Skill UI

This guide provides a comprehensive strategy for managing and reducing expenses within the Lindy AI platform. Learn how to audit agent credit consumption, right-size models by using less expensive versions for simple tasks, consolidate redundant agents, and optimize trigger frequency. Implementing these steps ensures cost efficiency and sustainable operation of your AI workflows.

A comprehensive diagnostic toolkit designed for advanced troubleshooting of Lindy AI agents. This bundle systematically collects environment information (OS, Python, Node.js), tests API connectivity for webhooks and major integrations (Slack, Notion, Gmail), reviews task history, and guides the user in generating a full, structured support ticket for technical support.

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.

This guide demonstrates the basic workflow for building an AI agent using a webhook trigger. The agent receives structured data (payload), utilizes an LLM to extract key information and generate a concise summary (summarization step), and finally executes an action by posting the summary to Slack or sending an email. Ideal for beginners learning about AI agent architecture, testing data flow, and implementing simple automated system notifications.

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.

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 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 guide provides comprehensive strategies for managing AI agent resource consumption and credit usage. It details optimizing task steps, setting trigger filters, implementing webhook rate limiting, and setting up budget alerts. Use this guide to ensure efficient, cost-effective AI operations and prevent unexpected billing overruns.

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.

This guide provides advanced strategies for optimizing API usage with Linear, helping users stay within strict rate limits and minimize operational costs. It covers best practices like replacing polling with webhooks, implementing request coalescing, minimizing query complexity, and utilizing smart caching with tailored TTLs, ensuring maximum throughput efficiency.

Automates LinkedIn workflows—posts, comments, profile/company info, and media uploads—through Composio's toolkit on Rube MCP after fetching current tool schemas. Use it to script routine social posting or profile management without manual UI steps.

A systematic, multi-phase workflow designed for diagnosing and resolving complex Linux system issues. It guides users through initial assessment, resource analysis (CPU, memory, disk), process investigation, log analysis, network diagnostics, and service failure resolution. Ideal for system administrators and DevOps engineers handling production incidents.