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 PCT-BR/n8n-mcp --skill n8n-agentsgit clone --depth 1 https://github.com/PCT-BR/n8n-mcpWrote 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/pct-br/n8n-mcp/n8n-agents)<a href="https://agentmods.dev/skills/pct-br/n8n-mcp/n8n-agents"><img src="https://agentmods.dev/badge/skills/pct-br/n8n-mcp/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/pct-br/n8n-mcp/n8n-agents"><img src="https://agentmods.dev/badge/skills/pct-br/n8n-mcp/n8n-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.05649 |
| Opus 5 | $0.00078 | $0.02825 |
| Sonnet 5 | $0.00031 | $0.01130 |
| Haiku 4.5 | $0.00016 | $0.00565 |
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 10d 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
92% identical to n8n-agents — 33 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 — 282 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.
- 10d ago First seen · 282 lines · 156 tokens per session scan A 31168e2118e4
n8n-agents is a skill published in the GitHub repository PCT-BR/n8n-mcp (0 stars, last pushed 24d ago), licensed MIT. It adds 156 tokens to every session and 5,649 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to n8n-agents, differing in 33 lines, and is treated as a copy.
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