alterlab-shap
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Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing explainable AI across tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model. Part of the AlterLab Academic Skills suite.
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npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-shap -a claude-code
Installs to .claude/skills/alterlab-shap/ · Each command fetches the skill straight from GitHub with the open skills CLI.
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