ml-pipeline-workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-workflow-dokhacgiakhoa-antigravity-ide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ML Pipeline Workflow Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. ## Do not use this skill when - The task is unrelated to ml pipeline workflow - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Overview This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring. ## Use this skill when - 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 ## 🧠 Knowledge Modules (Fractal Skills) ### 1. [Core Capabilities](./sub-skills/core-capabilities.md) ### 2. [Reference Documentation](./sub-skills/reference-docu
- Do not use this skill when
- Instructions
- Overview
- Use this skill when
- What This Skill Provides
- 🧠 Knowledge Modules (Fractal Skills)
- 1. [Core Capabilities](./sub-skills/core-capabilities.md)
- 2. [Reference Documentation](./sub-skills/reference-documentation.md)
- 3. [Assets and Templates](./sub-skills/assets-and-templates.md)
- 4. [Basic Pipeline Setup](./sub-skills/basic-pipeline-setup.md)
- 5. [Production Workflow](./sub-skills/production-workflow.md)
- 6. [Pipeline Design](./sub-skills/pipeline-design.md)
- 7. [Data Management](./sub-skills/data-management.md)
- 8. [Model Operations](./sub-skills/model-operations.md)
What does the ml-pipeline-workflow skill do?
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-workflow-dokhacgiakhoa-antigravity-ide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.