n8n-skills is a collection of Claude Code skills and hooks that guide AI assistants in creating, validating, and deploying n8n workflows through the n8n-mcp server. Developers use it to automate workflow construction, configure nodes and expressions, resolve validation errors, and operate self-hosted n8n. The catalogue entries are its skills, hooks, plugins, and instruction.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add czlonkowski/n8n-skills --skill n8n-agentsgit clone --depth 1 https://github.com/czlonkowski/n8n-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/czlonkowski/n8n-skills/n8n-agents)<a href="https://agentmods.dev/skills/czlonkowski/n8n-skills/n8n-agents"><img src="https://agentmods.dev/badge/skills/czlonkowski/n8n-skills/n8n-agents/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/czlonkowski/n8n-skills/n8n-agents"><img src="https://agentmods.dev/badge/skills/czlonkowski/n8n-skills/n8n-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00156 | $0.06646 |
| Opus 5 | $0.00078 | $0.03323 |
| Sonnet 5 | $0.00031 | $0.01329 |
| Haiku 4.5 | $0.00016 | $0.00665 |
Grade A, and why
n8n-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to n8n-agents — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
n8n Agents
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 n8n-workflow-patterns ai_agent_workflow.md — 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 get_node / validate_node, 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. |
| Route a natural-language input to one of N branches | Text Classifier (.textClassifier) |
ONE node, N output handles, downstream wires directly into each. Not Agent + Switch. |
| Pull structured fields out of free text | Information Extractor (.informationExtractor) |
Purpose-built field extraction with a schema. |
| 3-way positive/neutral/negative split | Sentiment Analysis (.sentimentAnalysis) |
Built-in branch outputs. |
| Condense a long document | Summarization Chain (.chainSummarization) |
Map-reduce summarization built in. |
| Generate an image / audio / video | The provider's native single-call node (OpenAI, Gemini, ElevenLabs…) | NEVER wrap media generation in an Agent — see "Binary and the agent boundary". |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 313 lines · 156 tokens per session scan A 58f1425f041a
n8n-agents is a skill published in the GitHub repository czlonkowski/n8n-skills (6,210 stars, last pushed 8d ago), licensed MIT. It adds 156 tokens to every session and 6,646 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to n8n-agents, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
n8n-agents
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…
cordis-plugin-sofagent-inject
A sofagent plugin that loads company constraints through several prompt-building layers when the agent starts.
dspy-prompt-optimizer
Tunes prompts iteratively using reflection and success metrics. Use for: optimize this prompt, dspy tune, improve prompt with reflection, self-refine-loop.
n8n-structured-extraction
Extract or classify structured data from text/documents with an LLM in n8n using a real JSON schema. Use when a workflow needs structured fields out of unstructured input (invoice/contract/email extraction, classification), when deciding between an AI Agent and a dedicated extractor node, or when LLM JSON output is…
add-prompt
Scaffold a new MCP prompt template. Use when the user asks to add a prompt, create a reusable message template, or define a prompt for LLM interactions.
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.