Skill UI

This guide details how to implement webhooks for Kling AI video generation tasks. Instead of inefficiently polling the API status, you can provide a callback URL when submitting a task. When the task completes (whether successful or failed), Kling AI will automatically send a POST request containing the full result to your designated endpoint. This mechanism eliminates polling overhead and is essential for building robust, event-driven video processing pipelines.

Master the creation of complex, event-driven, and scheduled automation workflows. This guide covers setting up various triggers (email, chat, webhook, calendar), implementing conditional filters, managing multi-step processes, and orchestrating specialized AI agents in a multi-agent 'Society' for advanced delegation and collaboration.

This skill provides comprehensive reference architectures for integrating Lindy AI agents into production applications. It covers various complex patterns, including simple webhook callbacks, sophisticated event-driven pipelines, multi-agent delegation systems, scheduled workflows, and RAG-powered customer chat interfaces. Essential for designing and building robust, scalable, and reliable AI-powered systems.

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 guide provides production-grade architectural patterns for integrating with the Linear API. It helps developers choose the optimal strategy—be it Simple SDK calls, Service-Oriented Gateways with caching, or Event-Driven Webhooks—based on system complexity, scale, and real-time requirements. Includes patterns like CQRS and rate limiting best practices.

This comprehensive guide details production-grade architectural patterns for integrating MaintainX with large enterprise systems, including ERPs, SCADA, and data warehouses. It covers best practices for event-driven synchronization using webhooks and Pub/Sub, implementing bi-directional sync gateways, and establishing scalable analytics data pipelines.

A comprehensive guide to implementing event-driven integrations with MaintainX. This skill covers setting up webhooks to capture real-time updates for work orders, asset changes, and maintenance notifications. It includes best practices like HMAC signature verification, idempotent processing to prevent duplicate actions, and setting up local development environments.

Guides designing robust microservices with service boundaries, event-driven communication, saga orchestration, API gateways, health checks, and anti-pattern awareness for resilient distributed systems.

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 specialist provides expert architectural guidance for designing and implementing scalable modular monolith backends using NestJS. It leverages Domain-Driven Design (DDD), Clean Architecture, and event-driven patterns to establish clear bounded contexts and module boundaries. Use this skill when building complex systems that require high modularity, maintainability, and a planned path toward microservices, ensuring state isolation and loose coupling within a single deployment unit.

This skill provides comprehensive reference architectures for building production-grade AI applications using OpenRouter as a unified LLM gateway. It covers three scaling patterns: simple single-service, standard microservice (with caching and budget enforcement), and enterprise event-driven systems (with message queues and workers). Use this when planning system design, reviewing complex AI pipelines, or ensuring scalability and cost control in your LLM integration.

This skill enables the development of robust, event-driven pipelines interacting with the Perplexity Sonar API. Since Perplexity lacks native webhooks, this system simulates event patterns using advanced techniques: Streaming Server-Sent Events (SSE) for real-time data, job queues (like BullMQ/SQS) for asynchronous batch research, and cron-triggered monitoring for scheduled news and trend alerts. It provides patterns for building complex, reliable, and scalable AI applications.