hp-tune

What problem does it solve?

This Skill automates the complex and time-consuming process of finding optimal hyperparameters for machine learning models, moving beyond traditional search methods to leverage LLM reasoning.

Core Features & Use Cases

• LLM-Driven Tuning: Claude reasons directly about past results to propose new configurations, avoiding rigid algorithms like Optuna or grid search.
• Iterative Improvement: Analyzes experiment results to intelligently explore and exploit the hyperparameter search space.
• Branch-Aware Analysis: Adapts tuning strategies based on different code branches, recognizing that hyperparameter sensitivities can vary.
• Use Case: When optimizing a deep learning model's performance, use this Skill to systematically explore learning rates, batch sizes, and other parameters based on previous training runs, leading to better accuracy or lower loss.

Quick Start

Use the hp-tune skill to propose the next set of hyperparameter configurations based on previous experiment results.