ml4t-case-study-pipeline
Pass
Audited by Gen Agent Trust Hub on Sep 13, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill documents a best-practice architecture for data science pipelines, focusing on reproducibility and artifact contracts.
- [SAFE]: The provided Python examples use standard data processing libraries such as Polars and scikit-learn for local data manipulation.
- [SAFE]: The skill correctly demonstrates the use of
yaml.safe_load()for parsing configuration files, which prevents arbitrary code execution vulnerabilities commonly associated with unsafe YAML loading. - [SAFE]: File system operations are restricted to local project directories (e.g.,
case_studies/etfs/) for reading and writing Parquet and YAML files.
Audit Metadata