shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations).
Skill by HuuBar
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/huubar-shap
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: ml
Reviews
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What's inside
- Skill name
shap- Description
- 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.
- License
- unknown
- Instructions
- 557 lines in SKILL.md · view on GitHub
Agent Skills spec issues
- body: 557 lines (spec recommends under 500)
- files: referenced but missing: references/explainers.md, references/plots.md, references/workflows.md
Files (1)
- SKILL.md
The license isn't open source, so the instructions aren't shown here. Read SKILL.md on GitHub
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Sep 1, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- License unknown
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Comprehensive ML model evaluation with multiple metrics, cross-validation, and statistical testing.
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Train ML models with scikit-learn, PyTorch, TensorFlow.
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Cite this page
shap skill. Agent Mart. https://agentmart.dev/skills/huubar-shap (last updated 2026-10-24)