mlflow

Quick Review

Excellent MLflow skill with comprehensive coverage of experiment tracking, model registry, and deployment. The description clearly communicates MLflow's capabilities for ML lifecycle management. Task knowledge is outstanding with extensive code examples covering all major frameworks (PyTorch, TensorFlow, scikit-learn, HuggingFace, XGBoost), autologging, model versioning, and deployment patterns. Structure is very good with logical organization from quick start to advanced topics, though the main SKILL.md is quite lengthy (could be slightly more modular). Novelty is strong - while MLflow itself is accessible, orchestrating experiment tracking, model registry workflows, stage transitions, and deployment across multiple frameworks involves significant complexity that this skill consolidates effectively. The skill provides substantial value by reducing the token cost and effort required for a CLI agent to implement proper ML operations workflows. Best practices section adds practical value for production usage.