From jeremylongshore-claude-code-plugins-plus-skills
Guides hyperparameter tuning for ML training, providing step-by-step patterns, configurations, and best practices for data prep, model training, and experiment tracking.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:hyperparameter-tunerThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for hyperparameter tuner tasks within the ML Training domain.
This skill provides automated assistance for hyperparameter tuner tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with hyperparameter tuner" 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
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skillsGuides TensorFlow model training: data prep, hyperparameter tuning, experiment tracking. Auto-activates on 'tensorflow model trainer' mentions.
Guides ML hyperparameter tuning workflows: strategy selection (grid/random/Bayesian/halving), Optuna Bayesian optimization, search space design, budget estimation, and result analysis. Activated by /ds:experiment for tuning tasks.
Runs AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm selection (bayesian, hyperband, asha, bohb, llm, hybrid, autoresearch), WandB experiment tracking, job execution on TAO SDK platforms, and custom evaluation hooks.