| 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 <[email protected]> |
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
- Provides step-by-step guidance for train test splitter
- Follows industry best practices and patterns
- Generates production-ready code and configurations
- 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