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pathway-enrichment

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".

73

Quality

90%

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SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-architected skill body: executable quick-start code, a decision table, a sequenced multi-step workflow with embedded correctness checks, and clean progressive disclosure to three real reference files. Slightly verbose in the pitfalls/integration sections but genuinely actionable throughout.

DimensionReasoningScore

Conciseness

Efficient and assumes competence — no padding about what enrichment or a p-value is — but the Common Pitfalls and Integration sections and repeated library lists add some length that could be trimmed without loss.

4 / 5

Actionability

Provides copy-paste-ready, executable code for ORA and preranked GSEA, a decision table mapping situation to tool, and a runnable helper script with concrete CLI invocations covering the common cases.

5 / 5

Workflow Clarity

An explicit 8-step Core Workflow is clearly sequenced with validation built into steps (ID check, background, adjusted-p filtering), but checkpoints are advisory rather than gated 'stop and verify' feedback loops as in the anchor-5 example.

4 / 5

Progressive Disclosure

SKILL.md is a lean overview with a Reference Files section signaling three real one-level-deep files (gseapy.md, databases-and-gene-sets.md, interpretation.md) and a bundled script; all referenced paths exist and navigation is clear.

5 / 5

Total

18

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20

Passed

Description

96%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, highly specific description with explicit what/when structure and a rich set of natural trigger phrases. It is somewhat long, but the density is earned by enumerating concrete methods and tools rather than padding.

DimensionReasoningScore

Specificity

Lists many concrete actions — ORA, preranked GSEA, ssGSEA/GSVA, ID mapping, background universe, multiple-testing correction, redundancy reduction, dotplots — covering the domain comprehensively.

5 / 5

Completeness

Explicitly answers both what (the methods and tools covered) and when ('Use whenever the user has a set of genes...') plus a final 'Use this for...' trigger list with concrete phrases.

5 / 5

Trigger Term Quality

Comprehensive natural trigger phrases and synonyms are included verbatim ('pathway analysis', 'GO enrichment', 'GSEA', 'over-representation', 'functional annotation') as well as tool/database names users would say.

5 / 5

Distinctiveness Conflict Risk

It carves a clear niche and even steers toward gget/database-lookup for adjacent tasks, but the broad functional-genomics scope means minor overlap with pydeseq2/scanpy skills.

4 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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