智能体工作流

ml-pipeline

Name: ml-pipeline
Author: jeffallan

jeffallan/claude-skills

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.

3948 次安装
1.2万星标
收录于 2026.01.21

安装命令

$ npx skills add https://github.com/jeffallan/claude-skills --skill ml-pipeline

SKILL.md

ML Pipeline Expert

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

Core Workflow

Design pipeline architecture — Map data flow, identify stages, define interfaces between components
Validate data schema — Run schema checks and distribution validation before any training begins; halt and report on failures
Implement feature engineering — Build transformation pipelines, feature stores, and validation checks
Orchestrate training — Configure distributed training, hyperparameter tuning, and resource allocation
Track experiments — Log metrics, parameters, and artifacts; enable comparison and reproducibility
Validate and deploy — Run model evaluation gates; implement A/B testing or shadow deployment before promotion

Reference Guide

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SKILL.md

ML Pipeline Expert

Core Workflow

Reference Guide

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