What metrics can this skill calculate?

The skill provides a comprehensive suite of metrics including accuracy, precision, recall, F1-score, and other relevant statistical indicators for model performance.

Does this require specific data formats?

The skill works best when you provide or point Claude to representative held-out datasets used for model validation and testing.

How do I trigger the evaluation process?

Simply mention phrases like 'evaluate model', 'model performance', or 'testing metrics' to Claude, and it will initiate the evaluation using the integrated suite.

Is this skill suitable for deep learning models?

Yes, it is designed to evaluate a wide range of machine learning models, including deep learning, provided the performance data is accessible to the suite plugin.

Can I compare two different models at once?

Yes, you can ask Claude to compare Model A and Model B, and it will use the suite to extract metrics for both and present a side-by-side comparison.

Model Evaluation Suite

Name: Model Evaluation Suite
Author: jeremylongshore

byjeremylongshore

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884

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数据科学与机器学习

Evaluates machine learning models using a comprehensive suite of performance metrics to ensure accuracy and reliability.

This skill provides Claude with the specialized capability to perform rigorous performance analysis of machine learning models directly within your development workflow. By leveraging the model-evaluation-suite plugin, it automates the extraction and calculation of critical metrics like precision, recall, and F1-score, allowing developers and data scientists to compare model architectures, validate results against held-out datasets, and identify specific areas for optimization prior to production deployment.

主要功能

01884 GitHub stars

02Context-aware identification of performance bottlenecks

03Automated analysis of models via the /eval-model command

04Detailed reporting of validation results and improvement areas

05Side-by-side performance comparison of multiple ML models

06Automated calculation of accuracy, precision, recall, and F1-score

使用场景

01Comparing the performance of different model versions during experimentation

02Validating model performance metrics as part of a pre-deployment checklist

03Benchmarking the accuracy of classification or regression models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills model-evaluation-suite

For use in Claude.ai and ChatGPT

Download Skill

主要功能

01884 GitHub stars

02Context-aware identification of performance bottlenecks

03Automated analysis of models via the /eval-model command

04Detailed reporting of validation results and improvement areas

05Side-by-side performance comparison of multiple ML models

06Automated calculation of accuracy, precision, recall, and F1-score

使用场景

01Comparing the performance of different model versions during experimentation

02Validating model performance metrics as part of a pre-deployment checklist

03Benchmarking the accuracy of classification or regression models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills model-evaluation-suite

For use in Claude.ai and ChatGPT

Download Skill