exploratory-data-analysis
Pass
Audited by Gen Agent Trust Hub on Apr 30, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides detailed instructions and methodologies for performing exploratory data analysis (EDA) on scientific data files.
- [EXTERNAL_DOWNLOADS]: The skill references several well-known scientific libraries for Python, including pandas, numpy, BioPython, RDKit, Scanpy, and others. It suggests installing these via
pipif missing, which is a standard procedure for data analysis tasks. All listed libraries are reputable and widely used in the scientific community. - [COMMAND_EXECUTION]: The workflow involves executing standard data analysis commands and scripts. It specifically warns against common pitfalls like memory overflows and provides best practices for handling large files (lazy loading, sampling).
- [PROMPT_INJECTION]: No prompt injection or behavior override patterns were detected in the instructions.
- [DATA_EXFILTRATION]: No evidence of unauthorized data access or network exfiltration was found. The skill focuses on analyzing local data files provided by the user.
Audit Metadata