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Skills & Agents for Java

Stargazers over time

Stargazers over time

CI Builds

Goal

A curated collection of Skills and Agents for Java Enterprise development, designed to streamline modern SDLC workflows.

QUESTION ROLE AREA SUPPORT
WHAT / WHEN PO, BA, EA, SA, TL Agile & Planning User Stories, Github Issues & Jira
WHY EA, SL, TL Architecture ADRs & UML / C4 / ER Diagrams
HOW SA, TL, SWE Spec-Driven AI Plan mode & OpenSpec
HOW TL, SWE 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, Spring Boot, Quarkus, Micronaut, OpenAPI, Wiremock & AGENTS.md

Deliverables

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

Deliverable Instalation Getting Started
1. Skills for Java npx skills add jabrena/cursor-rules-java --all --agent cursor Skills for Java
2. Agents for Java @003-agents-installation Install Agents in Cursor Agents for Java

Note: Once you install all skills, you could install the agents easily for Cursor or Claude.

Compatibility

This project is compatible with any Tool compatible with Skills, Agents, AGENTS.md & MCP Servers.

How to use them?

The SDLC 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.

Agent Workflow

Agents for Java Enterprise development were designed to help the Software engineer in the implementation phase. The software engineer define good Specs and that Specifications are delegated to Agents.

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).

Futher information here`

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.

Not all models behave in the same way

In the project exist some Interactive skills which require to use Premium models to run in an interactive way in other case, they will work like in sequence of steps.

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

See CONTRIBUTING.md for conventions, generator workflows, tests, and how to open a pull request.

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

  • Review the ADR index for the complete list.

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 - 11:00 - 12:30)

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

Other developments

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