145 Java Refactoring High Performance Agent Skill
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
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/jabrena/plinth --skill 145-java-refactoring-high-performanceThe instruction itself
5 sections, as written by the authorJava 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
- Identify Java hotspot and baseline behavior
Confirm the performance-sensitive Java path and baseline behavior before changing code.
- 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.
- 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).
- 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-cpu.md
- references/145-refactoring-high-performance-java-code-syntax.md
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How to use it
Take jabrena/145-java-refactoring-high-performance 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.