Skill UI

Implement comprehensive automated policy enforcement for Shopify apps, ensuring security and adherence to App Store guidelines. This skill includes ESLint rules for detecting hardcoded API secrets, enforcing GraphQL query cost budgets, and running pre-commit hooks and CI checks for critical compliance validations (e.g., GDPR webhooks). Use it when hardening a production-ready Shopify application.

A comprehensive skill for managing Shopify's unique rate limiting systems, covering both the REST API's leaky bucket mechanism and the GraphQL API's calculated query cost. It provides robust techniques for handling 429 errors, implementing exponential backoff, and optimizing queries to ensure stable, high-throughput e-commerce integrations.

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.

This guide identifies and provides solutions for common anti-patterns and mistakes when working with Snowflake. It covers best practices in SQL writing, warehouse resource management, data loading strategies (e.g., using MERGE), and access control setup. It is essential for auditing existing deployments, onboarding new users, or performing code reviews to ensure cost efficiency and data integrity.

This guide provides a comprehensive methodology for load testing and capacity planning within Snowflake data warehouses. It covers optimizing query performance by adjusting warehouse sizes (Scale UP), simulating high concurrency using multi-threading (Scale OUT), and configuring advanced scaling policies (Standard, Economy, Maximized). Users can benchmark critical queries using SQL and simulate real-world user traffic using Python, ensuring robust and cost-efficient production environments.

This comprehensive guide details how to set up robust, isolated, and secure development, staging, and production environments within Snowflake. It utilizes best practices like zero-copy cloning for efficient data replication, implements granular Role-Based Access Control (RBAC), and defines environment-specific compute resources (warehouses) and data masking policies. This ensures data integrity, compliance, and cost optimization across all stages of the data lifecycle.

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.

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).

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.

Provides comprehensive guidance for performance tuning, inference, and fine-tuning using the Together AI platform. This skill supports working with Together AI's OpenAI-compatible API, covering model deployment, utilizing various open-source models (like Llama, Mixtral), and implementing best practices for efficient and cost-effective batch inference.

This comprehensive checklist guides the operational setup (MLOps) for AI applications using Together AI's OpenAI-compatible API. It covers critical production concerns, including secure API key management, implementing robust rate limit handling, building error resilience features (circuit breakers, exponential backoff), and setting up comprehensive monitoring for latency, cost control, and model availability. Essential for reliable, scalable LLM deployment.