From jeremylongshore-claude-code-plugins-plus-skills
Guides cross validation setup for ML training, including data preparation, model training, hyperparameter tuning, and experiment tracking.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:cross-validation-setupThis 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 cross validation setup tasks within the ML Training domain.
This skill provides automated assistance for cross validation setup tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with cross validation setup" 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 ejentum-reasoningSplits data into train and test sets for ML training. Auto-activates when users mention train/test splitting, data preparation, or ML experiment tracking.
Creates detailed ML experiment implementation plans with atomic subtasks, validation criteria, and revision support. Use before writing code for multi-step ML tasks.
Provides scikit-learn API patterns for preprocessing, pipelines, model selection, evaluation, and hyperparameter tuning. Useful when /ds:experiment builds sklearn pipelines or evaluates models.