Ml Pipeline Workflow

You're setting up the whole MLOps lifecycle and need something that covers data prep through deployment in one coherent workflow. This walks you through orchestrating pipelines with Airflow, Dagster, or Kubeflow, handling everything from data validation and feature engineering to training jobs, model validation, and gradual rollouts. It includes DAG templates, validation checklists, and references for each stage. The progressive disclosure approach is smart, starting with simple linear pipelines before adding A/B testing and ensemble strategies. Best for greenfield ML infrastructure or when you're standardizing scattered workflows into a proper end-to-end system with proper versioning and monitoring built in.

skills.sh installs: 204
GitHub stars: 39.4K
last updated: Jun 3, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill ml-pipeline-workflow --agent claude-code

Installs into .claude/skills of the current project.

Files

Supporting material availability

This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files:

<support-root>/assets/pipeline-dag.yaml.template
<support-root>/references/data-preparation.md
<support-root>/references/model-training.md

Ask for the original supporting material or an existing installation before using a step that depends on these paths; this catalog does not identify a verified download source for them. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable.