Analysis updated 2026-05-18
Drop the rule files into Cursor or Copilot so the AI writes new code in the correct DDD layer.
Use the decision tree to pick the right pattern for a new feature before writing any code.
Onboard a team onto a shared hexagonal architecture without inventing conventions from scratch.
| alizhangsan602-bit/easy-ddd | aevella/sky-pc-mcp-companion | affaan-m/behavioral_rl | |
|---|---|---|---|
| Stars | 26 | 26 | 26 |
| Language | — | Python | HTML |
| Setup difficulty | easy | moderate | moderate |
| Complexity | 1/5 | 3/5 | 4/5 |
| Audience | developer | vibe coder | researcher |
Figures from each repo's GitHub metadata at analysis time.
Easy-DDD is a collection of coding guidelines and documentation for a software design approach called Domain-Driven Design, or DDD. DDD is a way of organizing code in medium-to-large applications so that the business rules and logic stay at the center of the codebase, kept separate from database connections, external APIs, and other technical details. This project addresses a common frustration: developers often understand DDD in theory but do not know where to put the actual code or how to structure their files. The project uses a hexagonal architecture, which organizes code into six defined layers named Domain, Application, Adaptor, Infrastructure, Client, and Model. The Domain layer holds pure business logic with no technical dependencies. The Application layer handles use-case orchestration. The Adaptor layer sits between the business code and outside systems, acting as a buffer. The Infrastructure layer contains technical implementation details like database access. The Client layer defines external API contracts, and the Model layer holds shared data structures used across the other layers. The documentation covers four development patterns for common coding situations. A write pattern applies to operations that create or change data. A read pattern handles queries that only fetch data. A rules-plus-calculation pattern covers cases involving business rules without state changes. A pure calculation pattern handles computations with no data persistence, such as fee calculation or view rendering. One feature the README highlights is that the specification documents are designed to work as AI coding assistant rules. You can drop the Markdown files into the configuration directory for tools like Cursor or GitHub Copilot, and the AI assistant will follow the DDD conventions when generating code. The repository contains separate Markdown files for each architectural layer, an overview document, and a decision tree for choosing which pattern fits a given business scenario. MIT licensed.
A set of Markdown coding guidelines that show developers exactly where each piece of code goes when building an app with Domain-Driven Design.
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.