Which machine learning frameworks are supported by this skill?

The Hyperparameter Tuner skill supports all major machine learning frameworks, including PyTorch, TensorFlow, and Scikit-learn, providing framework-specific implementation patterns.

Does it generate production-ready code?

Absolutely. The skill is designed to output clean, production-grade Python code and configurations that follow industry best practices.

How do I activate the Hyperparameter Tuner skill?

The skill activates automatically within Claude Code when you mention 'hyperparameter tuner' or ask questions regarding model optimization and ML training patterns.

Can this skill help with experiment tracking?

Yes, it provides guidance and generates code for tracking experiments, monitoring metrics, and logging results during the hyperparameter sweep process.

Hyperparameter Tuner

Name: Hyperparameter Tuner
Author: jeremylongshore

byjeremylongshore

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1,206

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데이터 과학 및 ML

Optimizes machine learning model performance by automating hyperparameter selection and providing expert implementation patterns for ML training.

The Hyperparameter Tuner skill is a specialized tool for data scientists and ML engineers that streamlines the process of model optimization within Claude Code. It provides automated assistance for selecting tuning strategies, generating production-ready code for frameworks like PyTorch and TensorFlow, and implementing industry-standard experiment tracking. Whether you are performing a simple grid search or complex Bayesian optimization, this skill ensures your ML training pipelines follow best practices for validation and performance.

주요 기능

01Provides framework-specific guidance for PyTorch, TensorFlow, and Scikit-learn

02Offers step-by-step implementation patterns for various optimization algorithms

03Generates production-grade code for hyperparameter optimization routines

04Automates the setup of experiment tracking and performance monitoring

051,206 GitHub stars

06Validates tuning configurations against industry-standard ML patterns

사용 사례

01Implementing automated grid or random search for model selection in Scikit-learn

02Setting up experiment tracking and validation logs for large-scale ML training sweeps

03Optimizing learning rates, batch sizes, and architecture parameters for deep learning models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills hyperparameter-tuner

For use in Claude.ai and ChatGPT

Download Skill