Ml Pipeline Workflow

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.

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GitHub stars: 42.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add wshobson/agents --skill ml-pipeline-workflow --agent claude-code

Installs into .claude/skills of the current project.

Files

ML Pipeline Workflow

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

Overview

This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.

When to Use This Skill

Building new ML pipelines from scratch
Designing workflow orchestration for ML systems
Implementing data → model → deployment automation
Setting up reproducible training workflows
Creating DAG-based ML orchestration
Integrating ML components into production systems

What This Skill Provides

Core Capabilities

Pipeline Architecture
- End-to-end workflow design
- DAG orchestration patterns (Airflow, Dagster, Kubeflow)
- Component dependencies and data flow
- Error handling and retry strategies
Data Preparation
- Data validation and quality checks
- Feature engineering pipelines
- Data versioning and lineage
- Train/validation/test splitting strategies