Skill UI

A comprehensive set of production-ready client patterns for integrating with the Instantly.ai API v2 using TypeScript and Python. It provides robust solutions for managing API interactions, including type safety, sophisticated retry logic, pagination handling, and multi-tenant support, essential when building reliable, reusable wrappers without an official SDK.

A comprehensive checklist designed for ensuring that Kling AI video generation integrations are robust, secure, and reliable before going live or during a deployment review. It covers critical areas including authentication protocols, detailed error handling (retry logic, timeouts), cost control mechanisms, content safety policies, and necessary monitoring setup for a stable production environment.

This guide provides production-grade coding patterns for integrating Lokalise into TypeScript and Node.js applications. It covers crucial best practices such as client singleton implementation, robust cursor-based pagination for large datasets, structured error handling with retry logic, and efficient batch operations. Implementing these patterns ensures reliable, scalable, and maintainable API usage.

A comprehensive client library pattern for interacting with rate-limited APIs. This solution implements exponential backoff, robust retry logic (handling 429/5xx errors), concurrent request queuing, and cursor-based pagination. It ensures high throughput and reliable data fetching without exceeding API limits.

This guide provides production-grade patterns for building robust integrations with the MaintainX REST API. It covers essential techniques such as creating type-safe clients using TypeScript, implementing reliable cursor-based pagination, handling API rate limits with exponential backoff retry logic, and performing efficient batch operations. Ideal for developers building scalable, resilient API wrappers.

This guide demonstrates how to implement robust rate limiting and retry logic for critical healthcare APIs using a token bucket pattern. It covers managing specific service limits (e.g., clinical queries, evidence synthesis) and implementing strategies like exponential backoff and batch processing to ensure graceful degradation and reliable data retrieval in clinical decision support systems.

This skill coordinates the complex parallel execution of end-to-end, integration, and unit tests across multiple frameworks (e.g., Jest, Playwright, pytest) and CI/CD environments. It manages test splitting, worker allocation, tiered execution strategies, and aggregates results into unified reports. Ideal for optimizing the testing lifecycle and ensuring robust quality assurance in large-scale, multi-component applications. Key functions include intelligent retry logic for flaky tests and building CI pipelines (e.g., GitHub Actions matrix).

This guide provides robust, production-ready strategies for integrating with the PostHog API. It teaches developers how to handle API rate limits (429 errors) gracefully using exponential backoff and retry logic. Furthermore, it covers implementing request queuing for burst protection, optimizing with data caching, and monitoring rate limit headers, ensuring stable and reliable data ingestion regardless of traffic volume.

This resource provides production-ready, structured patterns for building robust voice agent applications using the Retell AI SDK. It covers best practices such as implementing singleton client management, defining typed agent configurations with integrated LLMs, adding automatic retry logic for call failures, and managing large-scale outbound campaigns. Use this when building stable, scalable, and reliable voice automation systems.

Guides API integration teams through retry logic helpers, outlining patterns, best practices, production-ready code, and validation for webhooks, SDKs, OAuth, and third-party APIs.

A comprehensive guide for managing resource limits and quotas across all Supabase services (API, Auth, Storage, Realtime, Edge Functions). Learn how to understand tier-specific limits, configure connection pooling using Supavisor (Transaction/Session mode), and implement robust retry logic with exponential backoff to gracefully handle 429 rate limiting errors, ensuring high application reliability.

This skill provides robust strategies and Python implementations for dealing with API rate limiting (HTTP 429 errors) when interacting with services like Vast.ai. It includes automatic retry logic, exponential backoff algorithms, and optimized techniques for batch operations and polling, ensuring stable and efficient API throughput.