Skill UI

This skill provides expert guidance on integrating major payment processors like Stripe and PayPal. It covers end-to-end checkout flows, subscription billing, secure webhook handling, and strict PCI compliance requirements. Use this when building any feature involving financial transactions to ensure reliability and adherence to security best practices.

A comprehensive guide to integrating PayPal into applications, covering all aspects of the payment lifecycle. This skill covers client-side implementations using JavaScript SDK for Express Checkout, server-side API interactions (REST API) for order creation and capture, handling recurring billing via Subscriptions, managing asynchronous updates through IPN/Webhooks, and processing refunds and payouts. Ideal for building robust e-commerce payment gateways.

This guide provides comprehensive principles for modern game development, covering engine selection (Unity, Unreal, Godot), platform integration (Steam features, console compliance), input abstraction, and performance optimization techniques. It helps developers make informed decisions when building cross-platform games for PC and console environments.

A comprehensive guide to implementing Perplexity's API integration at various scales. It provides three validated architectural blueprints—Direct Widget, Cached Research Layer, and Multi-Query Pipeline—allowing developers to choose the optimal structure based on required query volume, latency goals, and complexity. Learn how to scale from simple search widgets to automated, high-volume research pipelines.

This guide details how to configure robust CI/CD pipelines for Perplexity Sonar API integrations using GitHub Actions. It emphasizes best practices for automated testing, including separating costly live integration tests from lightweight mocked unit tests, ensuring code quality while managing API costs and rate limits.

This guide provides a comprehensive reference for diagnosing and fixing common errors and exceptions encountered when interacting with the Perplexity Sonar API. Use this when troubleshooting API integration issues, handling specific HTTP status codes (like 401, 429, 402), or debugging failed requests. It covers best practices for rate limiting, key validation, and implementing robust error handling logic.

This guide provides comprehensive instructions for deploying applications powered by the Perplexity Sonar API. It covers setup for major cloud platforms like Vercel, Google Cloud Run, and Docker. Users can deploy production-ready endpoints, manage required secrets, implement edge functions, and utilize caching mechanisms (e.g., Redis) to ensure high availability and performance in production environments.

This guide provides comprehensive instructions for installing and configuring the Perplexity Sonar API authentication. It details setting up the necessary environment variables and using the standard OpenAI client library (for both Node.js and Python) to connect to Perplexity's API endpoint. Learning this process is essential for developers starting a new Perplexity integration, setting up API keys, or verifying API connectivity.

A comprehensive guide to identifying and avoiding common integration mistakes and anti-patterns when using the Perplexity Sonar API. It covers critical best practices such as correctly handling citations, setting maximum token limits, utilizing recency filters, and optimizing API calls to ensure cost efficiency and data accuracy. Essential for code review and developer onboarding.

This comprehensive checklist guides the deployment of Perplexity Sonar API integrations to a robust production environment. It covers critical aspects including API key management, robust code quality practices (retries, rate limiting), performance optimization (caching, streaming), detailed monitoring setup, and cost control strategies. Use this checklist when preparing for a major Perplexity feature launch or going live to production.

This guide outlines the comprehensive reference architecture for building advanced, AI-powered search and research applications, modeled after Perplexity. It covers three core tiers: a searchable service with model routing and caching, a citation pipeline for validation and rendering, and an orchestrator for multi-query research synthesis. Use this when designing complex, production-grade, search-augmented LLM integrations or reviewing advanced system patterns.

This skill provides production-ready patterns for integrating with the Perplexity Sonar API in both TypeScript and Python. It covers best practices for creating robust wrappers around the standard OpenAI client, including dedicated handling for citations, search results, and related questions. It also includes essential utilities like implementing exponential backoff for reliable retries. Use this when setting up complex search-augmented generation workflows or standardizing team coding practices around Perplexity usage.