AI SummaryA production-grade Windsurf prompt that scaffolds an ML malware detection system with ensemble models, SHAP explainability, and Streamlit UI. Security engineers and ML developers benefit from a complete, opinionated template for building enterprise malware classification tools.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to add the "ml-malware-detection-system — Windsurf Rules" prompt rules to my project. Repository: https://github.com/mwill20/ml-malware-detection-system Please read the repo to find the rules/prompt file, then: 1. Download it to the correct location (.cursorrules, .windsurfrules, .github/prompts/, or project root — based on the file type) 2. If there's an existing rules file, merge the new rules in rather than overwriting 3. Confirm what was added
Description
Windsurf Rules for ml-malware-detection-system
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.
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
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Works With
Any AI assistant that accepts custom rules or system prompts