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 Jun 1, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides a highly detailed and legitimate framework for statistical and econometric analysis. It promotes best practices such as data contracts, pre-analysis plans, and reproducibility logs.
- [SAFE]: It utilizes a wide range of standard, well-known Python libraries for data science and econometrics (e.g., pandas, statsmodels, econml, pyfixest). While it mentions a bridge to R via
rpy2, this is a standard and transparent practice in the econometric research community to access specific estimators not yet natively available in Python. - [SAFE]: The skill is entirely focused on local data processing and artifact generation (tables and figures). No unauthorized network operations, data exfiltration patterns, or hardcoded credentials were found.
- [SAFE]: The instructions and code snippets are transparent and educational, with no evidence of obfuscation, prompt injection, or persistence mechanisms.
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