Skill UI

Automates end-to-end mobile app testing on iOS and Android via Appium, Detox, XCUITest, or Espresso, including page objects and device/simulator setup.

Automates end-to-end mobile app testing on iOS and Android via Appium, Detox, XCUITest, or Espresso, including page objects and device/simulator setup.

A comprehensive guide for Corrective and Preventive Action (CAPA) management within Quality Management Systems (QMS), specifically tailored for medical devices. It covers the entire investigation lifecycle, including root cause analysis (RCA) using methodologies like 5-Why and Fishbone diagrams, developing robust corrective actions, and verifying effectiveness to ensure continuous compliance and quality improvement.

Automates cross-browser compatibility verification by orchestrating BrowserStack, Selenium Grid, or Playwright runs across Chrome, Firefox, Safari, and Edge, capturing screenshots/logs and producing a report when requested via `/browser-test`/`/bt` to confirm web app behavior across devices.

Comprehensive guidance for medical device manufacturers navigating complex FDA regulatory requirements. Covers device classification, submission pathways (510(k), PMA, De Novo), Quality Management System Regulation (QMSR/ISO 13485), HIPAA compliance, and cybersecurity best practices. Use this when determining the correct regulatory route for a new or modified device.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

Generates end-to-end tests for web workflows using Playwright, Cypress, or Selenium, automating browser steps and assertions so QA teams can validate registration, login, shopping cart, and other multi-step flows across browsers and devices.

A comprehensive guide for designing cross-device applications across the Apple ecosystem. This skill helps developers adhere to the specific Human Interface Guidelines (HIG) for various platforms—including iOS, macOS, watchOS, tvOS, and visionOS. It emphasizes adapting design principles (e.g., touch-first vs. pointer-based vs. spatial) while maintaining brand consistency, ensuring a native and optimal user experience regardless of the operating system.

This comprehensive guide outlines Apple's Human Interface Guidelines (HIG) for various technologies and user flows (e.g., Siri, Apple Pay, HealthKit, ARKit, Generative AI). It teaches developers how to ensure their applications feel natively integrated, respect user privacy, and adhere to established platform patterns across iOS, macOS, and other Apple devices. Essential reading for building high-quality, compliant, and seamless user experiences.

HuggingFace Accelerate adds distributed support to any PyTorch script with just four lines, providing a unified API for DDP, DeepSpeed, FSDP, and Megatron, plus automatic device placement, mixed precision, and simple launch/config commands for fast prototyping.

Knowledge Distillation is a crucial technique used to compress large, high-performing Language Models (Teacher) into smaller, more efficient Student models. This allows developers to deploy powerful AI capabilities (like those of GPT-4) on resource-constrained devices while significantly reducing inference costs. The method uses advanced techniques such as soft targets, temperature scaling, and Reverse KL Divergence (MiniLLM) to transfer knowledge effectively.

llama.cpp is a pure C/C++ library designed for efficient LLM inference, optimized to run on resource-constrained environments. It excels in scenarios where high-end NVIDIA GPUs (CUDA) are unavailable, making it ideal for Apple Silicon Macs, AMD/Intel GPUs, or edge deployment devices like Raspberry Pi. It supports GGUF quantization formats, enabling significant memory reduction and speed improvements for local, cross-platform AI applications.