explaining-machine-learning-models

5.2

Build this skill enables AI assistant to provide interpretability and explainability for machine learning models. it is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

explainability

5.2

Rating

Installs

Machine Learning

Quick Review

This skill provides a solid conceptual framework for ML model explainability with clear examples and workflow descriptions. However, it lacks sufficient technical detail for autonomous execution. The description covers when to use the skill and what it does, but doesn't specify exact invocation parameters, data formats, or how Claude should programmatically access the referenced scripts (data_preprocessing.py, explain_model.py, feature_importance.py). The task knowledge dimension assumes these scripts contain implementation details, but SKILL.md should better bridge how to use them. Structure is reasonable with clear sections, though some redundancy exists. Novelty is moderate - while model explainability is valuable, the skill description suggests relatively standard SHAP/LIME approaches that a capable LLM could potentially guide without a dedicated skill, though packaging these workflows does provide convenience and token savings for repeated use cases.