evaluating-machine-learning-models

4.6

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 but lacks concrete implementation details. The description mentions a '/eval-model' command and 'model-evaluation-suite' plugin that aren't clearly defined or connected to the actual scripts present (data_loader.py, evaluate_model.py, metrics_calculator.py, visualization_script.py). While the structure is clean with a good logical flow, the skill suffers from ambiguity about how to actually invoke it—a CLI agent wouldn't know whether to call the plugin command or run the Python scripts directly. The task knowledge dimension scores slightly higher assuming the referenced scripts contain implementation details. Novelty is moderate as basic model evaluation (accuracy, F1-score) is straightforward for modern AI agents, though a comprehensive suite with proper visualization could add value. To improve: (1) clarify the invocation mechanism, (2) explicitly document script parameters and usage, (3) provide concrete examples with actual data paths and commands, and (4) specify what advanced metrics distinguish this from standard sklearn evaluation.