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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.

A robust job queue designed to orchestrate large-scale, multi-seed, and multi-configuration Machine Learning experiments on remote GPU clusters. It handles complex workflows, including wave transitions, dependency chaining (teacher-student), Out-of-Memory (OOM) retries, and stale screen cleanup, ensuring reliable execution for demanding scientific computing tasks.

A collection of production-ready patterns for integrating frontend applications with backend APIs. It solves complex asynchronous issues such as race conditions, stale data, and duplicate requests. Patterns include centralized API layering, request cancellation (AbortController), exponential backoff retries, debouncing, and request deduplication, ensuring highly resilient and reliable user experiences.

This module provides production-ready patterns for interacting with the Glean enterprise search API. It features a typed client, implementing singleton patterns and robust mechanisms for search and indexing. Key patterns include batch pagination, comprehensive error handling (e.g., retries for rate limits), and a fluent request builder for executing complex, reliable API calls.

This comprehensive checklist guides developers through the necessary steps to deploy a robust and reliable Grammarly API integration in a production environment. It covers critical aspects such as secure credential storage, rate limit handling, implementing text chunking, designing error resilience patterns (circuit breakers, retries), and establishing comprehensive monitoring and alerting systems to ensure high content quality and system uptime.

A comprehensive guide to building fault-tolerant integrations with Claude. It details advanced patterns such as built-in SDK retries, model fallback chains, circuit breakers, graceful degradation, and timeout mechanisms. Use this guide to design resilient systems that maintain availability even when the underlying API encounters failures.

A systematic checklist for launching Attio API integrations into a live production environment. It covers critical best practices for ensuring stability, including robust authentication, advanced error handling (exponential backoff, retries), rate limiting strategies, data integrity checks, health monitoring endpoints, and comprehensive rollback plans. Essential for reliable SaaS deployments.

This pattern library provides production-ready, structured patterns for integrating with the Abridge clinical AI API. It establishes type-safe client wrappers, implements mandatory HIPAA-compliant audit logging (ensuring no Protected Health Information, or PHI, is logged), and incorporates robust error handling with exponential backoff retries. Use this when building secure, scalable, and standards-compliant healthcare AI applications.

Implements robust rate limiting, backoff logic, and quota management for the Anthropic Claude API. It handles 429 errors and optimizes request throughput by managing Requests Per Minute (RPM), Input Tokens Per Minute (ITPM), and Output Tokens Per Minute (OTPM). This skill provides advanced strategies including SDK retries, custom header awareness, queue-based throttling, and batch processing for stable, high-throughput production applications.