ml-pipeline

设计和实现生产级 ML 管道基础设施，配置 MLflow 或 Weights & Biases 实验跟踪，创建 Kubeflow 或 Airflow DAG 训练编排，构建 Feast 特征存储模式

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.

请帮我安装这个 Agent Skill：https://github.com/Jeffallan/claude-skills/tree/main/skills/ml-pipeline

1. 先读取该目录下的 SKILL.md 和其他文件，检查是否有可疑内容（执行未知脚本、读取密钥、向外发送数据等），用中文告诉我它会做什么、需要哪些权限；
2. 等我确认后，再把整个目录安装到 ~/.claude/skills/ml-pipeline/（如果你不是 Claude Code，安装到你自己约定的 skills 目录）；
3. 安装后告诉我怎么触发它。

git clone --depth 1 --filter=blob:none --sparse https://github.com/Jeffallan/claude-skills.git /tmp/ml-pipeline-src
cd /tmp/ml-pipeline-src && git sparse-checkout set "skills/ml-pipeline"
mkdir -p ~/.claude/skills && cp -r "skills/ml-pipeline" ~/.claude/skills/ml-pipeline

Skill 可以让智能体执行脚本。安装第三方 Skill 前务必看一遍内容，详见「安全」一课。