From jeremylongshore-claude-code-plugins-plus-skills
Splits data into train and test sets for ML training. Auto-activates when users mention train/test splitting, data preparation, or ML experiment tracking.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:train-test-splitterThis 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 train test splitter tasks within the ML Training domain.
This skill provides automated assistance for train test splitter tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with train test splitter" 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-skills --plugin j-rigSplits datasets into training, validation, and testing sets with configurable ratios and stratification for ML model development.
Guides cross validation setup for ML training, including data preparation, model training, hyperparameter tuning, and experiment tracking.
Selects and implements train/validation/test split strategies based on data characteristics like time, groups, imbalance, and size. Guides sklearn usage for model evaluation frameworks.