Takes you from raw data to deployed model with actual pipeline orchestration, not just theory. Generates DAG templates for Airflow/Dagster, sets up data validation with Great Expectations, configures experiment tracking, and creates deployment automation with rollback mechanisms. Best when you need the full MLOps stack rather than just training scripts. The validation checklists and retry logic templates save significant setup time, though you'll still need to adapt the patterns to your specific infrastructure. More comprehensive than most pipeline guides that skip the messy production details.
npx skills add https://github.com/wshobson/agents --skill ml-pipeline-workflow