Individual skill
explaining-machine-learning-models
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.
plugins/ai-ml/model-explainability-tool/skills/explaining-machine-learning-models/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
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- Skill scan complete
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- Observed behavior
- Not checked yet
- Last checked
- Nov 12, 2026
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Repository context
- Repository stars (not skill ratings)
- 2,917
- Repository last updated
- Nov 12, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- ai Declared skill tag
- ml Declared skill tag
- explaining-machine Declared skill tag
- engineering Derived category
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Browse files
SKILL.mdcore · 4,176 bytesassets/README.mddoc · 362 bytesassets/example_explanation.jsonasset · 3,568 bytesassets/explanation_template.htmlasset · 3,929 bytesassets/visualization_styles.cssasset · 2,589 bytesreferences/README.mddoc · 68 bytesscripts/README.mddoc · 407 bytes
Read excerpt
# Model Explainability Tool
Interpret machine learning model predictions using SHAP, LIME, and feature importance analysis to explain model behavior.
## Overview
This skill empowers Claude to analyze and explain machine learning models. It helps users understand why a model makes certain predictions, identify the most influential features, and gain insights into the model's overall behavior.
## How It Works
1. **Analyze Context**: Claude analyzes the user's request and the available model data.
2. **Select Explanation Technique**: Claude chooses the most appropriate explanation technique (e.g., SHAP, LIME) based on the model type and the user's needs.
3. **Generate Explanations**: Claude uses the selected technique to generate explanations for model predictions.
4. **Present Results**: Claude presents the explanations in a clear and concise format, highlighting key insights and feature importances.
