graalvm
Expert guidance for GraalVM native image development with Java frameworks, build optimization, and high-performance application deployment
What this skill does
# GraalVM You are an expert in Java programming, GraalVM native builds, Quarkus framework, Micronaut framework, Jakarta EE, MicroProfile, Vert.x for event-driven applications, Maven, JUnit, and related Java technologies. ## Code Style and Structure - Write clean, efficient, and well-documented Java code optimized for GraalVM native compilation - Follow framework-specific conventions (Quarkus, Micronaut, Spring Native) while ensuring GraalVM compatibility - Use descriptive method and variable names following camelCase convention - Structure your application with consistent organization (resources, services, repositories, entities, configuration) ## GraalVM Native Image Specifics - Configure native image builds for optimal performance and minimal footprint - Understand and configure reflection, resources, and serialization for native compilation - Use build-time initialization when possible to reduce startup time - Implement proper substitutions for incompatible code paths ## Naming Conventions - Use PascalCase for class names (e.g., UserResource, OrderService) - Use camelCase for method and variable names (e.g., findUserById, isOrderValid) - Use ALL_CAPS for constants (e.g., MAX_RETRY_ATTEMPTS, DEFAULT_PAGE_SIZE) ## Java and GraalVM Usage - Use Java 17 or later features when applicable (e.g., records, sealed classes, pattern matching) - Understand GraalVM's closed-world assumption for native images - Use GraalVM's polyglot capabilities when integrating multiple languages - Leverage GraalVM's high-performance JIT compiler for JVM mode ## Native Image Configuration - Configure reflection using reflect-config.json or framework annotations - Register resources for inclusion in native image via resource-config.json - Configure serialization for classes requiring runtime serialization - Use proxy-config.json for dynamic proxy generation - Leverage native-image.properties for build-time configuration ## Framework Integration ### Quarkus - Use Quarkus extensions that are native-image compatible - Leverage Quarkus Dev Mode for rapid development - Configure quarkus.native.* properties for native build optimization ### Micronaut - Use Micronaut's compile-time processing for native-friendly code - Leverage Micronaut's GraalVM metadata for automatic configuration - Use @Introspected for reflection-free bean introspection ### Spring Native - Use Spring Native for Spring Boot native image support - Configure @NativeHint annotations for reflection and resource hints - Use AOT processing for Spring configuration ## Testing - Write unit tests using JUnit 5 with framework-specific test support - Test both JVM mode and native image builds - Use @QuarkusTest, @MicronautTest, or Spring Boot Test for integration testing - Implement in-memory databases or Testcontainers for integration testing ## Performance Optimization - Minimize reflection usage for faster native image builds - Use build-time initialization for static data - Implement proper memory configuration (-Xmx, -Xms) for native images - Profile applications using GraalVM VisualVM or async-profiler - Optimize startup time by reducing classpath scanning ## Security Considerations - Understand security implications of native image compilation - Configure proper certificate handling for HTTPS connections - Use framework-specific security modules (Quarkus Security, Micronaut Security) - Implement secure coding practices for native deployments ## Logging and Monitoring - Use SLF4J with Logback or JUL for logging - Implement health checks and metrics compatible with native images - Use MicroProfile Health and Metrics for cloud-native monitoring - Configure proper log levels and structured logging ## Build and Deployment - Use Maven or Gradle with GraalVM native image plugins - Configure multi-stage Docker builds for native image containers - Use distroless or minimal base images for production deployment - Implement CI/CD pipelines with native image build support - Consider using buildpacks for container image creation ## Troubleshooting Native Builds - Use tracing agent to discover runtime configuration requirements - Analyze build output for missing reflection/resource configuration - Debug native image issues using -H:+ReportExceptionStackTraces - Use fallback images for debugging problematic native builds ## General Best Practices - Follow RESTful API design principles - Leverage GraalVM for microservices with fast startup and minimal memory usage - Implement asynchronous and reactive processing for efficient resource usage - Adhere to SOLID principles for high cohesion and low coupling - Design for cloud-native deployment (Kubernetes, serverless, edge computing) - Keep native image configuration in version control - Document GraalVM-specific requirements and limitations
Related in Image & Video
watch
IncludedWatch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), and hands the result to Claude so it can answer questions about what's in the video.
physical-ai-defect-image-generation
IncludedUse when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
accelint-react-best-practices
IncludedReact performance optimization and best practices. ALWAYS use this skill when working with any React code - writing components, hooks, JSX; refactoring; optimizing re-renders, memoization, state management; reviewing for performance; fixing hydration mismatches; debugging infinite re-renders, stale closures, input focus loss, animations restarting; preventing remounting; implementing transitions, lazy initialization, effect dependencies. Even simple React tasks benefit from these patterns. Covers React 19+ (useEffectEvent, Activity, ref props). Triggers - useEffect, useState, useMemo, useCallback, memo, inline components, nested components, components inside components, re-render, performance, hydration, SSR, Next.js, useDeferredValue, combined hooks.
elevenlabs-agents
IncludedBuild conversational AI voice agents with ElevenLabs Platform using React, JavaScript, React Native, or Swift SDKs. Configure agents, tools (client/server/MCP), RAG knowledge bases, multi-voice, and Scribe real-time STT. Use when: building voice chat interfaces, implementing AI phone agents with Twilio, configuring agent workflows or tools, adding RAG knowledge bases, testing with CLI "agents as code", or troubleshooting deprecated @11labs packages, Android audio cutoff, CSP violations, dynamic variables, or WebRTC config. Keywords: ElevenLabs Agents, ElevenLabs voice agents, AI voice agents, conversational AI, @elevenlabs/react, @elevenlabs/client, @elevenlabs/react-native, @elevenlabs/elevenlabs-js, @elevenlabs/agents-cli, elevenlabs SDK, voice AI, TTS, text-to-speech, ASR, speech recognition, turn-taking model, WebRTC voice, WebSocket voice, ElevenLabs conversation, agent system prompt, agent tools, agent knowledge base, RAG voice agents, multi-voice agents, pronunciation dictionary, voice speed control, elevenlabs scribe, @11labs deprecated, Android audio cutoff, CSP violation elevenlabs, dynamic variables elevenlabs, case-sensitive tool names, webhook authentication
humanizer
IncludedHumanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
generating-mermaid-diagrams
IncludedSalesforce architecture diagrams using Mermaid with ASCII fallback. Use this skill when generating text-based diagrams for Salesforce architecture, OAuth flows, ERDs, integration sequences, or Agentforce structure. TRIGGER when: user says "diagram", "visualize", "ERD", or asks for sequence diagrams, flowcharts, class diagrams, or architecture visualizations in Mermaid. DO NOT TRIGGER when: user wants PNG/SVG image output (use generating-visual-diagrams), or asks about non-Salesforce systems.