pymc-extras
Pass
Audited by Gen Agent Trust Hub on Jun 22, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides documentation and code examples for Bayesian statistical modeling using well-known scientific libraries. All operations described are standard data science workflows.
- [SAFE]: No sensitive file access, network exfiltration, or credential exposure patterns were detected.
- [SAFE]: Dependencies identified (pymc, numpy, arviz, pytensor) are standard in the Python scientific ecosystem. The library 'pymc-extras' is the primary subject of the skill and belongs to the specified author 'pymc-labs'.
- [SAFE]: No obfuscation, prompt injection, or persistence mechanisms were found across the provided files.
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