Skill UI

This skill provides comprehensive, multi-layered diagnostic scripts for resolving complex and intermittent issues with the Perplexity Sonar API. It allows users to test connectivity (DNS, TLS, TCP), profile API latency, validate citation stability, and verify search recency filters. It is crucial when standard troubleshooting methods prove insufficient for diagnosing hard-to-replicate API bugs or performance regressions.

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 advanced strategies for developers to significantly reduce Perplexity Sonar API billing costs. It covers key techniques including model routing (selecting the right model by query type), setting maximum output tokens, implementing robust query caching, and utilizing domain filters to minimize per-request charges. Essential for building cost-effective, high-volume AI applications.

This tool is designed to collect comprehensive diagnostic evidence for troubleshooting Perplexity API issues. It automatically gathers critical environment details (OS, Node/Python versions), tests API connectivity, and captures redacted configuration settings. The resulting secure bundle is ideal for submitting detailed support tickets or sharing with technical teammates.

This guide details how to implement Role-Based Access Control (RBAC) for Perplexity API in enterprise environments. Since Perplexity lacks native RBAC, the solution involves building a middleware gateway. This gateway enforces granular policies, including team-specific API key usage, model restriction (e.g., limiting access to 'sonar' only), budget caps, rate limiting, and mandatory search domain filtering for compliance and security.

Provides a minimal working example demonstrating how to integrate Perplexity's API for web-grounded answers. Learn to perform advanced searches, retrieve cited sources, and handle features like domain filtering and streaming using both TypeScript and Python. Ideal for integrating real-time, source-backed search functionality into applications.

This comprehensive runbook provides step-by-step procedures for rapidly responding to Perplexity API outages and service degradations. It covers initial triage (checking models, auth, DNS), handling common errors (401, 429, 500/503), implementing fallbacks, and structured post-incident activities, including evidence collection and postmortem documentation.

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 skill provides comprehensive guidance on load testing and capacity planning for the Perplexity Sonar API. Users learn how to simulate high traffic volumes, respecting strict rate limits (like 50 RPM), benchmark latency, and estimate operational costs. It is essential for performance engineering, capacity architecture design, or stress testing APIs at scale.