train-test-splitter

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 <[e-mail]>
tags	["ai","machine-learning"]
compatibility	Designed for Claude Code

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