evaluate-model
Measure model performance on test datasets. Use when assessing accuracy, precision, recall, and other metrics.
npx skills add majiayu000/claude-skill-registry --skill evaluate-model-homericintelligence-projectodyssey-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Evaluate Model Measure machine learning model performance using appropriate metrics for the task (classification, regression, etc.). ## When to Use - Comparing different model architectures - Assessing performance on test/validation datasets - Detecting overfitting or underfitting - Reporting model accuracy for papers and documentation ## Quick Reference ```mojo # Mojo model evaluation pattern struct ModelEvaluator: fn evaluate_classification( mut self, predictions: ExTensor, ground_truth: ExTensor ) -> Tuple[Float32, Float32, Float32]: # Returns accuracy, precision, recall ... fn evaluate_regression( mut self, predictions: ExTensor, ground_truth: ExTensor ) -> Tuple[Float32, Float32]: # Returns MSE, MAE ... ``` ## Workflow 1. **Load test data**: Prepare test/validation dataset 2. **Generate predictions**: Run model inference on test set 3. **Select metrics**: Choose appropriate metrics (accuracy, precision, recall, F1, AUC, MSE, etc.) 4. **Calculate metrics**: Compute performance metrics 5. **Analyze results**: Compare to baseline and identify strengths/weaknesses ## Output Format Evaluation report: - Task type (classification, regression, etc.) - Metrics (accuracy, precision, r
- When to Use
- Quick Reference
- Workflow
- Output Format
- References
What does the evaluate-model skill do?
Measure model performance on test datasets. Use when assessing accuracy, precision, recall, and other metrics.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill evaluate-model-homericintelligence-projectodyssey-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.