Skill UI

This skill provides a comprehensive solution for embedding the powerful, Photoshop-compatible Photopea editor into any web application. Using photopea.js, developers can control the embedded instance via JavaScript, enabling advanced workflows such as opening remote files, loading local assets (fonts, brushes), executing custom scripts to manipulate layers and text, and exporting the final result in various formats (PNG, JPG, PSD, WebP). Ideal for building professional creative tools directly into your product.

The RunAPI CLI serves as the dedicated execution layer for interacting with RunAPI's suite of advanced AI models. It allows developers to programmatically generate multimodal content, including images, videos, and music/audio, directly from the terminal or CI/CD pipeline. Users can submit one-off jobs, manage asynchronous tasks, and pass complex JSON request bodies to various services like Suno, Claude, and Gemini, making it essential for robust AI automation workflows.

This meta-skill is designed exclusively to orchestrate and formalize complex workflows composed of multiple existing skills. It acts as a comprehensive governance layer, guiding the user through intent clarification, trigger collision checks, linting, and proposal persistence. Use it when proposing a multi-step, integrated solution, but do not use it for simple standalone skill requests.

This advanced tool surfaces critical, often unrecorded, evidence of human-AI collaboration during development sessions. It analyzes git diffs and conversational context across multiple layers to classify the signal level (HIGH/MEDIUM/LOW), measure technical complexity, detect decision forks, and quantify the specific contribution of the AI model, providing structured data for post-session retrospectives and accountability.

Alex converts high-level requirements into a precise, dependency-aware, and actionable implementation checklist. It maps critical paths, breaks down features into atomic micro-tasks, and establishes clear Definitions of Done (DoD) across various layers. Ideal for bridging the gap between conceptual goals and step-by-step engineering execution.

Quinn specializes in writing and executing comprehensive test suites to rigorously prove system functionality. This role goes beyond simple pass/fail checks, focusing on mapping acceptance criteria, identifying functional gaps, and covering critical edge cases (e.g., nulls, boundary values, error paths). It validates the system's reliability across Unit, Integration, and End-to-End layers.

This skill operates on two layers: the THINK layer enforces rigorous coding discipline (simplicity, surgical changes, goal-driven execution), and the SPEAK layer compresses prose output (caveman/classical modes) for maximum token efficiency. Use it when brevity is explicitly requested or when disciplined, high-precision code generation is required.

This comprehensive guide provides technical implementations for achieving a modern 'Layered Design' aesthetic. It teaches developers how to create depth, intentional overlapping, and clear stratification across various platforms. Includes detailed code examples for CSS, SwiftUI, Flutter, and React Native to build complex, dimensional user interfaces.

Use @expo/ui to build high-fidelity, native user interfaces across iOS and Android from a single React codebase. This library renders real native UI components, such as SwiftUI on iOS and Jetpack Compose on Android. It promotes a universal component layer for cross-platform consistency, while also providing controlled access to platform-specific optimizations and drop-in replacements for existing community libraries.

A comprehensive discipline for managing the write path in React or React Native applications using a query/cache layer. It codifies the full optimistic update lifecycle, ensuring that UI updates feel instantaneous, rollbacks are verbatim upon failure, and the global cache remains perfectly coherent across all components and list views. This pattern is essential for complex, highly interactive user experiences.

This guide details best practices for implementing robust, type-safe GraphQL queries and mutations. It covers using separate .gql files, running code generation tools, and implementing mandatory error handling (onError) and loading states. Use this when building complex, reliable data fetching layers with Apollo Client.

This meta-skill acts as a routing layer for complex UI development tasks. It guides the process of selecting the smallest, most specific, and useful skill context using the ui-skills CLI. Use it when starting UI work to analyze goals, identify the correct category, and pinpoint the precise skill set needed for implementation, ensuring development efficiency and accuracy.