144-java-data-oriented-programming
Use when you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. This should trigger for requests such as Improve the code with Data-Oriented Programming; Apply Data-Oriented Programming; Refactor the code with Data-Oriented Programming; Model Java data with records and pure functions; Separate Java behavior from immutable data structures; Validate data integrity with pure Java functions. Part of Plinth Toolkit
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/jabrena/plinth --skill 144-java-data-oriented-programming- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads 144-java-data-oriented-programming automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Java Data-Oriented Programming Best Practices
Apply data-oriented programming in Java: separate data from behavior with records, use immutable data structures, pure functions for transformations, flat denormalized structures with ID references, generic-to-specific type conversion when needed, pure validation functions, and flexible generic data access layers. All transformations should be explicit, traceable, and composed of clear pure functional steps.
What is covered in this Skill?
- Separation of concerns: data structures (records, POJOs) vs behavior (utility classes, services)
- Immutability: records, final fields, transformations produce new instances
- Pure data transformations: functions depending only on inputs, no side effects
- Flat and denormalized data: ID references instead of deep nesting
- Generic until specific: Map<String,Object> for dynamic data, convert to records when processing
- Data integrity: pure validation functions returning validation results
- Flexible generic data access: generic CRUD interfaces, interchangeable implementations
Scope: The reference is organized by examples (good/bad code patterns) for each core area. Apply recommendations based on applicable examples.
Constraints
Before applying any data-oriented programming recommendations, ensure the project compiles. Compilation failure is a blocking condition. After applying improvements, run full verification.
- MANDATORY: Run
./mvnw compileormvn compilebefore applying any change - SAFETY: If compilation fails, stop immediately — do not proceed until resolved
- VERIFY: Run
./mvnw clean verifyormvn clean verifyafter applying improvements - BEFORE APPLYING: Read the reference for detailed good/bad examples, constraints, and safeguards for each data-oriented programming pattern
When to use this skill
- Improve the code with Data-Oriented Programming
- Apply Data-Oriented Programming
- Refactor the code with Data-Oriented Programming
- Model Java data with records and pure functions
- Separate Java behavior from immutable data structures
- Validate data integrity with pure Java functions
Workflow
- Compile project before data-oriented changes
Run ./mvnw compile or mvn compile and stop immediately if compilation fails.
- Read data-oriented reference and assess model design
Read references/144-java-data-oriented-programming.md and identify candidates for data/behavior separation and immutable transformations.
- Apply data-oriented refactorings
Implement selected improvements using records, pure transformation functions, flat structures, and explicit validation.
- Verify with full build
Run ./mvnw clean verify or mvn clean verify after applying improvements.
Reference
For detailed guidance, examples, and constraints, see references/144-java-data-oriented-programming.md.
FAQ
What does the 144-java-data-oriented-programming skill do?
Use when you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. This should trigger for requests such as Improve the code with Data-Oriented Programming; Apply Data-Oriented Programming; Refactor the code with Data-Oriented Programming; Model Java data with records and pure functions; Separate Java behavior from immutable data structures; Validate data integrity with pure Java functions. Part of Plinth Toolkit
How do I install 144-java-data-oriented-programming?
Run npx skills add https://github.com/jabrena/plinth --skill 144-java-data-oriented-programming in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/144-java-data-oriented-programming/ (personal) or .claude/skills/144-java-data-oriented-programming/ (project).
Which AI agents can use 144-java-data-oriented-programming?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is 144-java-data-oriented-programming safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains 144-java-data-oriented-programming?
It is published by jabrena in jabrena/plinth. It has 376 recorded installs.
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