skills/brycewang-stanford/awesome-agent-skills-for-empirical-research/linearmodels/Gen Agent Trust Hub
linearmodels
Pass
Audited by Gen Agent Trust Hub on May 16, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill serves as a documentation repository and syntax guide for the 'linearmodels' econometric library. It contains reference files for various statistical models (PanelOLS, IV2SLS, SUR, etc.) and provides standard code snippets for data analysis.
- [EXTERNAL_DOWNLOADS]: The skill mentions the installation of the 'linearmodels' package via the standard Python package manager (pip). This is expected behavior for a library-focused skill.
- [COMMAND_EXECUTION]: The documentation provides standard Python code examples for model estimation and data manipulation using pandas and linearmodels. No dangerous shell commands, privilege escalation attempts, or suspicious system calls were detected.
- [DATA_EXPOSURE]: No hardcoded credentials, sensitive file path access, or unauthorized network operations were found. Data loading examples use local or placeholder paths (e.g., 'data/raw/grunfeld.parquet') consistent with research workflows.
- [INDIRECT_PROMPT_INJECTION]: While the skill describes workflows that ingest external data (CSV/Parquet files), this represents the primary purpose of the skill (statistical analysis). The threat surface is minimal as the instructions focus on structured data estimation rather than processing untrusted natural language instructions.
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