umap-learn
Pass
Audited by Gen Agent Trust Hub on Jul 10, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides legitimate instructions and code snippets for the
umap-learnlibrary, including visualization, clustering, and feature engineering workflows. - [SAFE]: All listed dependencies are reputable, industry-standard Python libraries for machine learning and data science (
umap-learn,scikit-learn,numpy,tensorflow,matplotlib,hdbscan). - [SAFE]: The skill performs expected file operations, such as saving visualization plots to local files (
umap_embedding.png,umap_clusters.png), with no evidence of sensitive data exfiltration or unauthorized file access. - [SAFE]: Performance optimizations used in the examples, such as Numba's
@njitfor custom distance metrics, are standard practice for this library and do not constitute an unsafe dynamic execution risk in this context.
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