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Agent Data Handling and Compliance Guide

lindy-data-handling

jeremylongshore/claude-code-plugins-plus-skills

This skill provides comprehensive best practices for managing data within AI agent workflows. It covers data classification (PII, PHI), prompt-level controls, secure memory usage, and isolation techniques. It ensures that agents adhere to global regulations like GDPR, CCPA, and HIPAA when handling sensitive or regulated data.

Lindy AI Agent Debugging Bundle

lindy-debug-bundle

jeremylongshore/claude-code-plugins-plus-skills

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.

Lindy AI Integration Deployment Guide

lindy-deploy-integration

jeremylongshore/claude-code-plugins-plus-skills

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.

AI Agent Incident Response Runbook

lindy-incident-runbook

jeremylongshore/claude-code-plugins-plus-skills

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.

Configure Lindy AI API and Webhooks

lindy-install-auth

jeremylongshore/claude-code-plugins-plus-skills

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.

Local Webhook Testing for AI Agents

lindy-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

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.

Advanced Automation Migration to Lindy AI

lindy-migration-deep-dive

jeremylongshore/claude-code-plugins-plus-skills

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.

Multi-Environment AI System Deployment

lindy-multi-env-setup

jeremylongshore/claude-code-plugins-plus-skills

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.

AI Agent Observability and Metrics Monitoring

lindy-observability

jeremylongshore/claude-code-plugins-plus-skills

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.

AI Agent Performance Tuning Guide

lindy-performance-tuning

jeremylongshore/claude-code-plugins-plus-skills

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.

Production Readiness Checklist for AI Agents

lindy-prod-checklist

jeremylongshore/claude-code-plugins-plus-skills

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.

Lindy AI Agent Integration Architectures

lindy-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

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.

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