learn-dto-entity

To document data layer patterns, you can scan your source code to extract Data Layer DNA, capturing base entity classes, repository interfaces, and factory wiring into a knowledge_dto_entity.md file for LLM reuse. This process automatically identifies and catalogs your data access patterns.

Extracting Entity to Model conversion rules involves scanning the codebase to capture Factory wiring and mapping annotations. This approach automatically identifies inheritance structures and conversion logic, outputting real-source-based templates into a structured markdown knowledge file.

Cataloging repository query patterns requires scanning repository interfaces to identify findByXxx methods, custom @Query annotations, and pagination logic. The extracted query patterns are compiled into a comprehensive markdown document for future code scaffolding.

Yes, automated code analysis can generate scaffolding templates by scanning your codebase to discover base Entity and Model patterns. It captures annotations and mapping rules, producing a knowledge file with real-source-based templates to guide rapid data layer generation.

Yes, this documentation approach applies to multi-language codebases that define DTOs, Entities, Repositories, and Factories. It analyzes base entity classes and repository interfaces across different languages to capture structural patterns and conversion rules.

Scanning your codebase for data layer structures outputs a knowledge_dto_entity.md file located in the base_knowledge/structures/propose/ directory. This markdown file contains real source-derived templates, mapping rules, and guidance for LLM reuse.