SHAP is a tool that shows you why a machine learning model made a certain prediction. It uses a fair method from game theory to divide the credit among all the features. This helps you trust your model and find problems.
You can use SHAP with any kind of model like trees, neural networks, or simple linear models. It creates helpful pictures like waterfall and beeswarm plots. These pictures make it easy to see which features matter most.
SHAP also helps you debug your model and check for bias. It is a key part of explainable AI. Anyone who builds or uses machine learning models can benefit from SHAP.
Add Shap skill to your workflow
Global
mkdir -p ~/.claude/skills/shapProject
mkdir -p .claude/skills/shapSource Repository
More Data Science & ML Claude Code Skills
Explore Runlllllllama/rigorpilot-skills
Plan and rank quick deep learning exploration runs with clear boundaries
Run Trainlllllllama/rigorpilot-skills
Run deep learning training conservatively with structured logs and metrics
Env And Assets Bootstraplllllllama/rigorpilot-skills
Safe and careful setup of conda environments and asset paths for deep learning reproduction
Env And Assets Bootstraplllllllama/ai-paper-reproduction-skill
Prepare conda-first environment and asset paths for deep learning reproduction
Data Visualizationanthropics/knowledge-work-plugins
Pick the right chart, write clean Python code, and design for everyone
Powerbi Modelinggithub/awesome-copilot
Expert guidance for building optimized Power BI semantic models with star schemas and DAX
Power Bi Dax Optimizationgithub/awesome-copilot
Make your Power BI DAX formulas faster and easier to maintain
Bigquery Basicsgoogle/skills
Easily analyze massive datasets with SQL and built-in machine learning