alterlab-umap
Pass
Audited by Gen Agent Trust Hub on Apr 12, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill serves as a legitimate technical guide for the
umap-learnlibrary, providing accurate and safe implementation examples for dimensionality reduction. - [SAFE]: All dependencies and library imports (including
umap-learn,scikit-learn,matplotlib,hdbscan,tensorflow, andnumba) are well-established packages within the data science ecosystem. - [SAFE]: No malicious patterns such as prompt injection, data exfiltration, persistence mechanisms, or unauthorized privilege escalation were detected.
- [SAFE]: The code examples for custom metrics and parametric UMAP follow standard library API patterns and do not involve unsafe dynamic code execution from untrusted sources.
Audit Metadata