SkillsAdd

jabrena/plinth

145-java-refactoring-high-performance

Use when you need to refactor Java code for high performance — including memory/allocation reduction, CPU hot-path optimization, and syntax/API/control-flow improvements.

First seen May 7, 2026

Installation

$ npx skills add jabrena/plinth --skill 145-java-refactoring-high-performance
View on skills.sh ↗ GitHub repository ↗

Summary

  • Use when you need to refactor Java code for high performance — including memory/allocation reduction, CPU hot-path optimization, and syntax/API/control-flow improvements.
  • This should trigger for requests such as Review Java code for high performance; Optimize Java hot path; Reduce Java allocations; Improve Java latency/throughput.
  • Part of Plinth Toolkit

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 437
License LICENSE
Default branch main
Open issues 22
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Version0.18.0
LicenseApache-2.0
More metadata
author
Juan Antonio Breña Moral
version
0.18.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,751 B
  • docs SUMMARY.md 399 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 222 installs

SKILL.md

Java rules for High Performance

Identify and apply practical Java high-performance techniques using a measure-first approach, with emphasis on allocation reduction, data layout, concurrency discipline, and evidence-based validation.

What is covered in this Skill?

  • Measure-first workflow for Java code optimization
  • JVM/runtime-aware coding guidance
  • Allocation reduction techniques with bad/good patterns
  • CPU hot-path simplification and loop-level efficiency patterns
  • Concurrency/backpressure and timeout/cancellation discipline
  • I/O, parsing, and serialization efficiency patterns
  • Persistence/query and caching strategy guidance
  • Java-centric decision workflow: keep/revert based on measured impact

Scope: Practical optimization in application code and APIs. Apply only where profiling indicates real bottlenecks.

Constraints

Performance optimization must be evidence-driven and safe, focused on Java code changes that preserve correctness and maintainability.

  • MEASURE-FIRST: Establish baseline behavior and identify Java code hot paths before optimization
  • NO PREMATURE OPTIMIZATION: Only optimize code paths identified by profiling evidence
  • BEFORE APPLYING: Read the relevant reference(s) for bad/good examples and measurement workflow
  • EDGE CASE: If hotspot evidence is unclear, ask clarifying questions before changing code

When to use this skill

  • Review Java code for high performance
  • Optimize Java hot path
  • Reduce Java allocations
  • Improve Java latency
  • Improve Java throughput

Workflow

  1. Identify Java hotspot and baseline behavior

Confirm the performance-sensitive Java path and baseline behavior before changing code.

  1. Select the relevant reference(s) by bottleneck

Pick and read only the reference(s) matching the observed hotspot: references/145-refactoring-high-performance-java-memory-allocation.md for allocation pressure, primitives vs. wrappers, escape analysis, collection sizing, data layout, and deduplication; references/145-refactoring-high-performance-java-cpu.md for CPU-bound hot paths, bit-level parsing, branchless arithmetic, loop unrolling, Unsafe caution, and SIMD/vectorization; references/145-refactoring-high-performance-java-code-syntax.md for code shape, lambdas, API return conventions, parsing syntax, I/O strategy, concurrency, and control-flow improvements.

  1. Apply targeted optimizations

Implement minimal, evidence-backed changes scoped to the chosen domain(s): memory/allocation, CPU/low-level, or code shape/control flow (and adjacent concurrency, I/O, and persistence/caching in Java code).

  1. Validate and compare code-level outcomes

Compare before/after behavior and keep only Java code changes with meaningful, verified gains.

Reference

For detailed guidance, examples, and constraints, see:

  • [references/145-refactoring-high-performance-java-memory-allocation.md](references/145-refactoring-high-performance-java-memory-allocation.md)
  • [references/145-refactoring-high-performance-java-cpu.md](references/145-refactoring-high-performance-java-cpu.md)
  • [references/145-refactoring-high-performance-java-code-syntax.md](references/145-refactoring-high-performance-java-code-syntax.md)