patterns▌

Core React patterns for component design, state management, and optimization.

React and Next.js patterns for component composition, state management, performance, and accessibility. \n \n Covers component patterns including composition, compound components, and render props, plus custom hooks for state, data fetching, and debouncing \n Includes performance optimization techniques: memoization, code splitting with lazy loading, and list virtualization for large datasets \n Provides form handling with validation, error boundaries, and animation patterns using Framer Motion

Jest testing patterns, factory functions, and TDD workflows for unit test development. \n \n Covers TDD red-green-refactor cycle, behavior-driven testing focused on public APIs, and factory pattern for reusable test data and component props \n Provides custom render utilities, mock strategies for modules and GraphQL hooks, and structured test organization with describe blocks \n Includes query patterns (getBy, queryBy, findBy), user interaction simulation with fireEvent, and anti-patterns to avo

You are a no-code automation architect who has built thousands of Zaps and Scenarios for businesses of all sizes. You've seen automations that save companies 40% of their time, and you've debugged disasters where bad data flowed through 12 connected apps.

FastAPI async patterns for building high-performance, concurrent APIs with optimal resource usage. \n \n Covers async route handlers, database operations (SQLAlchemy, asyncpg, Motor, Tortoise ORM), and connection pooling strategies for efficient resource management \n Includes real-time communication patterns: WebSockets with authentication and broadcasting, Server-Sent Events (SSE), and streaming responses for large files or generated content \n Provides concurrent request handling with asyncio

Comprehensive guide to auditing web content against WCAG 2.2 guidelines with actionable remediation strategies.

Performance optimization patterns for Mapbox GL JS applications, prioritized by user impact. \n \n Eliminate initialization waterfalls by loading map data in parallel with map initialization, and set precise viewport to avoid redundant tile fetches \n Use symbol layers for 100+ markers (GPU-accelerated) and clustering for 10,000+ features; avoid HTML markers at scale \n Choose GeoJSON for datasets under 5 MB, vector tiles for 20+ MB; implement viewport-based loading to reduce bandwidth \n Always

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Backend architecture patterns and best practices for scalable server-side applications.

Structured decision-making framework for selecting and designing APIs across REST, GraphQL, and tRPC patterns. \n \n Provides a content map covering API style selection, resource design, response formats, versioning strategies, authentication, rate limiting, and security testing \n Includes decision trees and checklists to guide API type selection based on consumer requirements and project context \n Documents anti-patterns (verb-based endpoints, inconsistent responses, exposed errors) alongside