skills/brycewang-stanford/auto-empirical-research-skills/Full-empirical-analysis-skill/Gen Agent Trust Hub
Full-empirical-analysis-skill
Pass
Audited by Gen Agent Trust Hub on Nov 4, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill defines a structured workflow for data analysis using established and well-known Python libraries such as pandas, statsmodels, pyfixest, and econml. All provided code snippets are transparent and used for legitimate statistical purposes.
- [SAFE]: No evidence of prompt injection or attempts to bypass safety filters was found. The instructions are focused on guiding the agent through an empirical analysis pipeline.
- [SAFE]: Data handling is restricted to local files (CSV, DTA, etc.) and standard database connections using placeholders. There are no network operations to non-whitelisted domains or attempts to exfiltrate sensitive information.
- [SAFE]: Dependencies listed in the installation instructions are standard packages in the econometrics and data science ecosystem. While the skill suggests using
rpy2to interface with R for specific estimators, this is a common and legitimate practice in this domain.
Audit Metadata