Agent skill · research science · aiskillstore
shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
Why this skill is useful
Provides domain-specific knowledge on computing SHAP values and generating visualizations for model interpretability that the AI wouldn't reliably generate on its own.
What it needs
Requires pytorch, shap, tensorflow, xgboost installed locally. About 9k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
SHAP (SHapley Additive exPlanations) Overview SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for: Computing SHAP values for any model type Creating visualizations to understand feature importance Debugging and validating model behavior Analyzing fairness and bias Implementing explainable AI in production SHAP works with all model types: tree-based models (XGBoost, LightGBM, CatBoost, Random Forest), deep learning models (TensorFlow, PyTorch, Keras), linear models, and black-box models. When to Use This Skill Trigger this skill when users ask about: "Explain which features are most important in my model" "Generate SHAP plots" (waterfall, beeswarm, bar, scatter, force, heatmap, etc.) "Why did my model make this prediction?" "Calculate SHAP values for my model" "Visualize feature importance using SHAP" "Debug my model's behavior" or "validate my model" "Check my model for bias" or "analyze fairness" "Compare feature importance across models" "Implement explainable AI" or "add explanations to my model" "Understand feature interactions" "Create model interpretation dashboard" Quick Start Guide Step 1: Select the Right Explainer Decision Tree: 1. Tree-based model? (XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting) Use shap.TreeExplainer (fast, exact) 2. Deep neural network? (TensorFlow, PyTorch, Keras, CNNs, RNNs, Transformers) Use shap.DeepExplainer or shap.GradientExplainer 3. Linear model? (Linear/Logistic Regression, GLMs) Use shap.LinearExplainer (extremely fast) 4. Any other model? (SVMs, custom functions, black-box models) Use shap.KernelExplainer (model-agnostic but slower) 5. Unsure? Use shap.Explainer (automatically selects best algorithm) See references/explainers.md for detailed information on all explainer types. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/shap--39d1e0