Skill UI

Use this guide to build and manage Expo development clients using EAS Build. This is the recommended setup for production-grade testing, allowing developers to test custom native code, local modules, and advanced features that exceed Expo Go's capabilities. It covers local builds, TestFlight submission, and connecting to the Metro bundler.

This comprehensive guide details the use of FFUF, a fast web fuzzer written in Go. It is designed for advanced security penetration testing, providing techniques for content discovery, subdomain enumeration, parameter fuzzing, and POST data testing. Learn to master the FUZZ keyword, advanced filtering, and auto-calibration to efficiently identify hidden paths and potential vulnerabilities in web applications.

Leverages Monte Carlo-based simulation to spot potential software vulnerabilities by feeding randomized inputs into modules, tracking failure likelihoods, and highlighting risky code paths for security testing.

This skill generates a complete, opinionated, modern full-stack Rails application boilerplate. It sets up a robust tech stack including Rails 8, PostgreSQL, React, Inertia.js, Vite, Tailwind CSS, Sidekiq, and Redis. Designed for developers who need a starting point that adheres to best practices for database structure (UUIDs, JSONB), background job processing, comprehensive testing, and modern frontend development (TSX). Ideal for bootstrapping complex, production-grade web services.

This advanced pipeline orchestrates complex feature development based on Request for Comments (RFCs). It breaks down monolithic changes into small, independently verifiable work units, managing dependencies through a Directed Acyclic Graph (DAG). It includes multiple quality gates, dedicated unit testing, and a safe merge queue to ensure robust integration and controlled deployment.

Offers testing guidance covering TDD workflows, unit/integration/E2E organization, framework support, mocking strategy, and coverage to help teams deliver reliable, well-structured test suites.

A scientific, customer-centric framework for systematically improving conversion rates on websites and landing pages. This methodology guides users through funnel mapping, identifying core visitor objections, and conducting evidence-based A/B testing. It is essential for auditing underperforming funnels, optimizing checkout flows, and transforming general marketing hypotheses into measurable, statistically significant improvements.

A comprehensive 5-day, structured process (Map, Sketch, Decide, Prototype, Test) for rapidly testing and validating product ideas using real users. Developed by Google Ventures, this framework helps teams de-risk major product decisions by moving beyond endless debate to actionable, validated insights in a single week. Ideal for startups and established teams facing critical business challenges.

A comprehensive set of architectural patterns for building robust and maintainable HTTP servers using Kotlin Coroutines and the Ktor framework. It covers essential topics including REST routing DSL, plugin configuration (Authentication, CORS, StatusPages), Dependency Injection (Koin), serialization, WebSockets, and integration testing using testApplication.

A comprehensive guide to writing reliable and maintainable tests for Kotlin projects. This skill covers modern testing patterns using Kotest for various spec styles (FunSpec, BehaviorSpec), MockK for dependency mocking, and enforces TDD principles (RED/GREEN/REFACTOR). Learn how to implement unit tests, property-based testing, and ensure high code coverage with Kover.

A comprehensive guide to using k6 for robust performance and load testing across APIs, WebSockets, and browser scenarios. Learn to write realistic load simulations (smoke, load, stress, soak), analyze performance metrics, set SLAs, and integrate testing into CI/CD pipelines to ensure system reliability before deployment.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.