ml-pipeline
Pass
Audited by Gen Agent Trust Hub on Jul 29, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill promotes secure practices by explicitly instructing users to store credentials in secrets managers rather than hardcoding them in pipeline code.
- [SAFE]: Use of standard ML libraries (mlflow, scikit-learn, great_expectations, kfp) and patterns are consistent with the skill's stated purpose of ML infrastructure design.
- [SAFE]: The code templates for MLflow and Kubeflow use standard, benign implementations for experiment tracking and pipeline orchestration.
- [SAFE]: The external documentation link points to a project-specific GitHub Pages site belonging to the author, which is consistent with the skill metadata.
Audit Metadata