Agent skill · research science · jeremylongshore
evaluating-machine-learning-models
Build this skill allows AI assistant to evaluate machine learning models
Why this skill is useful
Adds specific commands and metrics for evaluating machine learning models that the AI wouldn't reliably generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Model Evaluation Suite Evaluate machine learning models using a comprehensive suite of metrics including accuracy, precision, recall, F1-score, and custom KPIs. 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/evaluating-machine-learning-models--ed3cd7