Skill UI

This guide provides a comprehensive, production-grade architecture for implementing analytics using PostHog. It covers best practices for event taxonomy design, SDK layering (client and server), feature flag management, and integrating the data pipeline (CDP). Ideal for tracking user lifecycle, feature adoption, and revenue in complex web applications.

Blueprint skill describing the architecture, stack, folder layout, API patterns, and testing/deployment workflows used by the Zenith AI customer discovery platform clade. Use it as a reference when working on this dedicated project to understand expectations for frontend, backend, AI integration, and QA.

This guide provides a comprehensive reference architecture for building robust, production-grade applications on Replit. It covers best-practice project layouts, system-level configurations (like replit.nix), data layer strategies (PostgreSQL, KV, Object Storage), and secure deployment techniques. Use it when starting a new Replit project, optimizing existing codebases, or establishing enterprise-level architectural standards.

Provides implementation patterns and reference architecture for building advanced AI voice agents and automating complex phone call workflows using the Retell AI SDK. This skill is designed for developers requiring robust voice interaction, real-time telephony integration, and comprehensive conversational AI capabilities in their applications.

Automates sdk documentation tasks by guiding users through best practices, producing production-ready docs, and validating outputs for APIs, user guides, and architecture reference.

This guide provides enterprise-grade architectural patterns for integrating Sentry into multi-service, microservices environments. It covers centralized configuration modules, standardized project topologies (e.g., one project per service), distributed tracing setup, and robust error handling middleware for Node.js, Express, and Python/FastAPI. Ideal for organizations planning large-scale Sentry rollouts or managing complex service ecosystems.

A comprehensive developer reference covering the full spectrum of Shopify development, including Liquid templating, OS 2.0 architecture, GraphQL APIs (Admin and Storefront), and modern headless solutions using Hydrogen. This guide assists in building advanced themes, custom apps, implementing serverless business logic via Functions, and optimizing performance for modern e-commerce storefronts.

This guide provides the complete, production-grade reference architecture for integrating the Speak language learning platform. It covers all necessary components, including the client, API gateway, assessment engine, and progress store. Use this blueprint to ensure a robust, scalable, and reliable implementation for managing large-scale language learning operations.

A comprehensive guide to implementing enterprise-grade Supabase architectures. This skill covers advanced patterns like monorepo layout, multi-tenant Row Level Security (RLS) isolation, microservices integration, and operational patterns (caching, queues, audit logging). Use this when building complex, scalable, and production-ready applications on Supabase.

Automates the creation of technical troubleshooting guides by following documentation best practices, generating templates, and validating outputs for API docs, user guides, and architecture references whenever you mention troubleshooting patterns.

This comprehensive guide outlines the production-grade reference architecture for advanced AI meeting systems using TwinMind. It details the end-to-end workflow, integrating high-accuracy transcription (Ear-3), AI summarization (GPT-4, Claude, Gemini), memory vault management, and automatic action item and calendar integration. Use this resource when implementing or managing a robust, automated AI meeting solution.

This guide details the production-grade reference architecture for running complex GPU compute workflows on Vast.ai. It covers the foundational three-tier pattern—Orchestrator, GPU Workers, and Artifact Storage—providing blueprints for job queue management, fault-tolerant training pipelines, and robust checkpointing. Use this when designing scalable and reliable ML/AI training systems.