Skill UI

This skill provides comprehensive guidance on optimizing Guidewire application performance. It covers advanced development techniques such as optimizing Gosu queries using the Query API, implementing efficient batch processing with BatchProcessBase, implementing caching strategies for frequently accessed reference data, and performing JVM heap tuning via the Guidewire Cloud Console (GCC). Use this when system performance bottlenecks are detected in Guidewire insurance or core business platforms.

This skill automatically gathers and structures comprehensive context for Apple product development. It analyzes existing project files (like README, Info.plist, and code) and prompts the user for details across product overview, target platforms, technology stack, design system, and accessibility requirements. The output is a structured context document used by other specialized skills to ensure consistent, informed design and development decisions.

This skill provides a complete local development loop for Ideogram integrations. It includes a strongly typed client wrapper, a mock server for offline testing (preventing API credit usage), and comprehensive Vitest unit tests. Ideal for setting up robust, fast-iterating development environments and ensuring API reliability.

A comprehensive guide for building professional, high-performance native iOS applications. Covers modern frameworks like SwiftUI and Swift 6, alongside deep expertise in UIKit, Core Data, networking (REST/GraphQL), performance profiling, and full App Store lifecycle management. Ideal for developers needing guidance on modern Apple ecosystem best practices.

An expert guide for building scalable, cloud-native enterprise applications using the latest Java 21+ features, including Virtual Threads and Pattern Matching. Deep mastery of the Spring Boot 3.x ecosystem, Spring Cloud, and microservices architecture. Covers full development lifecycle, from JVM performance tuning (GraalVM, ZGC) to robust testing (Testcontainers) and secure DevOps practices (Kubernetes, CI/CD).

A comprehensive guide and expert resource for mastering Julia 1.10+ development. Covers advanced topics including multiple dispatch, performance optimization, type stability, and modern tooling. Ideal for building production-ready applications in scientific computing, machine learning, data science, and web development.

A comprehensive guide applying Kaizen (continuous improvement) and Poka-Yoke (error proofing) principles to software development. Use this skill when you need to enhance code quality, refactor large systems incrementally, validate designs, or optimize development processes by focusing on prevention rather than fixing bugs.

Provides deep expertise in modern Kotlin patterns, covering structured concurrency via Coroutines and Flow, Multiplatform architecture (KMP), declarative UI using Compose, and server implementation with Ktor. Ideal for building robust, type-safe, and highly maintainable applications across various platforms.

This skill provides a comprehensive guide for deploying LangChain chains and agents into production-grade APIs. It supports multiple development stacks, including Python (LangServe/FastAPI) and Node.js (Express). Topics covered include containerization using Docker, setting up robust health checks, and deploying to major cloud platforms like Google Cloud Run and AWS Lambda. Learn how to build scalable, observable, and enterprise-grade API endpoints for advanced LLM applications.

A comprehensive guide to configuring a structured, local development environment for LangChain applications. It covers setting up project architecture, managing dev dependencies, and implementing a two-tiered testing strategy: isolated unit tests using mocked LLMs (for fast, API-free testing) and robust integration tests using real API providers. Ideal for establishing a reliable, rapid iteration workflow.

This guide details best practices for configuring LangChain applications across development, staging, and production environments. It ensures strict environment isolation by managing separate API keys, model settings, and LangSmith project names. The setup utilizes Zod for robust validation and integrates secure secret management via GitHub Actions, AWS Secrets Manager, or GCP, guaranteeing reliable and secure deployments.

This guide provides a comprehensive local development setup for LLM applications using Langfuse. It enables fast iteration cycles, real-time trace debugging, and hot reloading, making it ideal for developers building and testing complex AI workflows locally. Instructions cover environment setup, advanced SDK usage, and optional self-hosted Docker deployment.