--- name: "train-test-splitter" description: | Test train test splitter operations. Auto-activating skill for ML Training. Triggers on: train test splitter, train test splitter Part of the ML Training skill category. Use when writing or running tests. Trigger with phrases like "train test splitter", "train splitter", "train". allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)" version: 1.0.0 license: MIT author: "Jeremy Longshore " --- # Train Test Splitter ## Overview This skill provides automated assistance for train test splitter tasks within the ML Training domain. ## When to Use This skill activates automatically when you: - Mention "train test splitter" in your request - Ask about train test splitter patterns or best practices - Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking. ## Instructions 1. Provides step-by-step guidance for train test splitter 2. Follows industry best practices and patterns 3. Generates production-ready code and configurations 4. Validates outputs against common standards ## Examples **Example: Basic Usage** Request: "Help me with train test splitter" Result: Provides step-by-step guidance and generates appropriate configurations ## Prerequisites - Relevant development environment configured - Access to necessary tools and services - Basic understanding of ml training concepts ## Output - Generated configurations and code - Best practice recommendations - Validation results ## Error Handling | 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 | ## Resources - Official documentation for related tools - Best practices guides - Community examples and tutorials ## Related Skills Part of the **ML Training** skill category. Tags: ml, training, pytorch, tensorflow, sklearn