machine-learning-ops-ml-pipeline

npx skills add majiayu000/claude-skill-registry --skill machine-learning-ops-ml-pipeline --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Machine Learning Pipeline - Multi-Agent MLOps Orchestration Design and implement a complete ML pipeline for: $ARGUMENTS ## Thinking This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes: - **Phase-based coordination**: Each phase builds upon previous outputs, with clear handoffs between agents - **Modern tooling integration**: MLflow/W&B for experiments, Feast/Tecton for features, KServe/Seldon for serving - **Production-first mindset**: Every component designed for scale, monitoring, and reliability - **Reproducibility**: Version control for data, models, and infrastructure - **Continuous improvement**: Automated retraining, A/B testing, and drift detection The multi-agent approach ensures each aspect is handled by domain experts: - Data engineers handle ingestion and quality - Data scientists design features and experiments - ML engineers implement training pipelines - MLOps engineers handle production deployment - Observability engineers ensure monitoring ## Phase 1: Data & Requirements Analysis <Task> subagent_type: data-engineer prompt: | Analyze and design data pipeline f