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.
Install
npx skills add davila7/claude-code-templates --skill shapnpx skills add davila7/claude-code-templates --skill shap -a claude-code -gnpx skills add davila7/claude-code-templates --skill shap -a codex -ggemini skills install https://github.com/davila7/claude-code-templates.git --path cli-tool/components/skills/scientific/shapgh skill install davila7/claude-code-templates shapgit clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/davila7__claude-code-templates
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/davila7__claude-code-templates/cli-tool/components/skills/scientific/shap ~/.claude/skills/shap # Claude Code
cp -R /tmp/davila7__claude-code-templates/cli-tool/components/skills/scientific/shap ~/.agents/skills/shap # Codex, Cursor, Gemini CLI, Copilot, OpenCode