- Find enriched GO terms / KEGG / Reactome / WikiPathways / MSigDB Hallmark sets in a gene list.
- Run GSEA / preranked GSEA on DESeq2, edgeR, limma, or Scanpy
rank_genes_groupsoutput. - Score pathway activity per sample/cell (ssGSEA, GSVA).
- Interpret, deduplicate, and visualize enrichment results, or build a publication table/figure.
- Decide between ORA and GSEA, pick gene-set libraries, choose a background, or fix gene-ID problems.
For quick one-off Enrichr lookups the gget skill (gget enrichr) is lighter weight; for raw pathway/interaction APIs (Reactome, KEGG, STRING) see the database-lookup skill. Use this skill for full, defensible enrichment workflows.
Choosing the Right Method
| Situation | Method | Tool / entry point |
|---|---|---|
| You have a discrete hit list (DE genes, screen hits, cluster markers) | ORA | gp.enrichr(...) or g:Profiler |
| You have a full ranked list (every tested gene + a score) | Preranked GSEA | gp.prerank(...) |
| You have an expression matrix + class labels | GSEA | gp.gsea(...) |
| You want a pathway score per sample/cell | ssGSEA / GSVA | gp.ssgsea(...), gp.gsva(...) |
| You need a custom background or 500+ organisms | ORA with custom domain | g:Profiler (domain_scope='custom') |
| You want TF / signaling activity (PROGENy, DoRothEA) | activity inference | see references/databases-and-gene-sets.md (decoupler) |
What ships with it
4 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 · 212 lines · 239 tokens per session scan A 9738be9a8ba8
pathway-enrichment is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed yesterday), licensed MIT. It adds 239 tokens to every session and 3,601 once invoked, about $0.0012 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.
Other skills, from other repositories
discovery-toolbox
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discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.