Skill UI

A comprehensive runbook detailing procedures for responding to failures and outages across Lindy AI agents. Covers critical scenarios including platform downtime (SEV1), individual agent failures, integration authentication issues, credit exhaustion, and webhook endpoint failures. Provides structured diagnostics and fallback strategies to ensure business continuity during incidents.

This comprehensive guide details the end-to-end setup process for integrating Lindy AI into external applications. Learn how to obtain and manage API keys, install and initialize SDKs (Python/Node.js), and securely configure webhook authentication endpoints to enable programmatic access and trigger AI agents.

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.

A comprehensive guide detailing advanced strategies for migrating complex, rule-based automations from platforms like Zapier, Make, n8n, or custom codebases to Lindy AI. It teaches users how to redesign workflows, leveraging AI agents to replace rigid conditional logic with adaptable reasoning, ensuring a smooth and intelligent transition across multiple phases.

This guide details the best practices for configuring and isolating AI agents across development, staging, and production environments. It covers the setup of separate workspaces, secure secret management using tools like Vault/AWS/GCP, and integrating environment-aware logic into CI/CD pipelines to ensure data integrity and smooth promotion from dev to prod.

This guide details how to implement comprehensive observability for AI agents. It covers monitoring key metrics such as task success rates, step failures, execution duration, and credit consumption. Use this when setting up performance dashboards, configuring real-time alerts (via webhooks/Prometheus), or tracking agent reliability over time.

This guide provides comprehensive strategies for optimizing the execution speed, reliability, and cost efficiency of AI agents. Learn how to minimize latency, right-size models (e.g., Flash vs. Opus), consolidate multiple LLM steps into single prompts, and optimize workflow triggers. Ideal for use when agents are slow, consuming excessive credits, or producing inconsistent results.

This comprehensive checklist guides the deployment of AI agents into a live production environment. It covers critical areas including security best practices (API key management, SSO), robust error handling, monitoring setup, operational runbooks, and compliance adherence (HIPAA, GDPR). Use this when validating an agent for go-live to ensure maximum reliability and stability.

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.

This comprehensive guide outlines essential security best practices for implementing and using Lindy AI agents. It covers crucial topics such as robust API key management, webhook signature verification, strict agent permission scoping, and secure data handling protocols. Use this resource when auditing agent activity, configuring enterprise security features (SSO/SCIM), or ensuring compliance and minimizing the attack surface area of autonomous AI systems.

A comprehensive guide for managing Lindy AI agents through platform upgrades, reconfigurations, or workspace migrations. It covers the entire lifecycle, including inventory documentation, configuration exporting, phased deployment planning, and critical post-migration steps for webhooks, email, and phone numbers, ensuring business continuity.