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Cursor AI rules for Java

Stargazers over time

Stargazers over time

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Goal

The project provides a curated collection of System prompts, Skills & Agents for modern SDLC that help software engineers and pipelines in their daily work for Java Enterprise development.

The project add support for:

  • Agile: User Stories & Gherkin
  • Architecture: ADRs& UML/C4/ER Diagrams
  • AI Tooling: AGENTS.md & AI Planning
  • Java development: Build system based on Maven, Design, Coding, Testing, Observability, Refactoring & JMH Benchmarking, Performance testing with JMeter, Profiling with Async profiler/OpenJDK tools & Documentation
  • Java frameworks: Spring Boot, Quarkus & Micronaut

Deliverables

The project generates a set of deliverables at the end of any iteration.

Deliverable Location
System prompts for Java Catalog (.cursor/rules)
Skills for Java https://skills.sh/jabrena/cursor-rules-java
Agents for Java Catalog (.cursor/agents)

Getting started

Read the following comprehensive guides to use this project today.

How to use them?

The SLDC has evolved with the arrival of this new set of AI tooling, enhancing the Software Engineering process. In the development of this project, it was identified 3 different workflows: Prompting Enginering Workflow, Pipelines Workflow & Agentic Workflow.

Prompting Enginering Workflow

In this workflow, the Software engineer interact with models using User prompts and in an incremental way you delegate a delegate completely a task or ask help in certain moments. You could use this project to refactor the code generated or delegate the task and associate a System prompt / Skills to that task.

Pipelines Workflow

Adding AI tools to your pipeline can provide new opportunities to deliver more value (examples: automatic coding, code refactoring, continuous profiling, and others).

Agentic Workflow

Agents for Java Enterprise development were designed to cover the whole SLDC starting from the requirements and the architectural process. Once you have the foundations well designed. it is possible to execute Plans, iterate and discuss it and when the plans associated to User Stories has the right size & complexity, it is easy to pass to the models to be implemented with security in other case, you will have the classical issues with implementations without control.

Limitations

Lack of determinism

From the outset, be aware that the results provided by interactions with the different Cursor rules are not deterministic due to the nature of the models, but this can be mitigated with clear goals and validation checkpoints.

Limits of interactions with models

Models are able to generate code, but they cannot run code with your local data. To address this limitation, some prompts provide scripts to bridge this gap on the model side.

Contribute

If you have great ideas, read the following document to contribute.

Examples

The repository includes a collection of examples where you can explore the possibilities of these system prompts designed for Java.

Architectural decision records, ADR

Date ID Name
2026-03-01 ADR-004 Skill Generation
2025-09-16 ADR-003 Website Generation
2025-07-10 ADR-002 Cursor Rules scope configuration
2025-07-08 ADR-001 Cursor Rules generation from XML Files

Changelog

Java JEPS from Java 8

Java uses JEPs as the vehicle to describe new features to be added to the language. The repository continuously reviews which JEPs could improve any of the cursor rules present in this repository.

Meetups, Conferences, Workshops & Articles

Codemotion / Madrid (2026/04/20)

W-JAX / Munich (2025/11/06 - 10:30 - 11:30)

Devoxx BE / Antwerp (2025/10/07 - 18:20 - 18:50)

Madrid Jug / Madrid (2025/05/06 - 19:00)

Blogs

References

Cursor rules ecosystem

Powered by Cursor with ❤️ from Madrid