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model-evaluation-suite skill

/backups/skills-migration-20251108-070147/plugins/ai-ml/model-evaluation-suite/skills/model-evaluation-suite

This skill evaluates machine learning models using a comprehensive metrics suite to reveal performance strengths, weaknesses, and improvement opportunities.

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

Review the files below or copy the command above to add this skill to your agents.

Files (4)
SKILL.md
2.7 KB
---
name: evaluating-machine-learning-models
description: |
  This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".
---

## Overview

This skill empowers Claude to perform thorough evaluations of machine learning models, providing detailed performance insights. It leverages the `model-evaluation-suite` plugin to generate a range of metrics, enabling informed decisions about model selection and optimization.

## How It Works

1. **Analyzing Context**: Claude analyzes the user's request to identify the model to be evaluated and any specific metrics of interest.
2. **Executing Evaluation**: Claude uses the `/eval-model` command to initiate the model evaluation process within the `model-evaluation-suite` plugin.
3. **Presenting Results**: Claude presents the generated metrics and insights to the user, highlighting key performance indicators and potential areas for improvement.

## When to Use This Skill

This skill activates when you need to:
- Assess the performance of a machine learning model.
- Compare the performance of multiple models.
- Identify areas where a model can be improved.
- Validate a model's performance before deployment.

## Examples

### Example 1: Evaluating Model Accuracy

User request: "Evaluate the accuracy of my image classification model."

The skill will:
1. Invoke the `/eval-model` command.
2. Analyze the model's performance on a held-out dataset.
3. Report the accuracy score and other relevant metrics.

### Example 2: Comparing Model Performance

User request: "Compare the F1-score of model A and model B."

The skill will:
1. Invoke the `/eval-model` command for both models.
2. Extract the F1-score from the evaluation results.
3. Present a comparison of the F1-scores for model A and model B.

## Best Practices

- **Specify Metrics**: Clearly define the specific metrics of interest for the evaluation.
- **Data Validation**: Ensure the data used for evaluation is representative of the real-world data the model will encounter.
- **Interpret Results**: Provide context and interpretation of the evaluation results to facilitate informed decision-making.

## Integration

This skill integrates seamlessly with the `model-evaluation-suite` plugin, providing a comprehensive solution for model evaluation within the Claude Code environment. It can be combined with other skills to build automated machine learning workflows.

Overview

This skill enables Claude to perform comprehensive evaluations of machine learning models and deliver actionable performance insights. It uses a model-evaluation suite to compute standard metrics and highlight strengths and weaknesses. Results help guide model selection, debugging, and validation before deployment.

How this skill works

Claude identifies the target model and any user-specified metrics from the request, then invokes the evaluation endpoint to run tests on the provided dataset. The evaluation generates a suite of metrics (accuracy, precision, recall, F1, ROC/AUC, confusion matrix, etc.) plus summary diagnostics. Claude interprets the results, calls out notable issues (class imbalance, high variance, calibration problems), and suggests next steps.

When to use it

  • When you need a performance assessment of a trained model.
  • To compare multiple models or model versions objectively.
  • Before deployment to validate that performance meets requirements.
  • When investigating unexpected production behavior or regression.
  • For automated testing in CI pipelines to gate model updates.

Best practices

  • Specify which metrics matter for your use case (e.g., precision vs recall) to focus the evaluation.
  • Use a representative, held-out test set or cross-validation to avoid optimistic estimates.
  • Include class labels, sample weights, and any relevant preprocessing so evaluation matches production behavior.
  • Check calibration, confusion matrices, and per-class metrics for imbalanced datasets.
  • Report and store evaluation artifacts (metrics, plots, data slices) for reproducibility and audits.

Example use cases

  • "Evaluate model" on an image classifier and return accuracy, F1, and confusion matrix.
  • Compare F1 and ROC/AUC for two tabular models to recommend the better candidate.
  • Run validation metrics on a new model version as part of an automated CI check.
  • Diagnose why a model shows low recall on a specific class and recommend remediation steps.

FAQ

What metrics will you compute by default?

Default metrics include accuracy, precision, recall, F1-score, confusion matrix, and ROC/AUC when applicable. Additional metrics can be requested explicitly.

Can this skill handle imbalanced datasets?

Yes. It reports per-class metrics, weighted averages, and suggests remedies such as resampling or different thresholds when imbalance impacts performance.