AI Context Schema: A Universal Configuration Framework for Multi-Platform AI Development Tools

The proliferation of AI-assisted development tools has created a new challenge: configuration fragmentation. Developers using multiple AI coding assistants must manage platform-specific configuration formats, leading to vendor lock-in, maintenance overhead, and reduced experimentation. We present AI Context Schema, a universal configuration framework that enables developers to define AI assistant context once and deploy it across multiple platforms automatically. Our approach uses a platform-agnostic schema with intelligent adapters that preserve behavioral intent while handling platform-specific constraints. We demonstrate the effectiveness of our approach through a reference implementation supporting four major AI platforms (Claude Code, Cursor, Windsurf, GitHub Copilot) and show that universal schemas can eliminate configuration fragmentation while maintaining platform-specific optimizations. Our work establishes the foundation for interoperable AI development tooling and provides a path toward standardization in this rapidly evolving ecosystem.