shap-explainer
SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.
npx skills add majiayu000/claude-skill-registry --skill shap-explainer-a5c-ai-babysitter --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# shap-explainer ## Overview SHAP-based model explainability skill for feature attribution, summary plots, interaction analysis, and model interpretation. ## Capabilities - TreeExplainer for tree-based models (XGBoost, LightGBM, Random Forest) - DeepExplainer for neural networks - KernelExplainer for model-agnostic explanations - Summary, dependence, and force plots - Interaction value computation - Cohort-based analysis - Waterfall and bar plots - Expected value analysis ## Target Processes - Model Interpretability and Explainability Analysis - Model Evaluation and Validation Framework - A/B Testing Framework for ML Models ## Tools and Libraries - SHAP - matplotlib - numpy ## Input Schema ```json { "type": "object", "required": ["modelPath", "dataPath", "explainerType"], "properties": { "modelPath": { "type": "string", "description": "Path to the trained model" }, "dataPath": { "type": "string", "description": "Path to data for explanation" }, "explainerType": { "type": "string", "enum": ["tree", "deep", "kernel", "linear", "gradient"], "description": "Type of SHAP explainer to use" }, "analysisConfig": { "type": "object", "properties": { "numSamples": { "type": "integer" }, "back
- Overview
- Capabilities
- Target Processes
- Tools and Libraries
- Input Schema
- Output Schema
- Usage Example
What does the shap-explainer skill do?
SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill shap-explainer-a5c-ai-babysitter --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.