Skill UI

This comprehensive guide details strategies for optimizing Shopify API performance. Key techniques include reducing excessive GraphQL query costs, implementing bulk operations for large data exports, setting up robust response caching with webhook invalidation, and utilizing the Storefront API for high-traffic public storefronts. Essential for developers hitting rate limits or slow data retrieval.

This guide details how to build modern, custom headless storefronts using Shopify's Storefront API and Cart API. It covers key functionalities like product querying, cart management (adding/updating lines), and initiating the checkout flow. Designed for developers building client-facing e-commerce experiences, it emphasizes the use of public access tokens for decoupled, highly customizable online stores.

This skill guides developers through advanced techniques to optimize Shopify store performance and improve Core Web Vitals (LCP, CLS, INP). It covers fixing lazy-loaded hero images, migrating to the modern `image_url` filter for responsive images, profiling slow Liquid renders, and optimizing font delivery, ensuring a fast and professional e-commerce experience.

Guides developers through the pricing tiers of StackBlitz, covering free embedding options and commercial licensing for the WebContainer API. This skill is essential when integrating WebContainers or utilizing the StackBlitz SDK, ensuring correct setup, cost management, and adherence to official usage guidelines.

Provides a comprehensive production checklist specifically for WebContainer applications built using the StackBlitz SDK. This guide helps developers ensure that their web applications are robust and ready for deployment by checking essential aspects like security headers, cross-browser compatibility, and necessary fallback mechanisms.

This guide provides essential knowledge on resource limitations within WebContainer environments (such as memory, CPU, file system size, and process count). It is crucial for developers utilizing the WebContainers API or StackBlitz SDK to understand these constraints. Learn best practices for monitoring memory usage, optimizing mounted file systems by excluding unnecessary assets (like test or documentation folders), and managing dependencies to prevent common issues like Out-Of-Memory (OOM) crashes and slow build times.

This comprehensive guide provides production-ready patterns for the WebContainer API, essential for building sophisticated online IDEs and code playgrounds. It covers key functionalities such as singleton WebContainer management, robust file system CRUD operations, process lifecycle management, jsh terminal emulation, and embedding projects using the StackBlitz SDK. Ideal for developers creating collaborative development environments.

This guide provides comprehensive solutions for common automation errors encountered when utilizing the TechSmith Snagit COM API and Camtasia. It covers critical topics such as COM server registration, elevated permissions, and file locking issues, making it essential for developers writing automation scripts in PowerShell or Python.

This guide details the deploy integration process for automating screen capture and video editing using TechSmith's Snagit COM API and Camtasia Producer CLI. It is designed for developers needing to orchestrate complex content pipelines, such as programmatically capturing specific screen regions, handling various output formats (clipboard, file, printer), and automating batch rendering within a Windows environment using PowerShell or Python.

Provides comprehensive instructions for installing, registering, and interacting with the TechSmith Snagit and Camtasia COM Server API. This guide covers setup for COM interop using PowerShell, Python, and C#, allowing developers to automate screen capture and video processing workflows without traditional API keys.

A comprehensive reference guide for common errors encountered when interacting with the Together AI API. This includes error codes and solutions for inference, fine-tuning, and model deployment, covering authentication, rate limits, validation failures, and billing issues. Essential for developers integrating OpenAI-compatible LLM services.

A comprehensive workflow guide for fine-tuning open-source Large Language Models (LLMs) using the Together AI API. This process covers preparing structured training data (JSONL format), uploading the file, initiating the fine-tuning job, monitoring the training status, and finally deploying the custom model for inference. Ideal for developers needing to customize models for specific enterprise tasks, such as customer support or specialized content generation.