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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".

75

Quality

92%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

The body is highly actionable with executable examples, a clear sequenced workflow with verification steps, and well-structured one-level-deep references to real bundle files. The only weakness is mild redundancy across the method table, workflow steps, and pitfalls list, which slightly hurts token efficiency.

Suggestions

Consolidate the overlap between the 'Choosing the Right Method' table, the 'Core Workflow' steps, and 'Common Pitfalls' (e.g. pitfalls 1–4 largely restate steps 2–5) — consider folding the pitfalls into the relevant workflow steps as inline cautions to cut repetition.

Trim the 'Overview' framing sentence ('Enrichment analysis answers "what biology is over-represented in my genes?"...') which states domain context Claude already knows.

The 'Integration with Other Skills' list is useful but long; consider moving the detailed per-skill breakdown into a reference file and keeping only the one-line upstream/downstream mapping inline.

DimensionReasoningScore

Conciseness

The body is information-dense and assumes Claude's competence (no basic-concept padding), but the 'Choosing the Right Method' table, the 8-step 'Core Workflow', and the 'Common Pitfalls' list cover overlapping ground — pitfalls 1–4 restate steps 2–5 — so it could be tightened rather than the lean, every-token-earns-its-place level.

2 / 3

Actionability

Provides fully executable code for ORA and preranked GSEA, concrete CLI invocations for scripts/run_enrichment.py with flags, and specific function names, result columns, and cutoffs (e.g. 'Adjusted P-value < 0.05', min_size=15) — copy-paste ready, matching the top anchor.

3 / 3

Workflow Clarity

An explicit 8-step Core Workflow is clearly sequenced with a verification checkpoint at Step 6 ('Use Adjusted P-value... also check the overlap/gene count so a "hit" isn't 1 gene out of a 2000-gene set'), and Step 5/9 enforce reproducibility metadata, matching the 'clear sequence with explicit validation steps / checklists for complex processes' anchor.

3 / 3

Progressive Disclosure

SKILL.md is an overview that points to three real one-level-deep reference files (references/gseapy.md, references/databases-and-gene-sets.md, references/interpretation.md) and scripts/run_enrichment.py — all verified to exist — clearly signaled via a dedicated 'Reference Files' section, matching the 'clear overview with well-signaled one-level-deep references' anchor.

3 / 3

Total

11

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12

Passed

Description

100%

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

The description is strong: it states concrete actions, gives explicit 'what' and 'when' guidance with natural trigger phrases, and occupies a distinct niche. Voice is correctly third person throughout. No significant weaknesses to address.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Run pathway and gene-set enrichment analysis', 'gene-ID mapping', 'choosing the right background universe', 'multiple-testing correction', 'redundancy reduction', 'dotplots/enrichment maps', 'publication-ready tables') in third-person voice, matching the 'lists multiple specific concrete actions' anchor.

3 / 3

Completeness

Clearly answers what ('Run pathway and gene-set enrichment analysis... then interpret the results') and when ('Use whenever the user has a set of genes... and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched'), with an explicit 'Use whenever...' trigger clause — the top anchor for both what AND when.

3 / 3

Trigger Term Quality

The closing 'Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in"' are exactly the natural phrases a user would say, matching the 'good coverage of natural terms' anchor and not the level below which only has some relevant keywords.

3 / 3

Distinctiveness Conflict Risk

A clear niche (gene-set/pathway enrichment) with method-specific and database-specific triggers makes it unlikely to fire for unrelated skills; it is well above the 'somewhat specific but could overlap' level.

3 / 3

Total

12

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12

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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