Agent skill · software engineering · sickn33
n8n-agents
Design n8n AI agents, chains, classifiers, extractors, tool calling, memory, RAG, structured output, and human-review flows.
Why this skill is useful
Adds detailed workflows and configurations for designing n8n AI agents, including multi-turn interactions and tool integrations that aren't covered in public documentation.
What it needs
Requires n8n installed locally. About 12k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
n8n Agents When to Use Use this skill for n8n AI Agent, LangChain, classifier, extractor, memory, RAG, tool-calling, structured-output, or human-review design. Confirm the target n8n instance and inspect the live node schema before applying version-sensitive configuration. Before activating or testing a workflow that can send messages, write data, make purchases, change accounts, or call external services, show the user the exact effects and obtain approval. Store provider keys and tokens only in n8n credentials; never place them in prompts, Set nodes, workflow JSON, examples, or logs. The n8n AI Agent node (@n8n/n8n-nodes-langchain.agent) is a multi-turn LLM driver with sub-nodes for the model, memory, tools, and an optional output parser. This skill is the deep guide to designing agents and the LangChain family around them. For the high-level "where an agent fits in a workflow" picture, see the n8n-workflow-patterns skill — this skill goes one level down into how to build it well. For node-type formats: in workflow JSON the LangChain nodes use the long @n8n/n8n-nodes-langchain. form (.agent, .lmChatOpenAi, .memoryBufferWindow, .outputParserStructured, .toolWorkflow, .toolHttpRequest, .toolCode). When you call getnode / validatenode, use the short form (nodes-langchain.agent). See n8n-mcp-tools-expert for the format rules. --- Pick the right node first Reaching for an Agent when the task is one-shot classification or extraction is the most common over-build. Decide before you wire anything: You need to… Use Why --- --- --- Call tools, reason over multiple turns, or hold memory AI Agent (.agent) The full loop: model + tools + memory + optional parser. Also a fine default when you'd rather standardize. One-shot text in → text out, no tools Basic LLM Chain (.chainLlm) No agent loop, easier to debug. Still accepts an outputParserStructured sub-node. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/n8n-agents--ba52bc