Skill UI

This advanced skill performs a full-sweep analysis, providing a unified assessment of code quality across multiple dimensions: code decay, architecture integrity, technical debt, and test coverage. It automatically applies safe fixes directly to the codebase, while requiring confirmation for risky changes. It is ideal for comprehensive pre-release code health audits and automated refactoring.

This skill provides a deep framework for validating the spatial composition and technical integrity of a UI. It moves beyond simple visual checks, emphasizing that the output must be rendered and critically assessed at various viewport sizes. Key focus areas include detecting subtle design flaws like overlaps and imbalances, and most critically, implementing mandatory horizontal overflow checks (`scrollWidth`) to ensure the layout remains flawless across all device widths.

This skill guides the implementation of advanced SEO strategies tailored specifically for technical audiences and developers. It covers specialized techniques such as optimizing for error messages, researching high-intent technical long-tail keywords, creating structured how-to guides, and developing objective tool comparison content. Use this when targeting developers who search with precise technical intent, needing solutions that go beyond standard documentation.

This skill helps developers achieve maximum AI readiness by automatically detecting the project stack and seeding context using a single, precise goal sentence. It systematically fills the essential project context (the 6 Ws), requiring the user only to answer the few remaining knowledge gaps (like the 'why' or 'when'). This creates a portable, structured context file, ensuring AI models have a complete understanding of the project before coding, minimizing guesswork and maximizing efficiency.

This skill guides users through an in-depth interview process to achieve maximum project context completeness (100% "Gold Code") for their .faf file. By systematically asking questions about project goals, architecture, and technology stack, it ensures all critical context slots are filled, making the project fully structured and highly readable by AI systems.

This discipline establishes a portable, framework-agnostic layer for ensuring type safety at the network edge of any React or React Native application. It mandates a single, typed API client to wrap all network fetching, guaranteeing that raw wire JSON is immediately parsed into trusted, domain-specific types or standardized error objects before entering the application state. This pattern drastically reduces runtime errors and enhances code robustness.

A framework-agnostic, builder-based SEO system designed for React and React Native-for-web applications. It centralizes site identity (title, description, OG tags) in a single source of truth, ensuring all metadata, canonical URLs, and structured data (JSON-LD, sitemap, robots.txt, RSS) are consistent, machine-readable, and automatically generated for every route.

Use this comprehensive skill for interacting with the Gemini API. It supports a wide range of advanced AI tasks, including text generation, multi-turn chat, multimodal understanding, image/video creation, function calling, and structured output. Provides modern SDKs for robust application development in Python and JavaScript/TypeScript.

This skill guides developers on building real-time, bidirectional, and multimodal streaming applications using the Gemini Live API. It covers low-latency voice, video, and text streaming over WebSockets, incorporating advanced features like Voice Activity Detection (VAD), function calling, and secure session management. Ideal for conversational AI, interactive video conferencing, and complex real-time user experiences.

This skill provides an autonomous, repository-wide pipeline for auditing and fixing complex logical inconsistencies. It systematically executes stages including health checks, deep review, locating divergences, generating fixes, and performing diff-verification until the codebase is deemed clean. Ideal for maintaining high code quality and resolving deep, systemic bugs across entire projects.

This skill is designed to identify subtle and complex logic bugs within a single code file or function. It employs a semi-formal execution tracing methodology, systematically breaking down the suspected bug into five stages: Premises (expected state), Trace (execution path), Divergence (actual deviation), Trigger (reproduction case), and Remedy (fix). It is ideal for scenarios where users suspect code is wrong but cannot specify a concrete failure point.

Neon Functions provide long-running, stateful, serverless HTTP handlers deployed directly next to your Neon database branch. They are ideal for complex backend workloads that exceed traditional serverless limits, such as real-time WebSockets, Server-Sent Events (SSE) streaming, or sophisticated AI agents. Functions automatically inject the database connection URL and maintain state within the function scope, ensuring high performance and data proximity.