Menu

Individual skill

shap

0upvotes

Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.

skills/shap/SKILL.md

Read pinned source

Source commit 2d2065fb4520fa3f3f2bcf61cff26ec8b7eba4b5

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 10, 2026

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

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
56,931
Repository last updated
Nov 9, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# SHAP

Use SHAP to describe how a fitted predictive model maps inputs to outputs. Work from the modern `shap.Explanation` API, make the explained output and background distribution explicit, and validate every explanation before interpreting it.

This skill is aligned with **SHAP 0.52.0** (released 2026-05-28). That release requires Python 3.12 or newer.

The maintained examples were checked on Python 3.12.10 with SHAP 0.52.0, NumPy 2.5.3, pandas 3.0.6, scikit-learn 1.9.1, and matplotlib 3.11.2. Native tests cover small tree, exact, permutation, partition, linear, additive, kernel, text, and constant-image games; additional numeric XGBoost 3.4.1 smoke checks cover raw, probability, loss, and interaction outputs. Reference snippets that require a project model, framework, or data are adaptation templates; optional pretrained/deep, GPU, and distributed integrations remain illustrative and require their own runtime validation.

## Operating Rules

1. Explain a fixed, evaluated model; do not use SHAP as a substitute for predictive validation.
2. Use held-out or clearly labeled analysis rows for explanations. Choose background rows only from an appropriate training or reference population.
3. State the explained output: regression value, raw margin, probability, log loss, logit, or another model method.
4. Prefer `shap.Explanation` objects and `explainer(X)`. Some specialized options still require `.shap_values(X)`, including deep ranked outputs, gradient sampling budgets, and Kernel SHAP `nsamples`. Preserve their output indexes and baselines explicitly.
Back to top