Set up ML experiment tracking in Python projects using MLflow or Weights & Biases, automating package installs, tool initialization, and logging for parameters, metrics, and artifacts. Execute AI/ML tasks via context analysis, generating validated code, capturing metrics/insights, saving artifacts, and documenting results.
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npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin experiment-tracking-setupML experiment tracking with metrics logging and run comparison
Track and manage model versions
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
Skills to support Machine Learning experimentation using the Python ecosystem.