From jeremylongshore-claude-code-plugins-plus-skills
Builds model evaluation metrics operations and configurations for ML training workflows including data preparation, model training, hyperparameter tuning, and experiment tracking.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:model-evaluation-metricsThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for model evaluation metrics tasks within the ML Training domain.
This skill provides automated assistance for model evaluation metrics tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with model evaluation metrics" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin ejentum-reasoningEvaluates machine learning models using metrics like accuracy, precision, recall, F1-score. Activates for model performance analysis, validation, or testing.
Guides model explainability tool tasks for ML training, including data preparation, hyperparameter tuning, and experiment tracking.
Assesses ML pipeline stage and applies patterns for data pipelines, model training, serving, MLOps, evaluation, and debugging with validations like schema checks, drift detection, and skew guards.