pymc-bayesian-modeling
Pass
Audited by Gen Agent Trust Hub on Oct 12, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill outlines legitimate probabilistic programming workflows using trusted scientific libraries such as PyMC, ArviZ, and NumPy.
- [SAFE]: Code examples demonstrate standard data analysis practices, including data standardization and prior/posterior predictive checks, without any dangerous system calls or unauthorized network operations.
- [SAFE]: The internal references to scripts and templates (e.g., in the scripts/ and assets/ directories) are consistent with the skill's organizational structure and intended purpose.
- [SAFE]: The external link to the author's research platform (K-Dense Web) is presented as a plain-text informational resource and does not involve any security-evasive techniques.
Audit Metadata