Skill · em Dados, IA e pesquisa

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…

Procedência

Antes de instalar

5 arquivos · 82,6 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/shap/.

d=".claude/skills/shap"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/shap"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/explainers.md" "$u/references/explainers.md" \
  -o "$d/references/plots.md" "$u/references/plots.md" \
  -o "$d/references/theory.md" "$u/references/theory.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/skills/shap/.

d="$HOME/.claude/skills/shap"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/shap"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/explainers.md" "$u/references/explainers.md" \
  -o "$d/references/plots.md" "$u/references/plots.md" \
  -o "$d/references/theory.md" "$u/references/theory.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: instala em .agents/skills/shap/.

d=".agents/skills/shap"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/shap"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/explainers.md" "$u/references/explainers.md" \
  -o "$d/references/plots.md" "$u/references/plots.md" \
  -o "$d/references/theory.md" "$u/references/theory.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.agents/skills/shap/.

d="$HOME/.agents/skills/shap"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/shap"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/explainers.md" "$u/references/explainers.md" \
  -o "$d/references/plots.md" "$u/references/plots.md" \
  -o "$d/references/theory.md" "$u/references/theory.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

Neste projeto: instala em .agents/skills/shap/.

d=".agents/skills/shap"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/shap"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/explainers.md" "$u/references/explainers.md" \
  -o "$d/references/plots.md" "$u/references/plots.md" \
  -o "$d/references/theory.md" "$u/references/theory.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.gemini/antigravity-cli/skills/shap/.

d="$HOME/.gemini/antigravity-cli/skills/shap"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/shap"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/explainers.md" "$u/references/explainers.md" \
  -o "$d/references/plots.md" "$u/references/plots.md" \
  -o "$d/references/theory.md" "$u/references/theory.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.shap

Prévia do SKILL.md

---
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 fairnes…
---

# SHAP (SHapley Additive exPlanations)

## Overview

SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for:

- Computing SHAP values for any model type
- Creating visualizations to understand feature importance
- Debugging and validating model behavior
- Analyzing fairness and bias
- Implementing explainable AI in production

SHAP works with all model types: tree-based models (XGBoost, LightGBM, CatBoost, Random Forest), deep learning models (TensorFlow, PyTorch, Keras), linear models, and black-box models.

## When to Use This Skill

**Trigger this skill when users ask about**:
- "Explain which features are most important in my model"
- "Generate SHAP plots" (waterfall, beeswarm, bar, scatter, force, heatmap, etc.)
- "Why did my model make this prediction?"
- "Calculate SHAP values for my model"
- "Visualize feature importance using SHAP"
- "Debug my model's behavior" or "validate my model"
- "Check my model for bias" or "analyze fairness"
- "Compare feature importance across models"
- "Implement explainable AI" or "add explanations to my model"
- "Understand feature interactions"
- "Create model interpretation dashboard"

## Quick Start Guide

### Step 1: Select the Right Explainer

**Decision Tree**:
…

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