analytics-data-analysis
Pass
Audited by Gen Agent Trust Hub on Oct 11, 2026
Risk Level: SAFEEXTERNAL_DOWNLOADSINDIRECT_PROMPT_INJECTION
Full Analysis
- [SAFE]: The skill serves as a set of instructional guidelines and best practices for data science workflows, focusing on performance, readability, and reproducibility.
- [EXTERNAL_DOWNLOADS]: The instructions recommend using industry-standard libraries such as pandas, numpy, matplotlib, seaborn, jupyter, scikit-learn, scipy, plotly, and statsmodels for data manipulation and visualization tasks.
- [INDIRECT_PROMPT_INJECTION]: The skill describes procedures for ingesting and processing external datasets. It provides clear guidance on mitigating risks by implementing data validation, range checking, missing value handling, and sanity checks at key stages of the analysis pipeline. * Ingestion points: Reading external data for analysis (SKILL.md). * Boundary markers: Not explicitly defined in this instructional guide. * Capability inventory: Data manipulation and visualization using standard Python libraries (SKILL.md). * Sanitization: Includes explicit recommendations for data validation, sanity checks, and error handling (SKILL.md).
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