Skill UI

This skill guides developers and SaaS founders on minimizing operational costs when building on the Shopify platform. It covers deep dives into Shopify store plan rate limits, optimizing GraphQL and REST API usage, implementing robust cost tracking mechanisms, and applying advanced strategies like caching and bulk operations to reduce billable expenditures.

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 skill provides comprehensive SQL queries and procedures to analyze and optimize Snowflake usage costs. It covers budgeting via resource monitors, right-sizing compute warehouses, tuning auto-suspend settings, and minimizing storage costs. Use it when reviewing billing, implementing cost controls, or reducing credit consumption in your Snowflake environment.

A comprehensive guide to optimizing query performance in Snowflake. Learn advanced techniques such as implementing clustering keys, utilizing materialized views, leveraging result caching, and analyzing query profiles. This guide also covers best practices for warehouse tuning and optimizing common SQL anti-patterns to ensure efficient data querying and cost management.

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.

This skill focuses on optimizing the performance of WebContainers, specifically targeting improvements in boot time, file system mounting efficiency, and process spawning. It is essential for developers utilizing the WebContainer API or the StackBlitz SDK to ensure a stable and efficient development environment, especially in complex web applications.

A comprehensive guide for automating workflows using the TechSmith Snagit COM API and Camtasia. This skill provides detailed instructions on cost tuning, handling various input/output types, and troubleshooting common errors for advanced screen capture and video editing automation. Primary scripting methods involve PowerShell and Python (pywin32).

This guide provides comprehensive technical guidance for performance tuning and automating workflows using TechSmith Snagit COM API and Camtasia. It details key considerations for robust scripting, including using PowerShell and Python interop, defining various input/output types (like desktop, window, clipboard, or file), and solving common errors (such as COM registration failures or permission issues). Use this skill when building complex, automated pipelines for screen capture and video editing.

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.

This workflow guides users through the complete lifecycle of customizing large language models (LLMs). It covers uploading training data, initiating fine-tuning jobs, monitoring training progress, managing model versions, and finally deploying the optimized model to a dedicated, autoscaling inference endpoint using the Together AI API. Ideal for production-level customization.

A comprehensive guide to optimizing operating costs when utilizing Together AI's OpenAI-compatible API. This resource details strategies for cost reduction across various AI tasks, including inference, fine-tuning, and model deployment. Learn how to select appropriate model sizes, implement caching, leverage batch inference, and utilize Turbo variants to maximize efficiency and minimize spending.