Skill UI

A collection of production-ready Python patterns for interacting with the Vast.ai cloud API. This includes typed search query builders, context-managed instance lifecycle (guaranteeing auto-cleanup), offer scoring algorithms (cost, reliability, performance), robust retry mechanisms with backoff, and secure SSH command execution. Essential for building reliable, complex cloud workflow applications.

This guide provides comprehensive solutions for handling Vercel API rate limits (HTTP 429 errors). It covers implementing robust retry logic with exponential backoff for consuming Vercel's outbound APIs. Furthermore, it demonstrates how to configure Vercel's WAF rate limiting and custom edge configurations to protect your own deployed API endpoints, ensuring stability and preventing service abuse.

A comprehensive, production-ready TypeScript wrapper for interacting with the Vercel REST API. This client provides typed methods for managing deployments, projects, environment variables, and domains. It includes robust features like standardized error handling, exponential backoff retry logic, and structured request patterns, making programmatic integration with Vercel reliable and type-safe.

Automated assistant for planning and implementing webhook retry handlers within API integration workflows, offering best practices and production-ready code or configuration validation.

A comprehensive guide to mastering structured concurrency, reactive streams, and state management in Kotlin. Covers advanced topics like Flow operators (debounce, retryWhen), StateFlow for UI state, and implementing robust asynchronous logic for Android and Kotlin Multiplatform projects. Essential for modern Android development.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

Master the art of developing robust and scalable Python applications that interface with Microsoft Dataverse. This skill covers implementing enterprise-grade best practices, including comprehensive error handling using DataverseError hierarchy, advanced retry logic with exponential backoff, and optimizing API calls using OData standards (filtering, selecting, and expanding). It ensures code is clean, maintainable, and production-ready.

This tool opens the dedicated Context Mode Insight dashboard in your default browser. Insight serves as the central analytics layer for AI-assisted engineering teams, providing crucial metrics such as per-engineer productive rates, identifying retry waste, detecting blockers, and offering role-narrowed performance views. It is designed to be the single source of truth for comprehensive team performance monitoring and sign-in details.

This guide provides a set of production-grade TypeScript patterns for robust interaction with the Attio REST API. It covers essential best practices including typed clients, automatic retry mechanisms with exponential backoff, cursor-based pagination iterators, and multi-tenant factory implementation, ensuring reliable and scalable API integration. Use these patterns to handle common API challenges like rate limiting and large datasets efficiently.

A comprehensive guide and utility for managing API interactions with BambooHR. It implements robust retry logic (with exponential backoff and jitter) to handle 503 and 429 rate limit errors. It also provides patterns for proactive optimization, such as queue-based throttling, batching data using custom reports, and utilizing incremental sync to maximize API throughput and reliability.

This module provides robust handling for Fathom API rate limits (60 requests/minute per user). It implements advanced retry strategies (handling 429 errors and server failures) and efficient batch processing to reliably sync large volumes of meeting intelligence, such as transcripts and action items, into external systems like CRMs or project trackers.

Provides robust patterns for handling API rate limits, throttling, and connection errors when integrating with the Framer API. It covers implementing exponential backoff, retry mechanisms, batching large CMS writes, and debouncing plugin operations to ensure reliable and high-throughput API calls.