Skill UI

This comprehensive guide details advanced strategies for building highly resilient applications that interact with external LLM APIs. It covers built-in exponential backoff retries, controlling request throughput via concurrency limits, implementing provider fallbacks, and custom rate limiting using token buckets. Essential knowledge for achieving stable, high-volume API usage in production systems.

This comprehensive guide outlines essential, production-grade patterns for building robust LangChain applications. Learn best practices for implementing type-safe structured output using Zod, handling provider fallbacks, processing inputs concurrently with batching, optimizing user experience with streaming, and ensuring reliability through caching and retries.

This guide provides advanced techniques for managing API rate limits (429 errors) and complexity budgets when interacting with the Linear API. It covers reading rate limit headers, implementing exponential backoff with jitter for robust retries, controlling request bursts via token buckets, and optimizing GraphQL queries to minimize complexity scores. Use this when developing reliable integrations that must adhere to strict API quotas.

This SDK provides comprehensive, production-ready patterns for interacting with the OpenEvidence clinical evidence API. It enables developers to efficiently query medical literature, retrieve clinical guidelines, and generate evidence-based recommendations. Key features include a singleton client for consistent authentication, robust error wrappers (handling retries, invalid keys), and structured data models designed to ensure audit compliance and detailed citation provenance in complex healthcare applications.

A comprehensive technical audit checklist designed to validate the production readiness of OpenRouter API integrations. It covers critical areas including security practices (e.g., secret management, key rotation), system reliability (e.g., fallbacks, retries, timeouts), and observability requirements (e.g., structured logging, cost tracking). Use this guide before launching or during major operational reviews to ensure robust, scalable, and compliant deployments.

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.

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.

Automates guidance for configuring test retries in automation suites, walking through setup, best practices, and frameworks such as Jest or pytest so teams can stabilize flaky runs.

Enables Trigger.dev-based background jobs, AI workflows, and scheduled or batch async execution with a TypeScript-first SDK, durable retries, and built-in integrations while matching production behavior through the local CLI.

Upstash QStash expert handles serverless message queues, scheduled HTTP jobs, retries, deduplication, and callback tracking so you can deliver asynchronous tasks without managing infrastructure.

A comprehensive guide to implementing fault-tolerant patterns for Vercel serverless functions. Learn how to build resilient APIs using Circuit Breakers to handle external dependency failures, implementing Exponential Backoff for retries, and achieving Graceful Degradation by serving stale cache data. Essential for production-grade, highly available serverless architecture.

Produces production-ready Dataverse SDK code for Python, showcasing advanced error handling and retries, batch operations, OData query tuning, metadata lifecycle management, configuration/timeouts, cache invalidation, chunked file uploads, and Pandas integrations with references.