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Schema-generation-blueprint
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| # Schema Generation Playbook | |
| ## Overview | |
| This playbook provides a systematic approach to generating comprehensive, production-ready schemas for any domain. It combines domain expertise, automated research, and validation to ensure high-quality results. | |
| ## Core Principles | |
| ### 1. Domain-First Approach | |
| - **Always start with domain analysis** - Understand the specific requirements and patterns | |
| - **Research before generating** - Use MCP tools to gather relevant information | |
| - **Apply domain expertise** - Use established patterns and best practices | |
| - **Consider the full lifecycle** - From development to production to scaling | |
| ### 2. Production-Ready Focus | |
| - **Security by default** - Include proper authentication, authorization, and data protection | |
| - **Performance considerations** - Design for expected load and growth | |
| - **Cost optimization** - Balance features with operational costs | |
| - **Monitoring and observability** - Include logging, metrics, and alerting | |
| ### 3. Implementation-Focused | |
| - **Actionable schemas** - Provide clear next steps and implementation guidance | |
| - **Technology-specific** - Use appropriate tools and frameworks | |
| - **Best practices** - Follow established patterns and conventions | |
| - **Error handling** - Include comprehensive error scenarios | |
| ## Schema Generation Process | |
| ### Phase 1: Domain Analysis | |
| **Goal**: Understand what the user wants to build and identify the optimal approach. | |
| **Steps**: | |
| 1. **Parse user intent** - Extract key requirements and constraints | |
| 2. **Classify domain type** - Identify primary category (API, database, AI service, etc.) | |
| 3. **Identify stakeholders** - Consider users, developers, and business requirements | |
| 4. **Define success criteria** - What makes this schema successful? | |
| **Key Questions**: | |
| - What type of application is this? | |
| - Who are the end users? | |
| - What are the performance requirements? | |
| - What's the expected scale? | |
| - What's the budget/cost sensitivity? | |
| - What are the security requirements? | |
| ### Phase 2: Research & Discovery | |
| **Goal**: Gather comprehensive information about the domain and requirements. | |
| **Research Activities**: | |
| - **API Documentation**: Use `WebFetch` to get official API docs | |
| - **Framework Patterns**: Use `mcp__context7__*` to find established patterns | |
| - **Service Information**: Use `mcp__supabase__*` for database patterns | |
| - **Community Solutions**: Use `WebSearch` for latest approaches and solutions | |
| - **Cost Analysis**: Research pricing models and optimization strategies | |
| **Research Template**: | |
| ```markdown | |
| ## Research Summary | |
| - **Domain**: [API Integration/Database Design/AI Service/etc.] | |
| - **Key Technologies**: [List relevant technologies] | |
| - **Patterns Found**: [Established patterns and best practices] | |
| - **Cost Considerations**: [Pricing models and optimization opportunities] | |
| - **Security Requirements**: [Authentication, authorization, data protection] | |
| - **Performance Considerations**: [Latency, throughput, scalability] | |
| ``` | |
| ### Phase 3: Schema Architecture | |
| **Goal**: Design the high-level architecture and data flow. | |
| **Architecture Components**: | |
| 1. **Data Models** - Core entities and relationships | |
| 2. **API Design** - Endpoints, methods, and request/response formats | |
| 3. **Business Logic** - Workflows, validation, and processing rules | |
| 4. **Integration Points** - External services and dependencies | |
| 5. **Storage Strategy** - Database design and data persistence | |
| 6. **Security Framework** - Authentication, authorization, and data protection | |
| **Architecture Template**: | |
| ```json | |
| { | |
| "architecture": { | |
| "components": [ | |
| { | |
| "name": "component_name", | |
| "type": "api|database|service|integration", | |
| "purpose": "description", | |
| "dependencies": ["list", "of", "dependencies"] | |
| } | |
| ], | |
| "data_flow": [ | |
| { | |
| "from": "source", | |
| "to": "destination", | |
| "data": "data_type", | |
| "method": "synchronous|asynchronous" | |
| } | |
| ], | |
| "integration_points": [ | |
| { | |
| "service": "external_service", | |
| "purpose": "integration_purpose", | |
| "authentication": "auth_method", | |
| "rate_limits": "rate_limit_info" | |
| } | |
| ] | |
| } | |
| } | |
| ``` | |
| ### Phase 4: Schema Generation | |
| **Goal**: Create comprehensive, production-ready schema with all necessary components. | |
| **Schema Components**: | |
| 1. **Core Schema** - Primary data structures and relationships | |
| 2. **API Specification** - OpenAPI/Swagger documentation | |
| 3. **Database Schema** - Tables, indexes, and relationships | |
| 4. **Configuration** - Environment variables and settings | |
| 5. **Security Policies** - Authentication, authorization, and validation rules | |
| 6. **Error Handling** - Error codes, messages, and recovery strategies | |
| 7. **Monitoring** - Logging, metrics, and alerting configuration | |
| **Quality Criteria**: | |
| - **Completeness** - All necessary components included | |
| - **Consistency** - Naming conventions and patterns followed | |
| - **Scalability** - Designed for growth and performance | |
| - **Security** - Proper authentication and data protection | |
| - **Maintainability** - Clear structure and documentation | |
| ### Phase 5: Validation & Refinement | |
| **Goal**: Ensure the schema meets all requirements and follows best practices. | |
| **Validation Checklist**: | |
| - [ ] **Security Review** - Authentication, authorization, input validation | |
| - [ ] **Performance Review** - Indexes, caching, query optimization | |
| - [ ] **Cost Review** - Resource usage, pricing implications | |
| - [ ] **Scalability Review** - Growth patterns, bottlenecks | |
| - [ ] **Maintainability Review** - Code organization, documentation | |
| - [ ] **Integration Review** - External dependencies, API contracts | |
| **Refinement Process**: | |
| 1. **Identify gaps** - Missing components or considerations | |
| 2. **Optimize performance** - Improve efficiency and speed | |
| 3. **Enhance security** - Strengthen authentication and data protection | |
| 4. **Reduce costs** - Optimize resource usage and pricing | |
| 5. **Improve usability** - Simplify integration and usage | |
| ### Phase 6: Documentation & Implementation Guide | |
| **Goal**: Provide clear next steps and implementation guidance. | |
| **Documentation Requirements**: | |
| 1. **Schema Overview** - Purpose, architecture, and key components | |
| 2. **Implementation Guide** - Step-by-step setup and configuration | |
| 3. **API Documentation** - Endpoints, parameters, and examples | |
| 4. **Security Setup** - Authentication, authorization, and best practices | |
| 5. **Deployment Guide** - Environment setup and deployment strategies | |
| 6. **Monitoring Setup** - Logging, metrics, and alerting configuration | |
| 7. **Cost Optimization** - Strategies to minimize operational costs | |
| ## Domain-Specific Considerations | |
| ### API Integration Schemas | |
| - **Authentication patterns** - OAuth, API keys, JWT tokens | |
| - **Rate limiting** - Request throttling and backoff strategies | |
| - **Error handling** - Retry logic and failure scenarios | |
| - **Caching** - Response caching and invalidation strategies | |
| - **Webhooks** - Event-driven updates and notifications | |
| ### AI/ML Service Schemas | |
| - **Model selection** - Choosing appropriate models for the task | |
| - **Input validation** - Ensuring safe and valid inputs | |
| - **Job queuing** - Managing async processing and results | |
| - **Cost optimization** - Minimizing API calls and processing costs | |
| - **Result caching** - Storing and reusing previous results | |
| ### Database Design Schemas | |
| - **Data modeling** - Entities, relationships, and constraints | |
| - **Indexing strategy** - Performance optimization and query patterns | |
| - **Migration patterns** - Schema evolution and data migration | |
| - **Backup and recovery** - Data protection and disaster recovery | |
| - **Performance tuning** - Query optimization and scaling strategies | |
| ### User-Facing Application Schemas | |
| - **User management** - Authentication, profiles, and permissions | |
| - **UI/UX patterns** - Interface design and user experience | |
| - **State management** - Application state and data synchronization | |
| - **Offline support** - Caching and offline functionality | |
| - **Progressive enhancement** - Feature rollout and A/B testing | |
| ## Tools and Resources | |
| ### MCP Tools for Research | |
| - `mcp__supabase__list_tables` - Database schema examples | |
| - `mcp__supabase__generate_typescript_types` - Type generation patterns | |
| - `mcp__context7__resolve-library-id` - Find relevant libraries | |
| - `mcp__context7__get-library-docs` - Get framework documentation | |
| ### Web Research Tools | |
| - `WebFetch` - Get official API documentation | |
| - `WebSearch` - Find community solutions and patterns | |
| ### Common Research Queries | |
| - "[Technology] best practices" | |
| - "[Service] authentication patterns" | |
| - "[Framework] production setup" | |
| - "[Domain] security considerations" | |
| - "[Service] cost optimization" | |
| - "[Technology] monitoring and observability" | |
| ## Example Research Workflow | |
| ### For "External API Integration Project": | |
| 1. **Domain Analysis** - API integration + data processing | |
| 2. **Research Phase**: | |
| - `WebFetch` - Get API documentation and patterns | |
| - `mcp__context7__resolve-library-id` - Find relevant libraries | |
| - `mcp__supabase__list_tables` - Database integration patterns | |
| 3. **Schema Generation** - Job queue + webhook handlers + storage | |
| 4. **Validation** - Security, performance, cost optimization | |
| 5. **Documentation** - Implementation guide and best practices | |
| ## Success Metrics | |
| ### Technical Quality | |
| - **Schema completeness** - All necessary components included | |
| - **Security score** - Proper authentication and data protection | |
| - **Performance optimization** - Efficient queries and caching | |
| - **Cost efficiency** - Optimized resource usage | |
| ### Business Value | |
| - **Implementation clarity** - Clear next steps and guidance | |
| - **Time to market** - Reduced development time | |
| - **Operational efficiency** - Monitoring and maintenance considerations | |
| - **Scalability planning** - Growth and expansion strategies | |
| ## Continuous Improvement | |
| ### Feedback Loop | |
| - **User feedback** - Gather feedback on generated schemas | |
| - **Implementation results** - Track success of schema implementations | |
| - **Performance metrics** - Monitor schema performance in production | |
| - **Cost analysis** - Evaluate cost efficiency of generated schemas | |
| ### Pattern Library | |
| - **Successful patterns** - Document and reuse successful approaches | |
| - **Common pitfalls** - Identify and avoid common mistakes | |
| - **Domain expertise** - Build domain-specific knowledge base | |
| - **Tool effectiveness** - Evaluate and improve tool usage patterns |
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