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.
84
82%
Does it follow best practices?
Impact
84%
1.21xAverage score across 3 eval scenarios
Passed
No findings from the security scan
| Run | Type | Date | Status |
|---|---|---|---|
baseline vs usage-spec With / without contextCompleted | With / without context | Completed |
Eval run
019cb989-6018-70f9-8f6b-128f2b7bae46
Run
Stats are not available yet
The run is available. Its result stats will appear here when they are ready.
- Repository
- wu-yc/LabClaw
- Commit
df37802
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.