evaluating-machine-learning-models

4.6

116

133

Build this skill allows AI assistant 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. AI assistant can use this skill to assess model accuracy, p... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

model evaluation

4.6

Rating

Installs

Machine Learning

Quick Review

The skill provides a reasonable conceptual overview of ML model evaluation with clear use cases and examples. However, descriptionCoverage suffers from vague references to a `/eval-model` command and `model-evaluation-suite` plugin that aren't clearly documented or integrated with the actual Python scripts present (data_loader.py, evaluate_model.py, metrics_calculator.py, visualization_script.py). TaskKnowledge is moderate - while scripts are referenced and likely contain implementation details, SKILL.md lacks concrete parameters, input/output formats, or how to actually invoke the evaluation. Structure is decent with logical sections, though some generic boilerplate weakens it. Novelty is limited as model evaluation (accuracy, F1-score) is straightforward and well-supported by existing ML libraries; a CLI agent could accomplish similar tasks without significant token overhead. The skill would benefit from: (1) concrete invocation examples with actual file paths and parameters, (2) clearer integration between the documented commands and the Python scripts, (3) specification of supported model formats and metrics, and (4) more complex evaluation scenarios (cross-validation, statistical testing, ensemble analysis) to justify the abstraction.