Skill UI

This guide provides a comprehensive local development setup for LLM applications using Langfuse. It enables fast iteration cycles, real-time trace debugging, and hot reloading, making it ideal for developers building and testing complex AI workflows locally. Instructions cover environment setup, advanced SDK usage, and optional self-hosted Docker deployment.

Automates Leadoku tasks through Composio’s Rube MCP by insisting on upfront schema discovery, connection validation, and schema-compliant RUBE_MULTI_EXECUTE_TOOL calls, making workflows reliable and repeatable.

This guide provides the framework for configuring Continuous Integration and Continuous Deployment (CI/CD) pipelines specifically for Lindy AI agent integrations. It covers setting up automated tests for webhook receivers, validating payload processing, and checking authentication signatures using GitHub Actions and Node.js, ensuring robust and reliable deployment of agent services.

A comprehensive guide for deploying the application layer that interacts with Lindy AI agents. This includes setting up webhook receivers, managing callback handlers, and ensuring production readiness across multiple platforms like Docker, Vercel, and AWS. It covers prerequisites, multi-step deployment instructions, post-deployment verification scripts, and rollback plans for robust MLOps practices.

A comprehensive guide for setting up a local development loop to test AI agent integrations. This skill teaches developers how to build and debug webhook receivers, callback handlers, and associated application code that interacts with a cloud-hosted AI agent (like Lindy). It requires using tunneling services (e.g., ngrok) to expose local endpoints for receiving webhooks and simulating the entire agent-to-application data flow.

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.

This skill provides comprehensive patterns for integrating external applications with the Lindy AI platform. It covers three primary channels: Webhook triggers for receiving inbound data, HTTP actions for making outbound API calls, and executing custom Python/JavaScript code (Run Code). Use this when building complex, multi-step automation workflows or connecting internal systems to Lindy agents.

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.

A comprehensive solution to integrate Linear project management with GitHub Actions CI/CD pipelines. This skill enables automating development workflows by running integration tests, automatically linking Pull Requests (PRs) to issues, transitioning issue states based on PR events, and creating new Linear issues directly from build failures. Ideal for ensuring development quality and maintaining up-to-date project tracking.

This skill integrates Continuous Integration/Continuous Deployment (CI/CD) pipelines with Linear issue tracking. It automatically detects deployment URLs (e.g., Vercel, Railway), adds detailed deployment comments to relevant Linear issues, and manages state transitions (e.g., moving an issue to 'Review' or 'Done') based on the deployment environment (staging or production). It ensures a seamless link between code commits, deployment status, and project management tickets.

This guide provides advanced techniques for managing API rate limits (429 errors) and complexity budgets when interacting with the Linear API. It covers reading rate limit headers, implementing exponential backoff with jitter for robust retries, controlling request bursts via token buckets, and optimizing GraphQL queries to minimize complexity scores. Use this when developing reliable integrations that must adhere to strict API quotas.

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.