Skill UI

This skill provides comprehensive strategies for managing rate limits when interacting with the Figma REST API. It implements crucial patterns like exponential backoff, respects 'Retry-After' headers, utilizes request queuing with concurrency control, and includes a proactive rate limit monitor. Use this when developing high-volume, resilient integrations that frequently encounter 429 errors or require optimized API throughput.

This utility helps manage API rate limits when building parallel integrations with multiple financial providers (Gusto, QuickBooks, Plaid, Stripe). It implements a token bucket algorithm and robust retry strategies to prevent shared-limit conflicts and ensure reliable data synchronization during high-volume processes like month-end reconciliation.

This skill provides comprehensive guidelines and implementation examples for managing rate limits when interacting with the Glean Indexing and Search APIs. It includes a custom RateLimiter class, robust retry logic (handling 429 errors and server errors), and best practices for batch processing large datasets, ensuring stable, high-volume data ingestion pipelines.

A guide and toolkit for optimizing Grammarly API costs in large-scale applications. It addresses common cost overruns associated with high text volume by implementing strategies such as result caching, chunking long documents, sample-based scoring, and setting usage budget alerts. Use this when integrating Grammarly into enterprise workflows or managing billing for high-volume content analysis.

A comprehensive guide and implementation pattern for handling rate limits, backoff, and idempotency when integrating with the Grammarly Text API. It provides a custom rate limiter and robust retry logic to manage API throughput, ensuring reliable processing of high volumes of documents and stable interaction with various endpoints (e.g., writing score, plagiarism checks).

This skill provides advanced techniques to optimize interactions with the Hootsuite API. It addresses common performance bottlenecks by implementing efficient LRU caching for social profiles, optimizing message scheduling through concurrent batch processing, and ensuring robust connection reuse. Use this when experiencing slow API responses or needing to process high volumes of automation tasks efficiently.

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.

This module provides robust coding patterns for interacting with the Linktree API, specifically designed to prevent hitting rate limits (429 errors). It implements a token bucket rate limiter and a sophisticated retry strategy featuring exponential backoff and jitter. Ideal for use cases involving bulk link updates, scheduled analytics data fetching, or high-volume data synchronization across multiple accounts, ensuring reliable API interaction.

This guide provides advanced techniques to optimize integration performance with the MindTickle API. It addresses common enterprise bottlenecks such as slow learner progress queries, rate limit errors, and high-volume webhook backpressure. Techniques covered include implementing Redis caching, using offset-based pagination for bulk data retrieval, managing concurrent requests with connection pooling, and applying exponential backoff for robust rate limit handling.

This guide provides comprehensive strategies for optimizing API usage and managing costs when integrating with Miro. It covers credit monitoring, advanced techniques like caching, server-side filtering, batching, and subscribing to webhooks. By implementing these best practices, developers can drastically reduce API call volume, stay within rate limits, and ensure highly efficient and cost-effective application development.

This skill provides advanced architectural patterns for optimizing high-volume integrations with the Navan API. It addresses common bottlenecks like rate limiting, sequential calls, and naive pagination by implementing robust solutions such as data-type-specific response caching, controlled parallel fetching with concurrency throttling, efficient cursor-based pagination, and HTTP connection reuse. Use this when building scalable, reliable, and high-throughput systems.

Provides advanced patterns for high-volume Notion API interactions. Learn how to manage rate limits (3 req/sec) using worker queues, implement parallel requests with p-queue, and efficiently paginate massive databases (100K+ records) using incremental sync and chunked processing. Essential for building robust, scalable Notion automation tools.