Pathway Enrichment (Claude Skill)

Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results.

   
Type Claude Skill
Supplier K-Dense Inc. (community OSS)
Availability GA — part of the actively maintained K-Dense scientific-agent-skills collection
Pricing Free / OSS (MIT)
Capabilities Read/Write — Claude runs the skill’s Python locally (Bash), not as an MCP tool

How to install

  • Claude Code / Claude.ai — Skills CLI (recommended): 
    npx skills add K-Dense-AI/scientific-agent-skills

    Installs the K-Dense collection; enable the pathway-enrichment skill when prompted. Works across Claude Code, Cursor, and Codex via the Agent Skills spec (requires Node ≥ 18).

  • Claude Code / Claude Desktop — manual clone: 
    git clone https://github.com/K-Dense-AI/scientific-agent-skills
cp -r scientific-agent-skills/skills/pathway-enrichment ~/.claude/skills/

    Project-scoped alternative: copy into .claude/skills/ instead of ~/.claude/skills/. The skill declares its own Python dependencies in its SKILL.md; install them (the K-Dense skills generally use uv / pip) when prompted on first use.

What it does

Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Use whenever the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for “pathway analysis”, “enrichment analysis”, “GO enrichment”, “KEGG/Reactome pathways”, “GSEA”, “over-representation”, “functional annotation”, or “what pathways are my genes in”.

Primary use cases: Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results.

Notes

Distributed as a SKILL.md (plus code examples) in the K-Dense collection — Claude executes it locally via Bash/Python rather than as an MCP server. Upstream license: MIT. The skill name to enable after install is pathway-enrichment.

Sources

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