Skill UI

This meta-skill is designed to guide and execute structured developmental workflows, enabling continuous skill improvement and knowledge transfer. It is applicable when a task requires following a defined developmental process. Users must ensure all necessary inputs, permissions, and success criteria are provided, and must treat the output as a guide, supplementing professional validation.

This skill is designed to execute complex, structured workflows and actions based on a defined overview. It facilitates systematic process completion and adherence to scope. Users must ensure the task precisely matches the skill's scope, and remember that the output is not a substitute for manual validation or expert review.

This skill guide is essential for building advanced applications using Gemini and Gemma 4 hosted models. It covers working with multimodal content (text, images, audio, video), implementing complex features like function calling and structured output. It provides up-to-date SDK usage examples for Python, JavaScript, Go, and Java, ensuring developers use the latest, recommended API standards.

This skill automatically gathers and structures comprehensive context for Apple product development. It analyzes existing project files (like README, Info.plist, and code) and prompts the user for details across product overview, target platforms, technology stack, design system, and accessibility requirements. The output is a structured context document used by other specialized skills to ensure consistent, informed design and development decisions.

Master the art of building robust, production-grade LLM chains using LangChain Expression Language (LCEL). This guide covers advanced composition patterns, including sequential, parallel, and conditional branching chains. It also details prompt templating, structured output parsing (JSON/Zod), and context injection, enabling the creation of complex, multi-step AI pipelines.

This guide provides minimal working examples demonstrating the LangChain Expression Language (LCEL). It shows how to chain together diverse components—such as prompts, LLMs, and output parsers—using simple pipe syntax (e.g., .pipe() or |). Ideal for developers starting a new LangChain project, understanding core AI workflow patterns, or mastering structured data output.

This comprehensive guide outlines essential, production-grade patterns for building robust LangChain applications. Learn best practices for implementing type-safe structured output using Zod, handling provider fallbacks, processing inputs concurrently with batching, optimizing user experience with streaming, and ensuring reliability through caching and retries.

This comprehensive workflow guide covers all production-ready patterns for Mistral AI chat completions. It demonstrates critical advanced features including multi-turn conversation management, real-time streaming responses, structured JSON output using schemas, and implementing robust guardrails and moderation. Ideal for building scalable, production-grade conversational AI applications and advanced chat interfaces.

This guide provides production-ready patterns for integrating the Mistral AI SDK in both TypeScript and Python. It covers essential best practices such as singleton client management, handling structured JSON output, implementing streaming responses, concurrent asynchronous processing, and robust error handling with exponential backoff. Use this when establishing team coding standards or refactoring Mistral API usage.

This skill specializes in creating exhaustive, searchable, and highly structured technical references. It generates comprehensive API documentation, detailed parameter listings, configuration guides, and schema definitions, ensuring the output serves as the definitive source of truth for any technical system.

A specialized reverse-engineering skill designed to extract comprehensive functional and non-functional specifications from existing, undocumented, or legacy codebases. It operates by analyzing code structure, tracing data flows, and identifying observable behaviors. The output is structured using EARS (Easy Approach to Requirements Syntax), providing clearer requirements documentation, architectural diagrams, and dependency maps for onboarding or system modernization.

This skill provides opinionated and evolving constraints designed to guide AI agents effectively when building user interfaces. It offers structured patterns and guidelines, ensuring that the generated interfaces adhere to specific best practices and development rules. Use this skill when your task requires highly structured, guided output for UI development, moving beyond simple generation to guided design.