From jeremylongshore-claude-code-plugins-plus-skills
Creates Optuna studies for hyperparameter optimization in ML training with PyTorch, TensorFlow, and scikit-learn. Useful for data prep, model training, and experiment tracking.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin langchain-py-packThis skill is limited to using the following tools:
This skill provides automated assistance for optuna study creator tasks within the ML Training domain.
Manages hyperparameter tuning operations for ML training, providing guidance, code generation, and configs for data prep, model training, and experiment tracking. Triggers on 'hyperparameter tuner' phrases.
Sets up AutoML pipelines using Optuna or Ray Tune for hyperparameter optimization with Hyperband, ASHA, search spaces, and early stopping. For new ML projects, retraining, comparing models, or limited tuning expertise.
Optimizes ML model hyperparameters using grid, random, or Bayesian search via executed Python code with scikit-learn or Optuna. For tuning Random Forest, Gradient Boosting on datasets like Iris.
Share bugs, ideas, or general feedback.
This skill provides automated assistance for optuna study creator tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with optuna study creator" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn