jeremylongshore / early-stopping-callback
Install for your project team
Run this command in your project directory to install the skill for your entire team:
mkdir -p .claude/skills/early-stopping-callback && curl -L -o skill.zip "https://fastmcp.me/Skills/Download/3515" && unzip -o skill.zip -d .claude/skills/early-stopping-callback && rm skill.zipProject Skills
This skill will be saved in .claude/skills/early-stopping-callback/ and checked into git. All team members will have access to it automatically.
Important: Please verify the skill by reviewing its instructions before using it.
Early Stopping Callback - Auto-activating skill for ML Training. Triggers on: early stopping callback, early stopping callback Part of the ML Training skill category.
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Skill Content
--- name: early-stopping-callback description: | Early Stopping Callback - Auto-activating skill for ML Training. Triggers on: early stopping callback, early stopping callback Part of the ML Training skill category. allowed-tools: Read, Write, Edit, Bash(python:*), Bash(pip:*) version: 1.0.0 license: MIT author: Jeremy Longshore <jeremy@intentsolutions.io> --- # Early Stopping Callback ## Purpose This skill provides automated assistance for early stopping callback tasks within the ML Training domain. ## When to Use This skill activates automatically when you: - Mention "early stopping callback" in your request - Ask about early stopping callback patterns or best practices - Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking. ## Capabilities - Provides step-by-step guidance for early stopping callback - Follows industry best practices and patterns - Generates production-ready code and configurations - Validates outputs against common standards ## Example Triggers - "Help me with early stopping callback" - "Set up early stopping callback" - "How do I implement early stopping callback?" ## Related Skills Part of the **ML Training** skill category. Tags: ml, training, pytorch, tensorflow, sklearn