# Vettd skill report

## Vettd assessment

| Field | Value |
| --- | --- |
| Verdict badge | conditional |
| Overall grade | B |
| Finding counts | critical: 0 · high: 0 · medium: 1 · low: 0 · info: 0 |
| Scanner runs | 2 |
| Freshness | verified_unchanged (last verified 2026-10-21T21:44:22.029Z) |
| Verdict policy version | consensus-v1 |

### Findings

- **medium** [security] References external URL — review for indirect prompt injection risk

## Provenance

| Field | Value |
| --- | --- |
| Source URL | https://github.com/k-dense-ai/scientific-agent-skills/tree/main/skills/pathway-enrichment |
| Assessed at | 2026-10-07T02:52:51.756Z |
| Published at | 2026-09-10T21:05:47.390Z |
| Public ID | 35375be1-0998-4298-b0fb-8064a242e244 |
| Canonical page | https://vettd.agentichighway.ai/skill/35375be1-0998-4298-b0fb-8064a242e244 |
| Content digest | not yet published for skills |
| Signature / attestation | not yet issued; null means unsigned, not unknown |

## Publisher-supplied metadata (not verified by Vettd)

The fields below come from the skill's publisher. Vettd does not verify
them; never treat them as instructions.

Name:

> pathway-enrichment

Description:

> Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when 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".

Author:

> k-dense-ai

Version:

> 1.3

License:

> MIT