Skills for Real Engineers. Straight from my .agents directory.
39 skills
n8n-agents
Agent skill from
Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.
How to install n8n-agents
install n8n-agents
# install the n8n-skills pack with the skills CLI
npx skills add czlonkowski/n8n-skills# or add only this skill to Claude Code: copy the folder
skills/n8n-agents/
# into
~/.claude/skills/n8n-agents/ (all projects)
.claude/skills/n8n-agents/ (one project)
1# n8n Agents23The 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 **n8n-workflow-patterns** `ai_agent_workflow.md` — this skill goes one level down into *how to build it well*.45For 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 `get_node` / `validate_node`, use the **short** form (`nodes-langchain.agent`). See **n8n-mcp-tools-expert** for the format rules.67---89## Pick the right node first1011Reaching for an Agent when the task is one-shot classification or extraction is the most common over-build. Decide before you wire anything:1213| You need to… | Use | Why |14|---|---|---|15| 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. |16| One-shot text in → text out, no tools | **Basic LLM Chain** (`.chainLlm`) | No agent loop, easier to debug. Still accepts an `outputParserStructured` sub-node. |17…
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