Skill UI

This comprehensive guide outlines established best practices for developing robust, scalable, and high-quality enterprise applications using Spring Boot and Java. It covers structuring projects, implementing dependency injection, designing RESTful APIs, managing transactions, securing endpoints, and utilizing advanced testing strategies (Unit, Integration). Ideal for developers aiming for industry-standard code quality.

This comprehensive guide outlines best practices for writing robust JavaScript and TypeScript unit tests using the Jest framework. It covers essential topics including proper test structure, effective dependency mocking strategies (using jest.mock and jest.spyOn), handling asynchronous code, implementing snapshot testing, and modern techniques for testing React components with React Testing Library. It also provides a detailed reference of common Jest matchers.

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.

Generates customizable prompts to scan a codebase, identify high-quality exemplars, and document patterns across languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable depth, categorization, and documentation formats.

This comprehensive guide provides a complete, step-by-step workflow for setting up a modern Spring Boot Java project from scratch. It covers downloading the project template, configuring multiple data sources (PostgreSQL, Redis, MongoDB), integrating API documentation (SpringDoc), and orchestrating a full, containerized service environment using Docker Compose. Ideal for complex, enterprise-level application development.

A comprehensive skill guide for building production-grade, scroll-driven motion animations. It covers advanced techniques like parallax effects, sticky sections, horizontal scrolling, and staggered reveals using both the GSAP ScrollTrigger API and Framer Motion hooks. Ideal for implementing complex, performant animations in vanilla JavaScript, React, and Next.js.

Guides adding GraalVM native image support to Java apps by detecting Maven or Gradle frameworks, injecting native build plugins, configuring reflection/resource/JNI/proxy files, iterating builds per Oracle best practices, and validating the native executable.

Shows how to apply the Extract Method refactoring in Java to improve readability, modularity, and testability by isolating reusable behaviors from complex methods while preserving the original functionality.

This skill focuses on the 'Remove Parameter' refactoring pattern specific to Java development. It teaches systematic techniques to analyze methods and identify unused or redundant parameters. By applying this refactoring, developers can significantly improve code readability, enhance maintainability, reduce coupling, and simplify method signatures while preserving original functionality and logic.

Guides how to write JavaScript inside n8n Code nodes, choose execution modes, access data with $input/$node, handle Webhook payloads, and return the required [{json:…}] structure for automation tasks.

A comprehensive guide for writing and managing Python code specifically within n8n Code nodes. Learn how to perform complex data transformations, handle different execution modes (batch vs. item-by-item), and master various data access patterns (_input.all(), _input.item, _node). Note the critical limitations, such as relying only on the standard Python library and the general recommendation to use JavaScript for most n8n use cases.