pathway-enrichment
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
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…
- Package
- 6 files, 63 KB
- SKILL.md
- 2032 words, 12+ sections
- Code
- Python (1)
- License
- MIT
- Compatibility
- Requires Python 3.12+ with gseapy 1.3.1, numpy, pandas and matplotlib. Optional gprofiler-official, mygene and lxml for online mapping/catalogs. Online queries need network access; local GMT workflows run offline.
SKILL.md covers: Pathway Enrichment · Overview · When to Use This Skill · Choosing the Right Method · Setup · gseapy pulls pandas, numpy, scipy, matplotlib. Network access is needed for · Enrichr, g:Profiler, and MSigDB downloads. For fully offline ORA, use a local · GMT file with gp.enrich() (see references/gseapy.md). · Quick Start · ORA on a hit list (gseapy + Enrichr) · Match the library organism and identifier namespace; map aliases explicitly. · Keep original spelling: capitalization is not gene-ID or orthology mapping.
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 16 KB |
references/databases-and-gene-sets.md | doc | 8.8 KB |
references/gseapy.md | doc | 9.6 KB |
references/interpretation.md | doc | 7.3 KB |
references/verified-api.md | doc | 8.1 KB |
scripts/run_enrichment.py | script | 13 KB |
External hosts referenced
github.com www.gsea-msigdb.org biit.cs.ut.ee maayanlab.cloud docs.gsea-msigdb.org arxiv.org decoupler.scverse.org gseapy.readthedocs.io pypi.org data.broadinstitute.org docs.mygene.info doi.org export.arxiv.org mygene.info www.ensembl.org
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-pathway-enrichment-scan.json
- Badge for your README:
- Request a re-scan