shap

Trigger phrases

Phrases that activate this skill when typed to Claude Code:

• explain this model
• feature importance with SHAP
• SHAP waterfall plot
• why did the model predict
• model interpretability

What it does

shap is a Claude Code skill from K-Dense AI’s scientific-agent-skills repo. It turns Claude into a SHAP explainability expert that computes SHapley Additive exPlanations for any model — tree-based (XGBoost, LightGBM, random forest with TreeExplainer), deep learning (PyTorch, TensorFlow with DeepExplainer or GradientExplainer), linear models (LinearExplainer), and black-box models (KernelExplainer) — and generates the full suite of SHAP visualizations.

A session produces SHAP values for your model’s predictions and the relevant plots: waterfall plots for individual predictions, beeswarm plots for global feature importance, force plots for interactive explanation, scatter plots for feature interactions, and heatmaps for sample-level explanation patterns.

When to use it

Reach for it when:

• You need to explain an individual prediction to a stakeholder or in a regulatory context
• You want global feature importance that’s more reliable than impurity-based feature importance from random forests
• You’re debugging a model that’s behaving unexpectedly and need to understand which features are driving specific predictions

When not to reach for it:

• You only need rough feature rankings and are using a tree model — built-in feature_importances_ is faster
• Real-time inference contexts where SHAP computation latency is prohibitive

Install

Copy the SKILL.md from K-Dense AI’s shap folder into .claude/skills/shap/ in your project.

Trigger phrases: “explain this model”, “feature importance with SHAP”, “SHAP waterfall plot”, “why did the model predict”, “model interpretability”.

What a session looks like

A typical session has three phases:

1. Explainer selection. Claude identifies the model type and selects the fastest appropriate explainer — TreeExplainer for tree-based models (exact SHAP values, fast), DeepExplainer for neural networks, KernelExplainer for black-box models (slow, use sampling).
2. SHAP value computation. The explainer runs on the background dataset and optionally on specific instances. Claude handles the background dataset selection (k-means summarization for large datasets) and masking for text/image inputs.
3. Visualization. The requested plots are generated and saved. Claude interprets the top features and flags any SHAP values that suggest the model is using spurious correlations.

Receipts

Where it works well:

• Tree model explanations with TreeExplainer — exact SHAP values computed in seconds even for large forests, beeswarm plots give a reliable global feature ranking
• Individual prediction explanations for clinical or regulatory contexts — waterfall plots with feature contributions in natural units are interpretable to non-ML audiences

Where it backfires:

• KernelExplainer on complex black-box models is extremely slow without aggressive sampling; the approximation quality depends on the background dataset size
• SHAP values for correlated features split contributions across correlated predictors in ways that are mathematically correct but counter-intuitive for domain experts

Pattern that works: always use a background dataset that’s representative of your training distribution (not random subsets of test data); SHAP baseline values depend on the background and spurious baselines produce misleading explanations.

Source and attribution

Originally authored by K-Dense Inc.. The canonical SKILL.md lives in the shap folder of their public scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner’s perspective. For the formal spec and any updates, defer to the source repo.