Skill UI

This guide provides essential best practices for building resilient applications that interact with rate-limited APIs, specifically addressing the CAST AI REST API. It details the implementation of exponential backoff with jitter and utilizing request queuing mechanisms (like p-queue) to gracefully handle HTTP 429 errors. Use this pattern to optimize API call patterns and ensure system stability when making batch or frequent requests.

This guide details advanced patterns for managing API rate limits, particularly for external AI services like Cohere. It covers implementing exponential backoff with jitter for handling transient 429 errors, utilizing concurrency-limited request queues, and building proactive rate limit trackers. These techniques ensure highly reliable and optimized API throughput in production systems.

A comprehensive guide to production-ready patterns for ElevenLabs' TypeScript and Python SDKs. This resource covers crucial best practices including singleton client implementation, type-safe TTS service wrappers, robust error classification (rate limiting, quota), and concurrency management using request queues. Essential for building scalable and reliable voice AI applications.

A guide to optimizing API interactions with Flexport for logistics data. This skill implements advanced techniques including maximizing page sizes for bulk fetching, implementing LRU caching for frequently accessed data (e.g., product details), managing rate limits using parallel request queues (p-queue), and utilizing webhook-driven cache invalidation to ensure data freshness and high throughput.

This module provides robust patterns for managing rate limits and throttling when integrating with external APIs (e.g., Flexport). It covers three key strategies: monitoring rate limit headers (X-RateLimit-*), implementing exponential backoff with jitter for transient 429 or 5xx errors, and utilizing queue-based throttling for safe, controlled bulk operations. It ensures resilient and reliable API communication.

A comprehensive reference architecture for building robust and scalable supply chain integrations with Flexport's logistics API. It outlines a structured pipeline using Ingest (webhooks/polling), Core (business logic services), and Expose (REST API/Dashboard layers), ensuring reliable data flow, caching, and queueing best practices.

This skill provides best practices for managing API rate limits when interacting with the Hootsuite platform. It demonstrates implementing robust retry logic, incorporating exponential backoff using the `Retry-After` header, and using queue-based scheduling to ensure stable, high-throughput API calls while respecting platform constraints.

A comprehensive guide detailing three scalable architecture patterns for integrating applications with HubSpot CRM. It helps developers choose the optimal design based on project scale, ranging from simple embedded clients for MVPs, to robust Service Layers with caching and async queues, up to dedicated, enterprise-grade API Gateways for multiple services. Essential for planning complex, high-volume integrations.

A comprehensive guide to implementing production-grade reliability patterns for HubSpot CRM integrations. Learn to build fault-tolerant systems using Circuit Breakers, advanced retry strategies, graceful degradation, and Dead Letter Queues (DLQ). Essential for maintaining high availability when connecting to external APIs.

Provides advanced techniques for handling API rate limits, specifically targeting 429 errors. Implementations cover exponential backoff with jitter, queue-based request throttling, and proactive monitoring of rate limit headers (X-RateLimit-Remaining, X-RateLimit-Reset). Ideal for optimizing API request throughput and ensuring high reliability when integrating with services like Intercom.

A comprehensive architectural blueprint for building a high-read, optimized integration layer with the Linktree link-in-bio platform. This design utilizes a two-tier caching strategy (Redis), event queues (RabbitMQ/SQS), and webhooks to handle extreme traffic ratios. It ensures profile data remains fresh, while providing real-time click analytics and robust scalability considerations for production environments.

This reference architecture details the design of a multi-tenant integration layer for the MindTickle sales enablement platform. It strictly enforces tenant data isolation across all components (database, cache, queue) to ensure that organizational data remains separate. The system manages user synchronization (via SCIM 2.0), tracks course progress, records quiz results, and aggregates team readiness reports, making it ideal for complex SaaS enterprise deployments.