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 Impertio-Studio/n8n-Claude-Skill-Package --skill n8n-syntax-ai-nodesgit clone --depth 1 https://github.com/Impertio-Studio/n8n-Claude-Skill-PackageWrote 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/impertio-studio/n8n-claude-skill-package/n8n-syntax-ai-nodes)<a href="https://agentmods.dev/skills/impertio-studio/n8n-claude-skill-package/n8n-syntax-ai-nodes"><img src="https://agentmods.dev/badge/skills/impertio-studio/n8n-claude-skill-package/n8n-syntax-ai-nodes/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/impertio-studio/n8n-claude-skill-package/n8n-syntax-ai-nodes"><img src="https://agentmods.dev/badge/skills/impertio-studio/n8n-claude-skill-package/n8n-syntax-ai-nodes.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 | $0.00154 | $0.03145 |
| Opus 5 | $0.00077 | $0.01572 |
| Sonnet 5 | $0.00031 | $0.00629 |
| Haiku 4.5 | $0.00015 | $0.00314 |
Grade A, and why
n8n-syntax-ai-nodes 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
n8n AI/LLM Cluster Node System
n8n integrates with LangChain to provide advanced AI capabilities via a cluster node architecture — root nodes connected to specialized sub-nodes through typed connectors. Requires n8n v1.19.4+.
Quick Reference
Cluster Node Architecture
AI workflows in n8n use root nodes (agents, chains) connected to sub-nodes (models, memory, tools) through typed AI connectors. Root nodes NEVER work alone — they ALWAYS require at least one Chat Model sub-node.
┌─────────────────────────────────────────────────┐
│ ROOT NODE (Agent/Chain) │
│ ┌──────────┬──────────┬──────────┬───────────┐ │
│ │ai_language│ai_memory │ai_tool │ai_output │ │
│ │Model │ │ │Parser │ │
│ └────┬─────┴────┬─────┴────┬─────┴─────┬─────┘ │
└───────┼──────────┼──────────┼───────────┼───────┘
│ │ │ │
┌────▼────┐ ┌──▼───┐ ┌───▼────┐ ┌───▼──────┐
│Chat │ │Memory│ │Tool │ │Output │
│Model │ │Node │ │Node(s) │ │Parser │
└─────────┘ └──────┘ └────────┘ └──────────┘
AI Node Type Reference
| Category | Nodes | Purpose |
|---|---|---|
| Agents | Conversational, OpenAI Functions, Plan and Execute, ReAct, SQL, Tools Agent | Autonomous reasoning + tool use |
| Chains | Basic LLM, Summarization, Retrieval QA | Linear prompt-response pipelines |
| Specialized | Information Extractor, Text Classifier, Sentiment Analysis, LangChain Code | Task-specific AI operations |
| Chat Models | OpenAI, Anthropic, Azure OpenAI, Google Gemini, Groq, Ollama, Mistral, + more | LLM provider connections |
| Memory | Simple, Window Buffer, Token Buffer, Summary, PostgresChat, Redis, Xata, Zep | Conversation state persistence |
| Vector Stores | Pinecone, Qdrant, Supabase, PGVector, Chroma, Weaviate, In-Memory, Milvus, MongoDB Atlas, Azure AI Search, Redis | Vector similarity search backends |
| Embeddings | OpenAI, Cohere, Google, HuggingFace, Mistral, Ollama, Azure OpenAI | Text-to-vector conversion |
| Text Splitters | Character, Recursive Character, Token | Document chunking for RAG |
| Output Parsers | Structured, Auto-fixing, Item List | Response format enforcement |
| Retrievers | Vector Store, MultiQuery, Contextual Compression, Workflow | Document retrieval strategies |
| Tools | Calculator, Custom Code Tool, SearXNG, SerpApi, Wikipedia, Wolfram Alpha, Vector Store Q&A | Agent capabilities |
What ships with it
3 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.
- 11d ago First seen · 286 lines · 154 tokens per session scan A 1fe6c4c540b3
n8n-syntax-ai-nodes is a skill published in the GitHub repository Impertio-Studio/n8n-Claude-Skill-Package (3 stars, last pushed 2mo ago), licensed MIT. It adds 154 tokens to every session and 3,145 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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