Skill UI

This skill guides optimizing the entire developer onboarding journey to minimize Time-to-First-API-Call (TTFAC). It covers critical strategies for simplifying authentication, implementing effective sandbox environments, structuring interactive documentation, and identifying and eliminating common friction points to ensure developers can achieve their first successful API response quickly and easily.

This skill generates complete, production-grade client SDKs and API wrapper libraries for any REST API, regardless of the language (Python, TypeScript, etc.). It handles boilerplate code like authentication, retry logic (exponential backoff), data modeling, and structured resource management, saving significant development time. Use it whenever you need to consume a REST API via code.

A guide to designing friction-free developer onboarding experiences. Learn best practices for implementing GitHub OAuth, generating immediate API keys, and utilizing progressive profiling to maximize signup conversion rates while respecting the developer's time. Focuses on moving users from zero to coding as fast as possible.

A comprehensive resource containing official, managed integration examples for popular third-party services (Stripe, OpenAI, Supabase, etc.). This library provides canonical patterns for connecting external APIs into an Expo application. Users can utilize it in two primary ways: adapting the pattern into an existing project (most common) or scaffolding a new greenfield application.

A comprehensive guide for developers on creating and writing native modules and views using the Expo Modules API. It covers platform-specific implementation details for Swift (iOS), Kotlin (Android), and TypeScript. Use this when integrating system APIs, wrapping platform SDKs, or building complex native functionality that goes beyond standard React Native components.

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.

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.

Use this powerful skill to perform advanced, multi-modal video generation and editing using the official google-genai SDK. Capabilities include generating realistic videos from text prompts (Text-to-Video), animating scenes from a single input image (First-Frame-to-Video), and applying style or character references from images. It also supports complex video refinement, inpainting, and optimizing source footage for professional creative workflows.

A comprehensive guide and skill for deploying large language models (LLMs) locally using the llama.cpp framework and GGUF quantization format. It covers searching the Hugging Face Hub, selecting optimal quantizations (e.g., Q4_K_M), running local servers, and achieving OpenAI-compatible API endpoints. Supports various hardware backends including CPU, Mac Metal, CUDA, and ROCm. Ideal for developers needing offline, private LLM inference.

A comprehensive utility for building reusable, command-line scripts and utilities to interact with the Hugging Face API. It enables complex data workflows by allowing users to chain, pipe, and process data fetched from models, datasets, and various endpoints. Ideal for automation, enrichment, and advanced data pipelines.

This skill guides users through the complete lifecycle of data quality incident management using Monte Carlo MCP tools. It performs detailed investigation, including root cause analysis and blast radius assessment. It then coordinates various external tools (APIs, CLI) to propose and execute appropriate remediation actions, ensuring all data incidents are resolved with full context.