145-java-refactoring-high-performance

Included with Lifetime

$97 forever

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 cursor-rules-java project

Backend & APIs

What this skill does

# 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.

2. **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.

3. **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).

4. **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)

Files: 4

Size: 66.4 KB

Complexity: 52/100

Category: Backend & APIs

Source: https://github.com/jabrena/cursor-rules-java/tree/main/skills/145-java-refactoring-high-performance

Related in Backend & APIs

jfrog

Included

Interact with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.

Backend & APIsscripts

cupynumeric-migration-readiness

Included

Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.

Backend & APIsscripts

alibabacloud-data-agent-skill

Included

Invoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".

Backend & APIsscripts

token-optimizer

Included

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.

Backend & APIsscripts

resend-cli

Included

Use this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.

Backend & APIsscripts

alibabacloud-odps-maxframe-coding

Included

Use this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.

Backend & APIsscripts