How do I install the Ml Pipeline Workflow skill?

Run npx skillmds@latest add dokhacgiakhoa/ml-pipeline-workflow in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ml Pipeline Workflow safe to use?

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Which AI agents work with Ml Pipeline Workflow?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ml Pipeline Workflow free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ml Pipeline Workflow?

Dokhacgiakhoa (@dokhacgiakhoa) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ml Pipeline Workflow by dokhacgiakhoa

Name: Ml Pipeline Workflow
Rating: 4.2 (5 reviews)
Author: Dokhacgiakhoa

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.

Dokhacgiakhoa Updated Sep 9, 2026 505 repo stars

File contents

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

2. Reference Documentation

3. Assets and Templates

4. Basic Pipeline Setup

5. Production Workflow

6. Pipeline Design

7. Data Management

8. Model Operations

9. Deployment Strategies

10. Orchestration Tools

11. Experiment Tracking

12. Deployment Platforms

13. Batch Training Pipeline

14. Real-time Feature Pipeline

15. Continuous Training

16. Common Issues

17. Debugging Steps

---name: ml-pipeline-workflowdescription: 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.--- # 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-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) ### 9. [Deployment Strategies](./sub-skills/deployment-strategies.md) ### 10. [Orchestration Tools](./sub-skills/orchestration-tools.md) ### 11. [Experiment Tracking](./sub-skills/experiment-tracking.md) ### 12. [Deployment Platforms](./sub-skills/deployment-platforms.md) ### 13. [Batch Training Pipeline](./sub-skills/batch-training-pipeline.md) ### 14. [Real-time Feature Pipeline](./sub-skills/real-time-feature-pipeline.md) ### 15. [Continuous Training](./sub-skills/continuous-training.md) ### 16. [Common Issues](./sub-skills/common-issues.md) ### 17. [Debugging Steps](./sub-skills/debugging-steps.md)

Frequently asked questions