How do I trigger a model evaluation in Claude Code?

You can trigger it by asking Claude to 'evaluate model', 'test metrics', or 'check model performance' within the CLI environment.

Can I compare two different models with this skill?

Yes, the skill allows you to run evaluations on multiple models and generate a side-by-side comparison of their performance results to help with selection.

What metrics can this skill evaluate?

It evaluates a comprehensive range of metrics including accuracy, precision, recall, F1-score, and other standard machine learning performance indicators.

Does this skill require specific data formats?

The skill works best when you provide or point to representative datasets that the model will encounter in real-world scenarios for accurate validation.

Machine Learning Model Evaluation Suite

Name: Machine Learning Model Evaluation Suite
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Analyzes and benchmarks machine learning models using a comprehensive suite of performance metrics and validation tools.

About

The Machine Learning Model Evaluation Suite empowers Claude to perform deep diagnostic assessments of AI models, providing granular insights into accuracy, precision, recall, and F1-scores. By integrating directly into the development workflow, it allows users to compare multiple models, identify performance bottlenecks, and validate results on held-out datasets before deployment, ensuring high-quality model selection and optimization within the Claude Code environment.

Key Features

Automated performance metric generation including Accuracy, Precision, and Recall
3 GitHub stars
Detailed diagnostic reporting for model optimization and tuning
Comparative analysis tools for benchmarking multiple model architectures
Seamless integration via the /eval-model command for instant results
Validation of performance on specific test and held-out datasets

Use Cases

Validating model performance metrics before deploying to a production environment
Analyzing F1-scores to balance precision and recall for specialized datasets
Benchmarking multiple image classification models to determine the most accurate candidate

About

Key Features

Automated performance metric generation including Accuracy, Precision, and Recall
3 GitHub stars
Detailed diagnostic reporting for model optimization and tuning
Comparative analysis tools for benchmarking multiple model architectures
Seamless integration via the /eval-model command for instant results
Validation of performance on specific test and held-out datasets

Use Cases

Validating model performance metrics before deploying to a production environment
Analyzing F1-scores to balance precision and recall for specialized datasets
Benchmarking multiple image classification models to determine the most accurate candidate