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datarobot-model-explainability

Skill

Tools and guidance for model explainability, prediction explanations, feature impact analysis, SHAP values, SHAP distributions, anomaly assessment, and model diagnostics. Use when analyzing model explanations, feature impact, SHAP values, SHAP distributions, anomaly assessment, or diagnosing model b

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Install

git clone https://github.com/datarobot-oss/datarobot-agent-skills.git ~/.claude/skills/datarobot-model-explainability

What is datarobot-model-explainability?

Tools and guidance for model explainability, prediction explanations, feature impact analysis, SHAP values, SHAP distributions, anomaly assessment, and model diagnostics. Use when analyzing model explanations, feature impact, SHAP values, SHAP distributions, anomaly assessment, or diagnosing model behavior.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~77 tokens of context used while enabled, before you invoke anything

Documentation

README · ~5 min read

DataRobot Model Explainability Skill

This skill covers SHAP insights, XEMP prediction explanations, anomaly explanations, and model diagnostics.

SDK version: Use datarobot>=3.6.0 for the full API set in this skill (ShapDistributions was added in 3.6; ShapMatrix, ShapImpact, and ShapPreview are available in datarobot>=3.4.0). Use from datarobot.insights import ShapMatrix, ... with entity_id=model_id — not legacy datarobot.models.ShapMatrix (project_id / dataset_id). ShapMatrix, ShapImpact, ShapPreview, and ShapDistributions are the canonical SHAP API. The older dr.PredictionExplanations (XEMP-based) remains available but is the secondary path.

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