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-learn library, providing accurate and safe implementation examples for dimensionality reduction.
  • [SAFE]: All dependencies and library imports (including umap-learn, scikit-learn, matplotlib, hdbscan, tensorflow, and numba) 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
Risk Level
SAFE
Analyzed
Apr 12, 2026, 12:46 AM
Security Audit — agent-trust-hub — alterlab-umap