File contents What I do
Explain model predictions
Implement interpretable models
Create feature importance analysis
Build visualization tools
Design trust-building explanations
Handle regulatory requirements
When to use me
Use me when:
Model debugging
Stakeholder communication
Regulatory compliance
Bias detection
Trust in AI decisions
Key Concepts
Explanation Methods
import shap
import lime
from sklearn.inspection import permutation_importance
# SHAP values
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
# Visualize
shap.summary_plot(shap_values, X_test, feature_names=feature_names)
# LIME explanations
from lime.lime_tabular import LimeTabularExplainer
explainer = LimeTabularExplainer(X_train, feature_names=feature_names)
explanation = explainer.explain_instance(X_test[0], model.predict_proba)
# Permutation importance
result = permutation_importance(model, X_test, y_test, n_repeats=10)
Interpretable Models
Linear models: Coefficients
Decision trees: Path visualization
Rule-based: Explicit rules
Attention: Attention weights
Types of Explanations
Global : Overall model behaviorLocal : Single predictionFeature : Feature importanceCounterfactual : What if scenarios
1 --- 2 name: explainability 3 description: AI model explainability 4 license: MIT 5 --- 6 7 ## What I do 8 9 - Explain model predictions 10 - Implement interpretable models 11 - Create feature importance analysis 12 - Build visualization tools 13 - Design trust-building explanations 14 - Handle regulatory requirements 15 16 ## When to use me 17 18 Use me when: 19 - Model debugging 20 - Stakeholder communication 21 - Regulatory compliance 22 - Bias detection 23 - Trust in AI decisions 24 25 ## Key Concepts 26 27 ### Explanation Methods 28 ```python 29 import shap 30 import lime 31 from sklearn.inspection import permutation_importance 32 33 # SHAP values 34 explainer = shap.TreeExplainer(model) 35 shap_values = explainer.shap_values(X_test) 36 37 # Visualize 38 shap.summary_plot(shap_values, X_test, feature_names=feature_names) 39 40 # LIME explanations 41 from lime.lime_tabular import LimeTabularExplainer 42 43 explainer = LimeTabularExplainer(X_train, feature_names=feature_names) 44 explanation = explainer.explain_instance(X_test[0], model.predict_proba) 45 46 # Permutation importance 47 result = permutation_importance(model, X_test, y_test, n_repeats=10) 48 ``` 49 50 ### Interpretable Models 51 - Linear models: Coefficients 52 - Decision trees: Path visualization 53 - Rule-based: Explicit rules 54 - Attention: Attention weights 55 56 ### Types of Explanations 57 - **Global**: Overall model behavior 58 - **Local**: Single prediction 59 - **Feature**: Feature importance 60 - **Counterfactual**: What if scenarios
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Frequently asked questions How do I install the Explainability skill? Run npx skillmds@latest add ffsshhttiikk/explainability in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.
What does the Explainability skill do? AI model explainability It is listed under AI & ML on SkillMD.
Is Explainability safe to use? This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.
Which AI agents work with Explainability? This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.
Is Explainability free to use? Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.
Who published Explainability? ffsshhttiikk (@ffsshhttiikk) published this skill. Their other Agent Skills are listed on their SkillMD profile.
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