Machine Learning Ops Ml Pipeline

This orchestrates a full production ML pipeline using coordinated specialist agents for each phase: data engineering, feature design, model training, deployment, and monitoring. You get phase-based workflows where a data engineer sets up ingestion and quality checks, a data scientist designs features and experiments, an ML engineer builds training with MLflow or W&B, and DevOps handles Kubernetes deployment with KServe or Seldon. It's comprehensive but heavy, clearly aimed at teams building serious MLOps infrastructure rather than quick experiments. The multi-agent handoff approach makes sense for complex pipelines where you want domain expertise at each layer, though you'll need to adapt the templates to your actual stack and requirements.

skills.sh installs: 508
GitHub stars: 43.1K
last updated: Jul 14, 2026

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Install

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

Installs into .claude/skills of the current project.

Files

Machine Learning Pipeline - Multi-Agent MLOps Orchestration

Design and implement a complete ML pipeline for: $ARGUMENTS

Use this skill when

Working on machine learning pipeline - multi-agent mlops orchestration tasks or workflows
Needing guidance, best practices, or checklists for machine learning pipeline - multi-agent mlops orchestration

Do not use this skill when

The task is unrelated to machine learning pipeline - multi-agent mlops orchestration
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.

Thinking

This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes:

SKILL.md on GitHub