Skill UI

This guide provides essential security best practices for implementing Langfuse, an LLM observability platform. It covers critical areas such as secure credential management (public vs. secret keys), implementing PII scrubbing techniques before tracing, hardening self-hosted environments, and enforcing the principle of least privilege. Use this guide to ensure your LLM integrations meet strict data privacy standards (e.g., GDPR, HIPAA).

A comprehensive guide providing step-by-step instructions for upgrading Langfuse SDK versions (v3 to v5) and migrating between major API changes. It covers best practices for both TypeScript/JavaScript and Python implementations, focusing on adopting OpenTelemetry standards to ensure seamless LLM observability integration.

Master the configuration of Langfuse webhooks to capture crucial prompt lifecycle events (Created, Updated, Deleted). This skill guides you through building a secure endpoint to intercept these events, enabling powerful, automated integrations. Use cases include triggering CI/CD pipelines upon production prompt changes, syncing data to external systems, or notifying teams via Slack.

Use natural language via the Composio MCP integration to monitor orders, subscriptions, customers, discounts, products, and checkout sessions across your Lemon Squeezy store, letting you manage all digital business operations without hitting the dashboard.

Use the Composio Lever integration in Claude Code to manage Lever ATS job postings, opportunities, requisitions, tags, and pipeline stages entirely from the terminal for streamlined recruiting workflows.

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 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.

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 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.