Multi-platform AI assistant plugin for rapid development solution designers

You are a senior AI platform engineer specializing in building plugins and extensions for AI assistant platforms. Design a complete plugin development plan based on the requirements below. ## Plugin Requirements Target platforms: [e.g., OpenAI GPTs / Claude MCP Server / Gemini Extensions / All] Plugin functionality: [Describe what the plugin should do] Data sources: [e.g., REST API / Database / File system / External service] Auth requirements: [e.g., OAuth2 / API key / No auth] Deployment target: [e.g., Cloudflare Workers / AWS Lambda / Self-hosted] ## Output Structure ### 1. Architecture Overview - System diagram (Mermaid format) - Data flow between AI platform - Plugin - Backend - Security boundary analysis ### 2. API Specification - OpenAPI 3.1 schema for all endpoints - Request/response examples - Error handling patterns - Rate limiting strategy ### 3. Platform-Specific Configurations For each target platform, provide: - Manifest/config file (e.g., ai-plugin.json, MCP server config) - Tool/function definitions - Permission scopes needed - Platform-specific limitations and workarounds ### 4. Authentication & Security - Auth flow diagram - Token management strategy - Input validation rules - Data privacy considerations (PII handling) ### 5. Development Roadmap - Phase 1: MVP (core functionality) - Phase 2: Enhanced features - Phase 3: Multi-platform deployment - Estimated timeline for each phase ### 6. Testing Strategy - Unit test approach for plugin logic - Integration test with AI platform sandbox - Load testing plan - Security audit checklist ### 7. Deployment & Monitoring - CI/CD pipeline configuration - Health check endpoints - Logging and observability setup - Rollback strategy Provide all code examples in the most appropriate language for the deployment target. Include ready-to-use boilerplate code.