AI SummaryFaceSwap Copilot Instructions provides a structured guide for developing a React/TypeScript face-swapping web application with real-time ML capabilities. It benefits developers building entertainment, creative, or research-focused face manipulation tools.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to add the "FaceSwap — Copilot Instructions" prompt rules to my project. Repository: https://github.com/ogemboeugene/FaceSwap 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
FaceSwap is an open-source project for swapping faces in images and videos using advanced computer vision and deep learning techniques. It provides user-friendly tools and APIs for face detection, alignment, and seamless face replacement, suitable for research, entertainment, and creative applications.
Key Features
• Real-time camera feed with face detection • Expression recognition and mapping • Face swapping with pre-loaded celebrity faces • User face upload functionality • Photo/video capture of results • Responsive design for desktop and mobile
Face Swap Application Instructions
This is a React TypeScript face swap web application that uses machine learning for real-time face detection and swapping.
Project Architecture
• Frontend: React 18 with TypeScript and Vite • Styling: Tailwind CSS for responsive, modern UI • ML Libraries: TensorFlow.js, MediaPipe, face-api.js for face detection and processing • State Management: Zustand for lightweight state management • Camera: WebRTC for real-time video capture
Development Guidelines
• Use TypeScript for all components and services • Implement proper error handling for camera access and ML model loading • Optimize for performance (target 30fps for real-time processing) • Follow React best practices with hooks and functional components • Use Tailwind utility classes for styling • Implement proper loading states and user feedback • Ensure accessibility compliance • Handle edge cases (no face detected, multiple faces, poor lighting)
Discussion
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Works With
Any AI assistant that accepts custom rules or system prompts