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Design n8n automations for service integrations, scheduled tasks, databases, and AI agents. Choose from six workflow patterns, plan data flow and error handling, check connections before activation, and improve workflows that process large numbers of items.

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• Send form submissions to Slack
• Email a scheduled analytics report
• Synchronize records between databases

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

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• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Work across multiple n8n instances through one MCP connection, the link used by an agent to call tools. Specify each operation’s target, verify where credentials will be stored, and recover when requests reach the wrong environment.

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• Operate on production and staging with explicit targets
• Verify the destination before saving credentials
• Investigate unexpected NOT_FOUND errors

Write and troubleshoot native Python in n8n Code nodes. Covers data filtering and transformation, migration from older Python code, valid return formats, and restricted execution environments, including cases where errors pass unprocessed input downstream.

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• Filtering and aggregating data in Python Code nodes
• Migrating legacy Python (Beta) code
• Fixing input-variable and return-format errors

Write and troubleshoot n8n expressions, the formulas that pass and transform data between workflow steps. Correct field references, read webhook inputs, format dates and text, and query nested JSON while checking for silently missing results.

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• Include webhook form data in Slack messages
• Fix expressions that return null or literal text
• Extract and total values from nested JSON

Use n8n-mcp tools to find workflow steps, check settings, and create or update automations. Provides guidance on tool arguments, templates, folders, credentials, and security audits so agents can choose appropriate operations.

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• Finding nodes and checking their settings
• Creating and updating workflows
• Finding and deploying workflow templates

Build failure handling into n8n workflows with separate error paths, retries, alerts, and clear API responses. Use it for webhooks and unattended jobs to make failures visible and return status codes that explain what went wrong.

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• Add error responses to a webhook API
• Receive alerts when scheduled jobs fail
• Retry temporary external service failures

Find the specialist guidance needed to build, edit, test, or debug n8n workflows through n8n-mcp. This entry-point skill explains tool roles, directs tasks to related skills, and outlines checks for node settings, connections, outputs, and credential handling.

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• Choose specialist skills for an n8n automation task
• Check live node settings before editing
• Review connections and outputs before activation

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

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• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Configure n8n nodes by checking which fields each operation needs and which settings reveal additional options. Start with a minimal configuration, validate it, look up unclear properties, and make targeted edits to existing code or text fields.

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• Checking required fields when changing a node operation
• Finding why a configuration field is hidden
• Editing part of a long code or text field

Build n8n workflows that download, transform, and send files without losing their contents. Learn how file data differs from JSON, how to preserve attachments, and how to use storage keys or URLs with AI tools and chat messages.

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• Fixing attachments lost during data transformation
• Passing uploaded files to AI agent tools
• Displaying generated images in chat

Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.

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• Adding a calculation tool to an AI Agent
• Defining typed inputs for multiple arguments
• Fixing tool output type errors

Build reusable parts for n8n workflows instead of copying shared logic. This skill guides typed inputs, consistent outputs, naming, and execution settings so workflows and AI agents can call the same processing steps.

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• Sharing common logic across workflows
• Splitting a large workflow into reusable parts
• Exposing shared processing as an AI agent tool

Deploy n8n on a Linux server using Docker Compose and Caddy for automatic HTTPS. Choose a single instance or queue workers, configure secrets and Python task runners, and follow procedures for updates, backups, and recovery.

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• Deploy n8n on a personal VPS with HTTPS
• Set up queue workers for parallel workflow execution
• Update, back up, or restore an n8n instance

Design and build an official Apify integration for a company's product - workflow-automation apps (Zapier/n8n-style), AI agent plugins (coding-agent skills+MCP bundles or OpenClaw/Hermes-style harnesses), AI framework packages (LangChain/LlamaIndex-style), or direct application clients via apify-client. Use when planning, creating, or reviewing an integration that exposes Apify Actors, runs, datasets, or key-value stores inside another product.

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Read and write n8n: list workflows, inspect and update them, list executions. Trigger phrases: n8n, workflows.

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n8n otomasyon workflow'lari. Webhook, cron trigger, API entegrasyon, CI/CD otomasyon.

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n8n workflow automation patterns and API integration. This skill should be used when creating n8n workflows, using webhooks, managing workflows via REST API, or integrating n8n with MCP servers. Covers workflow JSON structure, node patterns, and automation best practices.

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Development standards for the Nathan n8n-Jira agent automation system. Covers n8n workflows, Python patterns, and project conventions.

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Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.

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SOP for building LLM applications on Dify — visual workflow + chatflow + agent + RAG knowledge base + plugin marketplace + observability, self-hostable. Use when shipping LLM apps fast with a "no-code to pro-code" gradient, especially when non-engineers need to co-author the flow.

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Expert guide for using n8n-mcp MCP tools effectively. Use when searching for nodes, validating configurations, accessing templates, managing workflows, or using any n8n-mcp tool. Provides tool selection guidance, parameter formats, and common patterns.

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Build a complete project management system with Telegram bot, task distribution, and photo reports using n8n. Based on DDC Project Management repository.

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Build Telegram bots for construction field workers. Real-time reporting, photo uploads, task assignments, progress tracking. Integrate with n8n for automated workflows.

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