Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jabrena/plinth --skill 145-java-refactoring-high-performancegit clone --depth 1 https://github.com/jabrena/plinthWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jabrena/plinth/145-java-refactoring-high-performance)<a href="https://agentmods.dev/skills/jabrena/plinth/145-java-refactoring-high-performance"><img src="https://agentmods.dev/badge/skills/jabrena/plinth/145-java-refactoring-high-performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jabrena/plinth/145-java-refactoring-high-performance"><img src="https://agentmods.dev/badge/skills/jabrena/plinth/145-java-refactoring-high-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00073 | $0.00743 |
| Opus 5 | $0.00036 | $0.00371 |
| Sonnet 5 | $0.00015 | $0.00149 |
| Haiku 4.5 | $0.00007 | $0.00074 |
Grade A, and why
145-java-refactoring-high-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- 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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 68 lines · 73 tokens per session scan A 7926b858d6cc
145-java-refactoring-high-performance is a skill published in the GitHub repository jabrena/plinth (438 stars, last pushed yesterday), licensed Apache-2.0. It adds 73 tokens to every session and 743 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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