Keith Uy This workflow corresponds to n8n.io template #16213 — we link there as the canonical source.
This workflow follows the Chainllm → Google Sheets recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "lkvu7etwF2YzDpPn",
"name": "Lead Enrichment + Personalized Icebreaker (Claude)",
"tags": [],
"nodes": [
{
"id": "e08f27e4-0054-443b-832f-119c36a4d194",
"name": "Sticky Note - Overview",
"type": "n8n-nodes-base.stickyNote",
"position": [
-240,
208
],
"parameters": {
"color": 4,
"width": 404,
"height": 456,
"content": "## Lead Enrichment + Personalized Icebreaker \n### **How to use:**\n1. Connect Anthropic (or other model of your choosing) and Google Sheets (or other database of your choosing).\n2. Point the trigger at a sheet with your lead list. Ensure your lead list has the following coluns: \n - `company`\n - `website` \n - `icebreaker` \n - `icp_fit_score` \n - `reasoning`\n*Note: icebreaker, icp_fit_score, and reasoning columns will be filled in by the AI. Company and Website are columns that should be filled in by your lead list builder.*\n3. Write your ICP into the Config node\n\nYou're good to run!"
},
"typeVersion": 1
},
{
"id": "4ae4fdd1-2114-4f88-9bcc-14139524982d",
"name": "Sticky Note - Setup",
"type": "n8n-nodes-base.stickyNote",
"position": [
496,
208
],
"parameters": {
"color": 5,
"width": 236,
"height": 328,
"content": "### Setup\nWrite your ICP here"
},
"typeVersion": 1
},
{
"id": "63c9b5c9-35a1-40ad-887b-245d1115496f",
"name": "Sticky Note - Fetch",
"type": "n8n-nodes-base.stickyNote",
"position": [
784,
208
],
"parameters": {
"color": 7,
"width": 440,
"height": 328,
"content": "### 1. Grab and read the site\nPulls the homepage and turns it into plain text, so the AI has the actual page to work from."
},
"typeVersion": 1
},
{
"id": "2d79bb65-e7fd-4d8e-91a8-511f653f55b6",
"name": "Sticky Note - AI",
"type": "n8n-nodes-base.stickyNote",
"position": [
1280,
208
],
"parameters": {
"color": 7,
"width": 332,
"height": 320,
"content": "### 2. Write the icebreaker and score\nAI returns JSON: the icebreaker, a 0-100 fit score, and a short reason."
},
"typeVersion": 1
},
{
"id": "e5e22eb7-9740-4d7d-836c-35827791e875",
"name": "Sticky Note - Write",
"type": "n8n-nodes-base.stickyNote",
"position": [
1648,
208
],
"parameters": {
"color": 7,
"width": 224,
"height": 328,
"content": "### 3. Write it back\nUpdates the same row by its row_number"
},
"typeVersion": 1
},
{
"id": "ab4aec5c-8428-4f69-a566-c4153675ea96",
"name": "New Lead Row",
"type": "n8n-nodes-base.googleSheetsTrigger",
"position": [
272,
384
],
"parameters": {
"event": "rowAdded",
"options": {},
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
},
"sheetName": {
"__rl": true,
"mode": "list",
"value": "Leads",
"cachedResultName": "Leads"
},
"documentId": {
"__rl": true,
"mode": "id",
"value": "YOUR_GOOGLE_SHEET_ID"
}
},
"typeVersion": 1
},
{
"id": "81d89179-71ed-4578-8617-1f4eb8bee3bb",
"name": "Fetch Prospect Website",
"type": "n8n-nodes-base.httpRequest",
"position": [
832,
384
],
"parameters": {
"url": "={{ $json.website }}",
"options": {
"timeout": 15000,
"redirect": {
"redirect": {}
},
"response": {
"response": {
"neverError": true
}
}
}
},
"typeVersion": 4.2
},
{
"id": "d968b9ba-6842-43f1-aed8-e51756224f6e",
"name": "Extract Page Text",
"type": "n8n-nodes-base.html",
"position": [
1104,
384
],
"parameters": {
"options": {
"trimValues": true
},
"operation": "extractHtmlContent",
"extractionValues": {
"values": [
{
"key": "page_text",
"cssSelector": "body"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "9f847e07-2112-467e-9bb8-4b23518b2ea3",
"name": "Generate Icebreaker + ICP Score",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
1328,
384
],
"parameters": {
"text": "=You write personalized cold-email icebreakers and score lead fit.\n\nICP DEFINITION:\n{{ $('Config (edit me)').item.json.icp_definition }}\n\nCOMPANY: {{ $('New Lead Row').item.json.company }}\nWEBSITE: {{ $('New Lead Row').item.json.website }}\n\nWEBSITE TEXT (truncated):\n{{ $json.page_text.slice(0, 4000) }}\n\nTASK:\n1. Write ONE personalized opening line (max 25 words) that references something specific and real from the website text. No flattery cliches, no \"I came across your website\". It should sound like a human who actually read the page.\n2. Score 0-100 how well this company fits the ICP above.\n3. Give one short sentence of reasoning for the score.\n\nIf the website text is empty or unusable, set icebreaker to \"[needs manual review]\" and icp_fit_score to 0.",
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.4
},
{
"id": "af68bbd3-021b-4acc-97d5-a278cc1d146b",
"name": "Anthropic Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"position": [
1280,
672
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "claude-sonnet-4-6",
"cachedResultName": "Claude Sonnet 4.6"
},
"options": {
"temperature": 0.4
}
},
"credentials": {
"anthropicApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.3
},
{
"id": "0904973f-7e16-4ccd-9119-fa4c258ca5bc",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
1488,
576
],
"parameters": {
"jsonSchemaExample": "{\n \"icebreaker\": \"Saw you just launched a done-for-you SEO retainer for dental clinics. Curious how you're filling those spots.\",\n \"icp_fit_score\": 82,\n \"reasoning\": \"Marketing agency running client outbound, mid-size, multiple service lines.\"\n}"
},
"typeVersion": 1.2
},
{
"id": "61efc480-3672-4d0f-a1e8-bfece0f4ef44",
"name": "Write Results to Sheet",
"type": "n8n-nodes-base.googleSheets",
"position": [
1712,
384
],
"parameters": {
"columns": {
"value": {
"reasoning": "={{ $json.output.reasoning }}",
"icebreaker": "={{ $json.output.icebreaker }}",
"row_number": "={{ $('New Lead Row').item.json.row_number }}",
"icp_fit_score": "={{ $json.output.icp_fit_score }}"
},
"mappingMode": "defineBelow",
"matchingColumns": [
"row_number"
]
},
"options": {},
"operation": "update",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "Leads",
"cachedResultName": "Leads"
},
"documentId": {
"__rl": true,
"mode": "id",
"value": "YOUR_GOOGLE_SHEET_ID"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 4.5
},
{
"id": "17e079dc-8db5-4886-895b-e7ba344332ee",
"name": "Config (edit me)",
"type": "n8n-nodes-base.set",
"position": [
560,
384
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "a1",
"name": "icp_definition",
"type": "string",
"value": "We sell cold-outbound lead-gen systems to US marketing agencies with 5-25 employees and roughly $500K-$5M in revenue. A strong fit runs outbound or cold email for clients, has no dedicated automation engineer, and shows signs of scaling (hiring, case studies, multiple service lines). A weak fit is a solo freelancer, an enterprise, or a company with no outbound motion."
}
]
}
},
"typeVersion": 3.4
},
{
"id": "f75b6fcb-67e7-4f5f-a3f0-d18329d9023c",
"name": "Sticky Note - Setup1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1232,
560
],
"parameters": {
"color": 5,
"width": 188,
"height": 248,
"content": "### AI Model\nFeel free to change this to your preferred model\n"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"binaryMode": "separate",
"executionOrder": "v1"
},
"versionId": "6c4244b9-071d-48d4-8bae-d4363c2c88e4",
"nodeGroups": [],
"connections": {
"New Lead Row": {
"main": [
[
{
"node": "Config (edit me)",
"type": "main",
"index": 0
}
]
]
},
"Config (edit me)": {
"main": [
[
{
"node": "Fetch Prospect Website",
"type": "main",
"index": 0
}
]
]
},
"Extract Page Text": {
"main": [
[
{
"node": "Generate Icebreaker + ICP Score",
"type": "main",
"index": 0
}
]
]
},
"Anthropic Chat Model": {
"ai_languageModel": [
[
{
"node": "Generate Icebreaker + ICP Score",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Fetch Prospect Website": {
"main": [
[
{
"node": "Extract Page Text",
"type": "main",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "Generate Icebreaker + ICP Score",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Generate Icebreaker + ICP Score": {
"main": [
[
{
"node": "Write Results to Sheet",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
anthropicApigoogleSheetsOAuth2Api
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
This workflow watches a Google Sheets lead list, fetches each new lead’s website, extracts page text, and uses Anthropic Claude to generate a personalized cold-email icebreaker plus an ICP fit score and reasoning, then writes the results back to the same sheet. Triggers when a…
Source: https://n8n.io/workflows/16213/ — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
This n8n automation is a complete LinkedIn Content Engine that turns simple topic ideas into fully written, visual, and scheduled posts. It features a "Human-in-the-Loop" design, meaning AI handles th
HTTP Request, Google Sheets, Chain Llm +5
This workflow automates the entire process of creating SEO-optimized meta titles and descriptions. It analyzes your webpage, spies on top-ranking competitors for the same keywords, and then uses a mul
Google Sheets, Output Parser Structured, HTTP Request +3
This workflow automates the process of generating and scheduling social media posts using content from a WordPress blog. It leverages advanced AI (OpenAI & Anthropic Claude), Google Sheets, and the Po
Google Sheets, Output Parser Structured, OpenAI +5
P6: Leads V2. Uses googleSheetsTrigger, jinaAi, chainLlm, outputParserStructured. Event-driven trigger; 21 nodes.
Google Sheets Trigger, Jina Ai, Chain Llm +5
> by Thomas Vie Thomas@pollup.net
HTTP Request, OpenAI Chat, Output Parser Structured +4