Skill UI

Master advanced techniques for building fault-tolerant applications integrated with Supabase. This guide implements critical resilience patterns, including circuit breakers, offline queuing (using IndexedDB), graceful degradation, exponential backoff retries, and dual-write strategies. Use this skill to handle network outages, API failures, and transient errors, ensuring your application remains highly available and reliable in production environments.

Learn robust and production-ready patterns for interacting with Supabase backend services. This skill covers client initialization, advanced CRUD operations (filtering, upserting), authentication, real-time subscriptions, and RPC calls. Provides best practices for both TypeScript and Python environments, ensuring secure and reliable data handling.

A comprehensive guide covering modern best practices for building high-performance, declarative user interfaces in SwiftUI. Topics include using the advanced @Observable framework for state management, achieving type-safe navigation with NavigationStack, effective view composition, and optimizing rendering performance for iOS and macOS platforms.

This skill guides the creation of production-ready design systems using Tailwind CSS. It covers advanced topics such as implementing design tokens, managing component variants, establishing responsive patterns, and ensuring high accessibility standards. Use this when building component libraries, applying theming, or standardizing UI patterns across a large codebase.

A comprehensive guide to modern utility-first CSS using Tailwind CSS v4 principles. Covers the shift to CSS-first configuration, utilizing CSS variables for design tokens, implementing advanced container queries, and mastering responsive, dark mode, and complex layout patterns (Grid/Flex). Essential for building modern, component-driven UIs efficiently.

This skill guides developers through advanced, safe, and systematic code refactoring using Test-Driven Development (TDD) principles. It provides a comprehensive workflow encompassing pre-assessment, detecting code smells, applying major design patterns (SOLID, Factory, Strategy), optimizing performance, and ensuring safety through rigorous, incremental testing. Use this when improving code structure, restructuring large components, or updating legacy systems while guaranteeing functional integrity and maintainability.

A comprehensive guide and expert resource for mastering durable workflow orchestration using the Temporal Python SDK. Covers advanced patterns including distributed transactions, saga implementation, signal handling, and query patterns. Essential for building highly reliable, scalable, long-running backend systems, and mastering async/await execution models for production deployment.

Automates terraform module creation within the DevOps Advanced workflow, guiding through best practices, patterns, and production-ready code plus validations.

Automates Terraform provider configuration guidance for advanced DevOps teams, offering best-practice code, IaC patterns, and validation support when working with Kubernetes and CI/CD stacks.

A comprehensive guide covering best practices for unit and integration testing, including Test-Driven Development (TDD) and Behavior-Driven Development (BDD). This guide details factory functions for creating reusable mock data and props, advanced mocking strategies for modules and hooks, and custom rendering utilities, ensuring tests are maintainable and reliable in React/TypeScript environments.

This comprehensive skill provides advanced architectural patterns for implementing robust API throttling and rate limiting policies. It covers critical concepts like concurrency limiting, priority queuing, circuit breakers, and adaptive throttling that adjust limits based on real-time backend health metrics (p95/p99 latency). Use this when designing resilient, scalable backend services that must protect themselves from overload and maintain stability under high load.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.