Skill UI

A collection of production-ready GitHub Actions workflow patterns designed for automating the entire software development lifecycle (SDLC). Use these templates to streamline continuous integration (CI), build Docker images, deploy to Kubernetes clusters, perform security scans, and execute matrix testing across various tech stacks.

A specialized AI assistant acting as an expert Haskell engineer. This skill focuses on advanced type systems (GADTs, type families), pure functional architecture, concurrency (STM, async), and high-assurance software design. Use it for building robust, idiomatic, and maintainable Haskell code.

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.

This skill provides a comprehensive pattern for building robust, scalable, and fault-tolerant asynchronous video generation pipelines using Kling AI. It implements advanced software engineering patterns, including Redis queues (Producer/Consumer), State Machines, and event-driven callbacks. It is designed for managing complex, multi-step AI workflows—such as generate, extend, and download—ensuring reliable background task processing.

This elite workflow simulates the comprehensive process of a Senior Software Engineer and Product Designer. It enforces a rigorous development pipeline: starting with planning, solid architecture, and TDD implementation, through clean code practices, and technical auditing. Crucially, it includes mandatory, pixel-perfect UX visual validation to ensure the output is flawless, consistent, and production-grade.

This advanced workflow emulates an elite Senior Software Engineer and Senior Product Designer, ensuring maximum quality output. It enforces a rigorous, multi-step process including ultra-fast planning, solid architecture design, TDD implementation, clean code refactoring, technical auditing, and crucially, mandatory pixel-perfect UI/UX validation. Use this for complex projects demanding both robust code and flawless visual presentation.

Expert skill for writing, reviewing, and debugging idiomatic Rust code, focusing on memory safety and zero-cost abstractions. Specializes in advanced concepts like ownership patterns, explicit lifetime management, trait-based API design, and building concurrent async applications using Tokio. Use this when developing high-performance, reliable, systems-level software in Rust.

This skill provides comprehensive guidelines for architecting and developing high-quality, maintainable software. It is deeply rooted in Clean Architecture and Domain-Driven Design (DDD) principles. It covers best practices for code decomposition, naming conventions, separation of concerns, and avoiding common anti-patterns. Use this skill when designing, analyzing, or developing complex, domain-specific software systems.

A detailed, multi-phase workflow that strictly enforces the Test-Driven Development (TDD) discipline using the Red-Green-Refactor cycle. This guide covers the entire software development lifecycle, from test specification and architecture design to minimal implementation, rigorous refactoring, and comprehensive integration testing. It mandates high code and test coverage (e.g., 80% line coverage) to ensure robust, maintainable, and high-quality production code.

A specialized skill designed to ensure robust software quality by covering the entire testing lifecycle. This includes defining test strategies, writing unit, integration, and E2E tests, and implementing advanced testing types like performance (k6), security (OWASP), and functional testing. It guides users through coverage analysis, debugging flaky tests, and generating detailed defect and quality reports.

A comprehensive skill for structured software development life cycle (SDLC). It guides users through adaptive phases—Specify, Design, Tasks, and Execute—ensuring that every feature is built with traceable requirements, atomic tasks, and rigorous verification. Features include an independent Verifier, persistent decision logging, and a self-improving lessons layer to enhance future development quality. Ideal for complex, multi-component feature implementation.

This skill simulates an expert software engineer and systems analyst. It is designed for deep, evidence-based understanding of complex codebases. Users can ask "how does X work" or request architectural reviews, ensuring every claim is grounded in actual code paths, data flows, and dependency chains, following a rigorous multi-iteration process.