statistical-analysis
Pass
Audited by Gen Agent Trust Hub on Mar 10, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides a set of Python utilities for statistical assumption checking (normality, homogeneity of variance, etc.) using established libraries such as SciPy, Pandas, and Matplotlib. There are no network calls, subprocess executions, or unauthorized file system operations in the code.
- [SAFE]: The technical references for statistical tests and reporting standards are informative and legitimate for an academic tool. No evidence of prompt injection, role-play bypasses, or instruction overrides was found.
- [SAFE]: The external dependencies mentioned (pingouin, statsmodels, pymc, arviz) are widely used, reputable libraries in the data science ecosystem. The suggestion to use the vendor's own web platform for complex workflows is a standard productivity recommendation and does not constitute a security threat.
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