Skill UI

A comprehensive guide to mastering Shopify's calculated query cost system. Learn how to prevent API throttling errors by understanding the difference between requested and actual query costs. The guide covers advanced techniques like optimizing pagination, selecting minimal fields, avoiding deep nesting, and utilizing bulk operations for massive data exports.

This guide provides a minimal, working example of connecting to a Shopify store using the GraphQL Admin API. It demonstrates how to initialize the API client, authenticate the connection, and execute a query to retrieve product and variant details. Ideal for developers starting a new e-commerce integration, testing API connectivity, or learning basic Shopify data retrieval patterns.

This guide details how to implement robust and secure authentication for Shopify applications using the official @shopify/shopify-api SDK. It covers three main authentication methods: OAuth flow for public apps, session token exchange for embedded apps, and private access for custom apps. Use this skill when setting up, connecting, or debugging any application that requires deep integration and secure API access to Shopify's admin or storefront data.

This guide details how to use Shopify Metafields and Metaobjects via the GraphQL Admin API. Metafields allow attaching key-value custom data to existing resources (like products or orders), while Metaobjects enable defining entirely new, structured content types. Together, they provide a powerful way for developers to model complex custom data, often eliminating the need for external databases to extend e-commerce functionality.

A comprehensive deep dive into migrating entire e-commerce platforms to Shopify. This guide details how to transfer product catalogs, customer records, historical orders, and inventory using advanced methods like Shopify's GraphQL Admin API, bulk mutations (e.g., productSet), and staged uploads. Ideal for developers and technical teams undertaking platform replatforming or migrating data from other e-commerce systems.

This guide details how to configure Shopify applications across isolated Development, Staging, and Production environments. It establishes separate app instances, API credentials, and stores for each environment, ensuring secure, structured deployments and preventing cross-environment data leakage. Essential for professional SaaS development workflows.

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.

A comprehensive runbook for rapid incident response to Snowflake outages, query failures, and pipeline breaks. It guides users through systematic triage, checking account usage, managing resource monitors, and performing rollbacks using time travel, ensuring minimal data loss and quick recovery after critical data infrastructure incidents.

This comprehensive guide provides step-by-step instructions for upgrading Snowflake drivers (Node.js and Python) and managing complex data warehouse migrations. It covers prerequisite checks, reviewing release notes, handling common API breaking changes (e.g., cursor types, async connections), and performing post-migration validation to ensure application stability during platform or edition upgrades.

Master event-driven patterns within Snowflake using native alerts, email integrations, external functions, and cloud notification services (S3/GCS). This skill is essential for building robust, reactive pipelines that trigger actions (like sending alerts, calling external APIs, or ingesting data) in response to data changes or specific system events. Use cases include data quality checks, real-time monitoring, and automated notifications.

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.