pymc
Pass
Audited by Gen Agent Trust Hub on Sep 23, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill serves as a legitimate technical guide for Bayesian modeling using the PyMC library.
- [SAFE]: All identified code snippets follow standard scientific computing patterns and best practices for probabilistic programming.
- [SAFE]: Dependencies such as PyMC, ArviZ, and NumPy are established, well-known libraries in the data science ecosystem.
- [SAFE]: No network exfiltration, sensitive file access, or privilege escalation patterns were identified.
- [SAFE]: External links point to the official documentation and examples for the PyMC project.
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