How do I trigger a model evaluation in Claude Code?

You can trigger the skill by asking Claude to 'evaluate model', 'check model performance', or 'run validation results'.

Can I use this skill to compare two different AI models?

Yes, you can request a comparison of specific metrics between multiple models to determine which one performs better for your specific use case.

Which plugin is required for this skill to function?

This skill integrates seamlessly with the model-evaluation-suite plugin to execute the /eval-model command.

What metrics does the ML Model Evaluation skill support?

The skill supports a wide range of standard metrics including accuracy, precision, recall, F1-score, and other relevant performance indicators.

ML Model Evaluation Suite

Name: ML Model Evaluation Suite
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Evaluates machine learning model performance using a comprehensive suite of metrics including accuracy, precision, and F1-score.

About

This skill empowers Claude to perform rigorous assessments of machine learning models by leveraging the model-evaluation-suite plugin. It automates the generation of critical performance insights, allowing users to analyze model accuracy, recall, and precision through standardized commands like /eval-model. Whether you are validating a model before deployment, comparing multiple architectures, or identifying specific areas for optimization, this skill provides the structured data needed to make informed decisions in the machine learning lifecycle.

Key Features

Automated calculation of accuracy, precision, recall, and F1-scores
Integration with the /eval-model command for streamlined workflows
Side-by-side performance comparison of different model versions
Context-aware interpretation of model performance indicators
3 GitHub stars
Detailed validation reporting for held-out datasets

Use Cases

Benchmarking multiple model candidates to select the best performer
Validating model performance metrics prior to production deployment
Assessing the accuracy of image classification or NLP models

About

Key Features

Automated calculation of accuracy, precision, recall, and F1-scores
Integration with the /eval-model command for streamlined workflows
Side-by-side performance comparison of different model versions
Context-aware interpretation of model performance indicators
3 GitHub stars
Detailed validation reporting for held-out datasets

Use Cases

Benchmarking multiple model candidates to select the best performer
Validating model performance metrics prior to production deployment
Assessing the accuracy of image classification or NLP models