Skill UI

Configures the Guidewire Studio local development environment, enabling efficient development, debugging, and testing. Features include Gosu debugging with breakpoints, hot deployment for rapid Gosu changes, GUnit integration for unit testing, and local test data loading via sample data loader. Ideal for simulating and developing the InsuranceSuite locally using 'gradle runServer'.

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).

This skill acts as an expert architect for scaffolding robust, production-ready applications using TypeScript. It generates complete, structured project templates for various types, including Next.js full-stack apps, React/Vite SPAs, and Node.js APIs. It incorporates modern tooling like pnpm, Vite, and Next.js features, ensuring type safety, proper dependency management, comprehensive testing setups, and adherence to current industry best practices. Use it to accelerate project setup and maintain scalable architecture.

This comprehensive guide helps developers safely migrate between different versions of the Kling AI video generation model (e.g., v1.x to v2.x). It details breaking changes, parameter differences, feature availability matrices (including native audio and camera control), and provides code examples for parallel A/B testing and robust rollback strategies, ensuring seamless API adoption.

Automate the validation and testing of prompts and AI responses within your CI/CD pipeline using Mistral AI. This integration provides robust prompt regression testing, model quality checks, automatic cost estimation for API usage, and deployment gates, ensuring the stability and reliability of your AI features upon every pull request.

A comprehensive skill set for building, deploying, and managing production-grade Machine Learning systems. Expertise spans modern frameworks (PyTorch, TensorFlow), model serving using Kubernetes, advanced feature engineering (Feast, Tecton), and robust MLOps practices (monitoring, A/B testing, CI/CD). Focuses on delivering scalable, reliable, and business-value-driven AI solutions.

This skill provides comprehensive guidance for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, from initial data ingestion and rigorous feature engineering to model training, advanced validation (A/B testing, performance drift), and robust deployment strategies (Canary, Blue-green). It enables the design and orchestration of scalable, reproducible, and observable ML systems using tools like Airflow and Kubeflow.

Automatically generates robust, schema-compliant mock API servers directly from OpenAPI specifications. This tool supports advanced testing features, including stateful CRUD operations, Faker-driven dynamic data generation, configurable latency simulation, and detailed request recording for contract testing. Ideal for frontend parallel development and isolated integration testing.

This skill provides a comprehensive guide to setting up isolated development, testing, and production vaults within Obsidian. It covers advanced techniques like symlinking plugin sources for hot reloading, implementing environment-specific configuration logic (e.g., managing debug modes and feature flags), and creating standardized vault templates for team onboarding, ensuring a robust and scalable development lifecycle.

This skill automates the entire software development process overnight. By leveraging mandatory Git hooks, it strictly enforces Test-Driven Development (TDD). Use this when complex features need building, large codebases need refactoring, or bugs need fixing autonomously, guaranteeing that every commit passes rigorous testing cycles and meets high production-ready standards.

This skill enables the coordination of multiple specialized AI agents within a single session. It is designed for complex, multi-domain tasks such as comprehensive code reviews, security audits, or full-stack feature implementations. It coordinates expertise across various domains (backend, frontend, testing, architecture) to provide holistic analysis and actionable recommendations.

Runs automated visual regression tests via Percy, Chromatic, or BackstopJS to capture component or page screenshots, compare them against baselines, and flag unintended UI changes during feature updates or CI/CD runs.