Skill UI

This tool provides comprehensive Python solutions for processing batches of text-to-video requests using the Kling AI API. It handles controlled parallelism, rate-limiting, and detailed status tracking. Use this when building robust content pipelines that require generating multiple videos efficiently from a list of prompts, supporting synchronous, asynchronous, and webhook callback methods.

This skill provides comprehensive monitoring and tracking for asynchronous video generation tasks using the Kling AI API. It supports polling single tasks, managing large batches of jobs, and detecting stalled or failed processes across multiple video endpoints. Ideal for developers building robust workflow dashboards, implementing reliable batch processing, or debugging long-running media generation pipelines.

This skill provides comprehensive strategies for managing API rate limits, focusing on high-volume and mission-critical workflows. It implements exponential backoff with jitter, task concurrency limiting using `asyncio`, and request queue patterns. Designed to detect and gracefully handle `429 Too Many Requests` errors, ensuring reliable and stable execution even under severe throttling conditions.

A comprehensive guide to mastering asynchronous programming using Kotlin Coroutines and Flow. Covers advanced topics such as structured concurrency, robust exception handling, and creating reactive data streams with StateFlow and SharedFlow. Essential for developing high-performance, modern Kotlin applications.

This guide provides production-grade architecture patterns for implementing robust LLM observability using Langfuse. It covers singleton SDK setup, context propagation across asynchronous boundaries, Express middleware for automatic tracing, and cross-service trace correlation using OpenTelemetry. Ideal for designing enterprise-level, scalable tracing infrastructure for LLM applications.

Comprehensive skill for configuring advanced Lindy AI integration using webhooks. This covers three key patterns: Inbound webhooks (external systems triggering Lindy), Outbound requests (Lindy calling external APIs), and two-way Callback mechanisms for asynchronous communication. Use this skill when building complex, event-driven workflows that require reliable, real-time data exchange between your applications and the AI agent.

This skill guides developers through setting up and processing Lokalise webhooks for real-time content synchronization. It covers the API integration steps to register webhook endpoints, handling various translation lifecycle events (key creation, translation updates, file imports), and implementing secure payload verification using webhook secrets. It demonstrates building robust, asynchronous event handlers in Node.js/Express, perfect for automating workflows based on content changes in a SaaS environment.

This guide provides production-ready patterns for integrating the Mistral AI SDK in both TypeScript and Python. It covers essential best practices such as singleton client management, handling structured JSON output, implementing streaming responses, concurrent asynchronous processing, and robust error handling with exponential backoff. Use this when establishing team coding standards or refactoring Mistral API usage.

A comprehensive guide to implementing advanced asynchronous patterns with Mistral AI. This skill covers using the Agents API for stateful, multi-turn workflows, the Batch API for cost-effective bulk inference, and event-driven streaming architectures. It is essential for building robust, long-running, or high-volume AI applications that require reliable background job handling.

This comprehensive guide covers advanced topics in modern JavaScript (ES6+), focusing on functional programming patterns and industry best practices. It is ideal for developers looking to refactor legacy code, optimize performance, manage asynchronous operations (Promises/async-await), and build highly maintainable, scalable web applications.

Enables Claude to monitor and analyze application CPU usage, detect hotspots, inefficient loops and regexes, and offer optimization strategies such as asynchronous processing or algorithmic improvements to boost performance.

This comprehensive guide helps developers make critical architectural decisions in modern Node.js development. It covers framework selection (Fastify, Hono, Express), implementing layered architecture, mastering async patterns, robust error handling, and essential security protocols. The focus is on teaching architectural thinking and problem-solving skills, ensuring the development of scalable, high-performance, and maintainable backend APIs.