pathway-enrichment

Editorial and source context

What this Skill does

Pathway Enrichment is a source-indexed Agent Skill for data, research & analysis work. Based on the SKILL.md description, it focuses on run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the…. Use the linked GitHub file to confirm scope and prerequisites before enabling it.

What the author says

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".

Compatibility and requirements

• Use an agent runtime that supports Agent Skills and the linked SKILL.md format.
• Confirm the source repository's tools, SDKs, and platform prerequisites for data analysis work.

How to install or import it

• Open the linked GitHub SKILL.md and review its scope and setup instructions.
• Install or import the skill using the agent runtime's documented workflow.
• Run a small, non-sensitive test and verify the output before broader use.

Permissions and risks

• Review every command, file path, network request, dependency, and credential scope before enabling it.
• GitHub stars indicate popularity, not safety or correctness; validate the source and test with non-sensitive data.

Example workflows

• Ask an AI agent to apply Pathway Enrichment to data analysis work described in the source record.
• Have the agent state assumptions, required tools, and expected output before using this data, research & analysis skill.
• Compare the result with the linked GitHub SKILL.md and verify it against your project requirements.