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

Install

KitScout test notes

Not tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.

Similar tools

• reverse-engineer-anything 456,653 ★
  Reverse engineer native, managed, Electron/JavaScript, packaged, firmware, and browser targets with REA, and b
• scaffold-exercises 323,485 ★
  Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use
• wait-what 323,485 ★
  Stop. That last message did not land: re-pitch it.
• template-skill 193,970 ★
  Replace with description of the skill and when Claude should use it.
• ponytail-debt 190,107 ★
  List every `shortcut:` comment (and older `ponytail:` ones) as a debt ledger. One-shot report, changes nothing