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
npx skills add https://github.com/jabrena/plinth --skill 144-java-data-oriented-programming
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?
Scope: The reference is organized by examples (good/bad code patterns) for each core area. Apply recommendations based on applicable examples.
Before applying any data-oriented programming recommendations, ensure the project compiles. Compilation failure is a blocking condition. After applying improvements, run full verification.
./mvnw compile or mvn compile before applying any change./mvnw clean verify or mvn clean verify after applying improvementsRun ./mvnw compile or mvn compile and stop immediately if compilation fails.
Read references/144-java-data-oriented-programming.md and identify candidates for data/behavior separation and immutable transformations.
Implement selected improvements using records, pure transformation functions, flat structures, and explicit validation.
Run ./mvnw clean verify or mvn clean verify after applying improvements.
For detailed guidance, examples, and constraints, see references/144-java-data-oriented-programming.md.
Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
Comprehensive GitHub code review with AI-powered swarm coordination
Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
GitHub CLI (gh) comprehensive reference for repositories, issues, pull requests, Actions, projects, releases, gists, codespaces, organizations, extensions, and all GitHub operations from the command line.
GitHub CLI - manage repositories, issues, pull requests, actions, releases, and more from the command line.
You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.
You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create acti
Take jabrena/144-java-data-oriented-programming from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.