model-evaluator

What This Skill Does

The model-evaluator skill is a robust toolkit designed for data scientists and ML engineers to streamline the validation of machine learning pipelines. It automates the generation of comprehensive performance reports, moving far beyond simplistic accuracy metrics. By integrating with the SpecWeave documentation framework, this skill automatically logs evaluation results, statistical significance tests, and visual performance charts directly into your project increments. It supports classification, regression, and ranking metrics, ensuring that every deployment decision is backed by rigorous quantitative evidence, cross-validation stability, and clear, actionable insights.

Installation

You can install this skill directly through the OpenClaw Hub interface or by running the following command in your terminal: clawhub install openclaw/skills/skills/anton-abyzov/sw-model-evaluator

Use Cases

• Model Selection: Comparing multiple algorithms (e.g., XGBoost vs. Neural Networks) based on standardized metrics to choose the best candidate for production.
• Performance Auditing: Verifying that a retrained model has not suffered from performance degradation compared to previous versions.
• Regulatory Compliance: Generating standardized, automated performance reports that include confidence intervals and p-values for stakeholder or regulatory reviews.
• Overfitting Detection: Analyzing the variance between training and validation folds to ensure generalization capabilities.

Example Prompts

• "Evaluate the current model using the test dataset and generate a full performance report with ROC curves."
• "Compare the performance metrics of the new XGBoost model against the previous baseline model, including statistical significance tests."
• "Run a 5-fold cross-validation on this regression model and save the detailed accuracy and residual analysis to the latest SpecWeave increment."

Tips & Limitations

• Data Quality: The evaluator is sensitive to input data integrity. Ensure X_test and y_test are pre-processed correctly to avoid skewing metrics.
• Computational Cost: Comprehensive statistical testing and cross-validation can be resource-intensive for very large datasets; use sampling if running in constrained environments.
• Contextual Awareness: While the tool provides statistical significance (p-values), always interpret these in the context of your specific business domain and data distribution.
• Documentation: Always verify that your SpecWeave increment ID is current, as this ensures the evaluator correctly attaches the report to the appropriate project version.