Individual skill

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.

tests/ext_conformance/artifacts/templates-davila7/cli-tool/components/skills/scientific/shap/SKILL.md

Source commit a177737d6df6a543d21d3b7ee6163ec715d24aa8

What was checked

Matches the observed repository HEAD

Yes

Scanner rules are current

No

Repository scan complete

No

Skill scan complete

No

Observed behavior

Not checked yet

Last checked

Nov 8, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

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