Elite WindSurf GitHub Playbook - ML Malware Detection

# Elite WindSurf GitHub Playbook - ML Malware Detection

**Purpose:** Production-grade ML Malware Detection System with Streamlit UI, ensemble models, and explainable AI features.

## Master Prompt Applied

You are WindSurf acting as a Senior ML/AI Software Engineer following our **Elite Repository Standard**.

### Project Details
- **PROJECT_NAME**: ml-malware-detection-system
- **PACKAGE_NAME**: ml_malware_detection
- **ONE-LINER**: Advanced malware detection system using ensemble ML models with explainable AI and grey-zone decision gating

### ML/AI Specific Implementation
- **Models**: 9 trained models (ensemble voting, stacking, HGB, RF, SVM, etc.)
- **Features**: 60+ engineered features from PE file analysis
- **Decision Logic**: Grey-zone gating with anomaly detection backstops
- **Explainability**: SHAP integration for model interpretability
- **UI**: Streamlit web application for analyst workflow

### Repository Structure Applied
```
ml-malware-detection-system/
├── README.md                    # Comprehensive project documentation
├── pyproject.toml              # Modern Python packaging
├── .pre-commit-config.yaml     # Code quality automation
├── Makefile                    # Development commands
├── .github/workflows/          # CI/CD and security
├── docs/                       # ML-specific documentation
├── src/ml_malware_detection/   # Source code package
├── models/                     # Trained model artifacts (gitignored)
├── data/                       # Data and feature metadata (gitignored)
├── notebooks/                  # Analysis and training notebooks
├── inference/                  # Prediction CLI and API
├── streamlit_app.py           # Web application
├── tests/                     # Test suite
└── examples/                  # Usage examples
```

### Security & Compliance Features
- Model artifact management with size logging
- Data privacy protection (gitignored datasets)
- Dependency vulnerability scanning
- Supply chain security with SBOM
- Decision audit trails and forensics logging

This playbook ensures your ML malware detection system meets enterprise security standards while maintaining research reproducibility.