data-science-model-evaluation
Pass
Audited by Gen Agent Trust Hub on Mar 1, 2026
Risk Level: SAFECOMMAND_EXECUTION
Full Analysis
- [SAFE]: No malicious patterns or security risks were detected. The skill contains documentation and code examples for standard data science libraries such as scikit-learn, MLflow, and various visualization frameworks.
- [COMMAND_EXECUTION]: Includes shell commands for installing well-known development utilities (e.g.,
pip install nbval,pip install voila) and rendering notebooks into different formats (e.g.,jupyter nbconvert,quarto render). These represent standard developer operations for the described workflows. - [DATA_EXFILTRATION]: Mentions the use of experiment tracking tools like MLflow and Weights & Biases. These tools are designed to log metrics and parameters to tracking servers, which is their intended purpose in a data science environment and is documented here as standard practice.
- [CREDENTIALS_UNSAFE]: Includes a reference to Streamlit secrets management using a placeholder (
openai_api_key = "..."). This is an educational example of best practices for secret handling rather than a hardcoded credential.
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