Skill UI

This skill provides a comprehensive test quality review, drawing on established principles from classic engineering texts like xUnit Test Patterns and How Google Tests Software. It diagnoses structural flaws—such as brittleness, decay, and poor coverage—in existing test suites, helping ensure that the codebase and its tests are robust, maintainable, and adhere to high industry standards.

This advanced skill performs code review by simulating the documented engineering philosophies of historical figures (e.g., Linus Torvalds, Steve Jobs, Ken Thompson). It moves beyond generic AI findings by grounding critiques in specific, citable principles—such as 'good taste' or 'customer experience.' Ideal for high-stakes PRs, architectural reviews, and ensuring deep, pre-submission code hardening.

This skill automatically rewrites raw commit messages to adhere to professional engineering standards. It strips out common 'AI/marketing slop' phrases (like 'robust implementation' or 'leverage') while strictly maintaining the Conventional Commits format. It ensures subjects are concise, bodies provide necessary context (why, breaking changes), and the tone is direct and actionable, making your project history clean and readable.

A sophisticated and print-friendly presentation template designed for explaining complex system architectures and data pipelines. Featuring a blueprint grid, cream background, and rust red accents, this deck is ideal for technical deep dives in engineering and system design.

A specialized presentation deck template designed for internal engineering talks, technical conferences, and deep-dive knowledge sharing. It features a professional GitHub-dark theme, JetBrains Mono coding font, and a structured layout including Cover, Agenda, main content slides (with code blocks), demo slides (terminal screenshots), and Q&A sections.

The /goal command converts standard agent prompts into persistent, self-correcting loops (plan → act → test → review → iterate). It is designed for long-running, complex engineering tasks like migrations, coverage lifts, or refactoring, ensuring progress is tracked against a verifiable stop condition and mandated documentation.

This skill enforces highly efficient coding practices, guiding the AI to write the simplest, most direct code possible. It strictly follows an 'Efficiency Ladder' (YAGNI, reusing existing code, standard library, etc.) to prevent over-engineering, unnecessary abstractions, and boilerplate. Use this when the goal is maximum efficiency with minimal code.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

A comprehensive framework for creating structured, clean, and highly accessible design systems. It defines foundational constraints, typography scales, color tokens, and component-level rules, ensuring consistency and usability across engineering and design teams. Focuses on achieving WCAG 2.2 AA compliance and implementation readiness.

A comprehensive guide detailing advanced performance optimization patterns for React 18/19 and Next.js. Adapted from Vercel Engineering's best practices, this skill organizes 70+ critical rules across eight categories—including eliminating waterfalls, optimizing bundle size, improving server-side rendering, and managing client-side re-renders. Use this when developing, reviewing, or refactoring high-performance React applications.