Skill UI

Provides comprehensive strategies for managing API rate limits (RPM/TPM) when integrating with Mistral AI. This guide demonstrates implementing token-aware rate limiters, exponential backoff, and robust retry mechanisms using `Retry-After` headers, ensuring high throughput and stable application performance for large-scale LLM applications.

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.

Provides comprehensive patterns for creating production-grade client wrappers when integrating with OpenRouter via the OpenAI SDK. This skill covers handling advanced topics such as robust retry mechanisms, structured typing, caching strategies, and middleware implementation. It is essential for developers building stable, reliable SDKs or commercial client libraries that require enterprise-level API robustness.

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 details the recommended pattern for managing per-project plugin settings and state within a development environment. Plugins store their configuration in a dedicated `.claude/plugin-name.local.md` file. This file utilizes YAML frontmatter for structured settings (like enabling flags, retry counts, and parameters) and a Markdown body for additional context or instructions. It provides techniques for reading these settings using Bash hooks, commands, and agents.

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.

This guide provides production-ready patterns for integrating the Speak language learning API. It covers managing conversation sessions, performing pronunciation assessment, preprocessing audio files using ffmpeg, implementing robust retry mechanisms (with backoff), and tracking user learning progress. Use this when developing a full-featured, high-reliability language learning application or troubleshooting complex API integrations.

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.

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.

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.