ml-pipeline

ml-pipeline is an open-source workflow skill for Claude Code and compatible agents, published by Jeffallan. Its author describes it as: “Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store sch…”. The project has 11k stars on GitHub and is available under the MIT license. Add it to your setup with `/plugin marketplace add Jeffallan/claude-skills`.

What ml-pipeline does

Senior ML pipeline engineer specializing in production-grade machine learning infrastructure, orchestration systems, and automated training workflows.

Installation

Add ml-pipeline to your agent with:

/plugin marketplace add Jeffallan/claude-skills

Always review a skill's source before installing it. This command comes from the skill's public repository; the linked repo is the source of truth for exact setup steps.

What's inside

The SKILL.md for ml-pipeline is organised into these sections:

• Core Workflow
• Reference Guide
• Code Templates
• MLflow Experiment Logging (minimal reproducible example)
• Kubeflow Pipeline Component (single-step template)
• Data Validation Checkpoint (Great Expectations style)
• Constraints
• Output Format
• Knowledge Reference

When to use it

Reach for ml-pipeline when you want workflow help from your agent without writing the same instructions every session. Load the skill and the agent picks it up automatically for relevant tasks.

Strengths

• Clear MIT license — safe to read and adapt
• Ships in Jeffallan/claude-skills, an established project with 11,060 GitHub stars
• Actively maintained (recent commits)

Topics

Frequently asked questions

What does ml-pipeline do?

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.

How do I install ml-pipeline?

Run /plugin marketplace add Jeffallan/claude-skills in your agent, then reload your skills. Review the source at https://github.com/Jeffallan/claude-skills before installing.

Is ml-pipeline free to use?

Yes. ml-pipeline is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.

Where does ml-pipeline come from?

ml-pipeline ships inside Jeffallan/claude-skills, a repository that contains 41 catalogued skills in total. The repository's 11,060 GitHub stars apply to that whole collection, not to this skill on its own.