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skills/model-explainer/SKILL.md

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+---
+name: model-explainer
+description: |
+  Model interpretability and explainability using SHAP, LIME, feature importance, and partial dependence plots. Activates for "explain model", "model interpretability", "SHAP", "LIME", "feature importance", "why prediction", "model explanation". Generates human-readable explanations for model predictions, critical for trust, debugging, and regulatory compliance.
+---
+
+# Model Explainer
+
+## Overview
+
+Makes black-box models interpretable. Explains why models make specific predictions, which features matter most, and how features interact. Critical for trust, debugging, and regulatory compliance.
+
+## Why Explainability Matters
+
+- **Trust**: Stakeholders trust models they understand
+- **Debugging**: Find model weaknesses and biases
+- **Compliance**: GDPR, fair lending laws require explanations
+- **Improvement**: Understand what to improve
+- **Safety**: Detect when model might fail
+
+## Explanation Types
+
+### 1. Global Explanations (Model-Level)
+
+**Feature Importance**:
+```python
+from specweave import explain_model
+
+explainer = explain_model(
+    model=trained_model,
+    X_train=X_train,
+    increment="0042"
+)
+
+# Global feature importance
+importance = explainer.feature_importance()
+```
+
+Output:
+```
+Top Features (Global):
+1. transaction_amount (importance: 0.35)
+2. user_history_days (importance: 0.22)
+3. merchant_reputation (importance: 0.18)
+4. time_since_last_transaction (importance: 0.15)
+5. device_type (importance: 0.10)
+```
+
+**Partial Dependence Plots**:
+```python
+# How does feature affect prediction?
+explainer.partial_dependence(feature="transaction_amount")
+```
+
+### 2. Local Explanations (Prediction-Level)
+
+**SHAP Values**:
+```python
+# Explain single prediction
+explanation = explainer.explain_prediction(X_sample)
+```
+
+Output:
+```
+Prediction: FRAUD (probability: 0.92)
+
+Why?
++ transaction_amount=5000 → +0.45 (high amount increases fraud risk)
++ user_history_days=2 → +0.30 (new user increases risk)
++ merchant_reputation=low → +0.25 (suspicious merchant)
+- time_since_last_transaction=1hr → -0.08 (recent activity normal)
+
+Base prediction: 0.10
+Final prediction: 0.92
+```
+
+**LIME Explanations**:
+```python
+# Local interpretable model
+lime_exp = explainer.lime_explanation(X_sample)
+```
+
+## Usage in SpecWeave
+
+```python
+from specweave import ModelExplainer
+
+# Create explainer
+explainer = ModelExplainer(
+    model=model,
+    X_train=X_train,
+    feature_names=feature_names,
+    increment="0042"
+)
+
+# Generate all explanations
+explainer.generate_all_reports()
+
+# Creates:
+# - feature-importance.png
+# - shap-summary.png
+# - pdp-plots/
+# - local-explanations/
+# - explainability-report.md
+```
+
+## Real-World Examples
+
+### Example 1: Fraud Detection
+
+```python
+# Explain why transaction flagged as fraud
+transaction = {
+    "amount": 5000,
+    "user_age_days": 2,
+    "merchant": "new_merchant_xyz"
+}
+
+explanation = explainer.explain(transaction)
+print(explanation.to_text())
+```
+
+Output:
+```
+FRAUD ALERT (92% confidence)
+
+Main factors:
+1. Large transaction amount ($5000) - Very unusual for new users
+2. Account only 2 days old - Fraud pattern
+3. Merchant has low reputation score - Red flag
+
+If this is legitimate:
+- User should verify identity
+- Merchant should be manually reviewed
+```
+
+### Example 2: Loan Approval
+
+```python
+# Explain loan rejection
+applicant = {
+    "income": 45000,
+    "credit_score": 620,
+    "debt_ratio": 0.45
+}
+
+explanation = explainer.explain(applicant)
+print(explanation.to_text())
+```
+
+Output:
+```
+LOAN DENIED
+
+Main reasons:
+1. Credit score (620) below threshold (650) - Primary factor
+2. High debt-to-income ratio (45%) - Risk indicator
+3. Income ($45k) adequate but not strong
+
+To improve approval chances:
+- Increase credit score by 30+ points
+- Reduce debt-to-income ratio below 40%
+```
+
+## Regulatory Compliance
+
+### GDPR "Right to Explanation"
+
+```python
+# Generate GDPR-compliant explanation
+gdpr_explanation = explainer.gdpr_explanation(prediction)
+
+# Includes:
+# - Decision rationale
+# - Data used
+# - How to contest decision
+# - Impact of features
+```
+
+### Fair Lending Act
+
+```python
+# Check for bias in protected attributes
+bias_report = explainer.fairness_report(
+    sensitive_features=["gender", "race", "age"]
+)
+
+# Detects:
+# - Disparate impact
+# - Feature bias
+# - Recommendations for fairness
+```
+
+## Visualization Types
+
+1. **Feature Importance Bar Chart**
+2. **SHAP Summary Plot** (beeswarm)
+3. **SHAP Waterfall** (single prediction)
+4. **Partial Dependence Plots**
+5. **Individual Conditional Expectation** (ICE)
+6. **Force Plots** (interactive)
+7. **Decision Trees** (surrogate models)
+
+## Integration with SpecWeave
+
+```bash
+# Generate all explainability artifacts
+/ml:explain-model 0042
+
+# Explain specific prediction
+/ml:explain-prediction --increment 0042 --sample sample.json
+
+# Check for bias
+/ml:fairness-check 0042
+```
+
+Explainability artifacts automatically included in increment documentation and COMPLETION-SUMMARY.
+
+## Best Practices
+
+1. **Generate explanations for all production models** - No "black boxes" in production
+2. **Check for bias** - Test sensitive attributes
+3. **Document limitations** - What model can't explain
+4. **Validate explanations** - Do they make domain sense?
+5. **Make explanations accessible** - Non-technical stakeholders should understand
+
+Model explainability is non-negotiable for responsible AI deployment.