bayesian-workflow
Pass
Audited by Gen Agent Trust Hub on Jun 22, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: No malicious patterns, obfuscation, or data exfiltration attempts were found. The skill follows established best practices for Bayesian statistical modeling.
- [SAFE]: The skill uses industry-standard Python libraries for scientific computing, including PyMC, ArviZ, NumPy, and Xarray. These are well-maintained and trusted community packages.
- [SAFE]: Data processing is handled through structured numeric libraries. By converting user input to NumPy arrays before processing, the skill inherently sanitizes data against many forms of indirect prompt injection.
- [SAFE]: Installation instructions utilize standard package managers (mamba, conda, pip) and official repositories. File operations are limited to local storage of analysis results (NetCDF format).
Audit Metadata