{ "meta": { "instanceId": "10215853c4801e5639deaf521f25f7bf4080db160528a7c77f6f3b15eb83e446", "templateCredsSetupCompleted": true }, "nodes": [ { "id": "5073ba97-93b4-4b08-9f20-a19256f08699", "name": "When clicking ‘Execute workflow’", "type": "n8n-nodes-base.manualTrigger", "position": [ -864, 336 ], "parameters": {}, "typeVersion": 1 }, { "id": "4ee3e1c9-24cb-468d-8f62-d5b2b7568fd5", "name": "Serper Search", "type": "n8n-nodes-base.httpRequest", "position": [ -416, 336 ], "parameters": { "url": "https://google.serper.dev/search", "method": "POST", "options": {}, "jsonBody": "={\n \"q\": \"{{ $json.search_query }}\",\n \"num\": 50\n}\n ", "sendBody": true, "sendHeaders": true, "specifyBody": "json", "authentication": "genericCredentialType", "genericAuthType": "httpHeaderAuth", "headerParameters": { "parameters": [ { "name": "Content-Type", "value": "application/json" } ] } }, "credentials": { "httpHeaderAuth": { "id": "credential-id", "name": "httpHeaderAuth Credential" } }, "typeVersion": 4.2 }, { "id": "05a255ed-4cde-47a6-a627-5bee768f2bb8", "name": "parse search results", "type": "n8n-nodes-base.code", "position": [ -192, 336 ], "parameters": { "jsCode": "// Serper returns JSON with \"organic\" results array\nconst results = items[0].json.organic || [];\nconst leads = [];\n\nfor (const r of results) {\n if (!r.link) continue;\n\n leads.push({\n json: {\n company_name: (r.title || \"\").replace(/\\|.*$/, \"\").trim(),\n website: r.link,\n snippet: r.snippet || \"\",\n status: \"raw_from_serper\"\n }\n });\n}\n\nreturn leads;\n" }, "typeVersion": 2 }, { "id": "d5d2fb78-c2e5-416e-8412-e059c8991462", "name": "Normalize leads", "type": "n8n-nodes-base.code", "position": [ 32, 336 ], "parameters": { "jsCode": "// Get all the leads from the previous node\nconst leads = $input.all().map((item) => item.json);\n\n// This is our new, smarter blocklist. It looks for patterns, not just names.\nconst junkKeywords = [\n 'top 10', 'top 20', 'best agencies', 'best companies', 'top rated',\n 'reviews of', 'list of', 'directory of', 'find the best', 'agencies in',\n 'firms in', 'jobs in', 'has anyone', 'reddit.com', 'youtube.com',\n 'indeed.com', 'glassdoor.com', 'clutch.co', 'designrush.com',\n 'goodfirms.co', 'themanifest.com'\n];\n\nconst normalizedLeads = leads\n .map((lead) => {\n // Clean up the company name and website URL\n let cleanCompanyName = lead.company_name\n .split(' - ')[0]\n .split(' • ')[0]\n .split(' (@')[0]\n .trim();\n \n let cleanWebsite = lead.website.trim();\n if (cleanWebsite.endsWith('/')) {\n cleanWebsite = cleanWebsite.slice(0, -1);\n }\n \n return {\n company_name: cleanCompanyName,\n website: cleanWebsite,\n original_title: lead.title, // Keep the original title for checking\n original_snippet: lead.snippet, // Keep the original snippet for checking\n status: \"normalized\"\n };\n })\n .filter((lead) => {\n // Now, check if the original title or website contains any junk keywords\n const textToSearch = (lead.original_title + ' ' + lead.website).toLowerCase();\n \n const isJunk = junkKeywords.some(keyword => textToSearch.includes(keyword));\n \n // Return true only if it's NOT junk\n return !isJunk;\n });\n\nreturn normalizedLeads;" }, "typeVersion": 2, "alwaysOutputData": false }, { "id": "5cebc6a1-58c0-4469-bd1b-180d832e9858", "name": "Loop Over Items", "type": "n8n-nodes-base.splitInBatches", "onError": "continueRegularOutput", "position": [ 256, 336 ], "parameters": { "options": {} }, "typeVersion": 3 }, { "id": "a6386eea-66ac-4885-a6ac-ac5654530b18", "name": "Wait", "type": "n8n-nodes-base.wait", "position": [ 480, 336 ], "webhookId": "8e6e905b-6613-4d83-9c4d-15dacbdd171d", "parameters": { "amount": 1.5 }, "typeVersion": 1.1 }, { "id": "264116f1-e587-4b8d-ae0d-69b99cc71530", "name": "Fetch Homepage", "type": "n8n-nodes-base.httpRequest", "onError": "continueRegularOutput", "position": [ 704, 336 ], "parameters": { "url": "=http://api.scraperapi.com", "options": { "redirect": { "redirect": {} }, "response": { "response": { "responseFormat": "text" } }, "allowUnauthorizedCerts": true }, "sendQuery": true, "queryParameters": { "parameters": [ { "name": "api_key", "value": "your-api-key" }, { "name": "url", "value": "={{ $json.website }}" }, { "name": "render", "value": "true" } ] } }, "typeVersion": 4.2, "alwaysOutputData": false }, { "id": "fc0f56fc-f326-490b-b010-4895992ff747", "name": "Google Gemini Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini", "position": [ 928, 512 ], "parameters": { "options": {} }, "credentials": { "googlePalmApi": { "id": "credential-id", "name": "googlePalmApi Credential" } }, "typeVersion": 1 }, { "id": "8f8fed6a-f6e1-445c-8281-b4f984d6475f", "name": "Edit Fields", "type": "n8n-nodes-base.set", "position": [ -640, 336 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "8b63ae6a-cf26-4a3e-882d-d2e6cdae34b1", "name": "search_query", "type": "string", "value": "Marketing and Advertising agencies in Canada" } ] } }, "typeVersion": 3.4 }, { "id": "b0fd9c92-dc54-4d09-95fc-87d13f873025", "name": "HTTP Request1", "type": "n8n-nodes-base.httpRequest", "onError": "continueRegularOutput", "position": [ -624, 688 ], "parameters": { "url": "http://api.scraperapi.com", "options": { "redirect": { "redirect": {} }, "response": { "response": { "responseFormat": "text" } }, "allowUnauthorizedCerts": true }, "sendQuery": true, "queryParameters": { "parameters": [ { "name": "api_key", "value": "your-api-key" }, { "name": "url", "value": "={{ $json.Detail }}" }, { "name": "render", "value": "true" } ] } }, "typeVersion": 4.2 }, { "id": "4134a602-e739-4237-8260-b835da530926", "name": "Google Gemini Chat Model1", "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini", "position": [ -400, 864 ], "parameters": { "options": {} }, "credentials": { "googlePalmApi": { "id": "credential-id", "name": "googlePalmApi Credential" } }, "typeVersion": 1 }, { "id": "e013ce45-9c3c-4e96-86fd-2dde63234ed2", "name": "output combiner", "type": "n8n-nodes-base.code", "position": [ -48, 688 ], "parameters": { "jsCode": "// This node receives all the separate findings from the deep scan\nconst allItems = $input.all();\nconst aggregatedResult = {\n emails: [],\n phones: [],\n contact_form_present: false,\n summaries: []\n};\n\n// Loop through each finding (from the about page, contact page, etc.)\nfor (const item of allItems) {\n // 1. Get the raw string from the 'output' field.\n const rawOutput = item.json.output;\n if (!rawOutput) continue; // Skip if the AI returned nothing.\n\n try {\n // 2. Clean the string to remove markdown and get the pure JSON.\n const jsonString = rawOutput.replace(/```json\\n|\\n```/g, '').trim();\n if (!jsonString) continue; // Skip if the string is empty after cleaning.\n\n // 3. Parse the string into a usable object. THIS IS THE STEP WE MISSED.\n const data = JSON.parse(jsonString);\n\n // 4. Now, the original aggregation logic will work on the clean data.\n if (data.emails && data.emails.length > 0) {\n aggregatedResult.emails.push(...data.emails);\n }\n if (data.phones && data.phones.length > 0) {\n aggregatedResult.phones.push(...data.phones);\n }\n if (data.contact_form_present) {\n aggregatedResult.contact_form_present = true;\n }\n if (data.summary) {\n aggregatedResult.summaries.push(data.summary);\n }\n } catch (error) {\n // This will prevent the workflow from crashing if the AI returns bad JSON.\n console.error(\"Could not parse AI output:\", item.json.output, error);\n }\n}\n\n// IMPORTANT: Remove any duplicate emails or phone numbers\naggregatedResult.emails = [...new Set(aggregatedResult.emails)];\naggregatedResult.phones = [...new Set(aggregatedResult.phones)];\n\n// Return a single, clean object with all the combined results\nreturn [{ json: aggregatedResult }];" }, "typeVersion": 2 }, { "id": "d235c3e4-b133-49b3-8834-f074ccce2f0c", "name": "Merge", "type": "n8n-nodes-base.merge", "position": [ 640, 1008 ], "parameters": { "mode": "combine", "options": {}, "combineBy": "combineByPosition", "numberInputs": 3 }, "typeVersion": 3.2 }, { "id": "31692d8d-b7ed-4cea-b436-5cfd6e98084e", "name": "Merge1", "type": "n8n-nodes-base.merge", "position": [ 416, 1120 ], "parameters": { "mode": "chooseBranch", "useDataOfInput": 2 }, "typeVersion": 3.2 }, { "id": "04d6675a-206c-4b9a-985b-49b823251389", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -928, 160 ], "parameters": { "color": 5, "width": 656, "height": 336, "content": "## 1. Search & Initialize\nDefines the target niche and executes a Google Search to get raw results." }, "typeVersion": 1 }, { "id": "06c1c953-b26b-4da6-9e4b-e87e1595bdcf", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ -256, 176 ], "parameters": { "color": 5, "width": 672, "height": 320, "content": "## 2. Clean & Loop\nFilters out directories (like Yelp) to save AI credits, then starts a polite loop (1.5s delay) to process leads one by one." }, "typeVersion": 1 }, { "id": "23f8cb2b-561f-415c-9a54-c855a7d8200d", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [ 464, 176 ], "parameters": { "color": 5, "width": 944, "height": 480, "content": "## 3. Homepage Analysis\nScrapes the main website. The AI Agent reads the HTML to find hidden \"Contact Us\" sub-pages and social media links." }, "typeVersion": 1 }, { "id": "17212719-41ce-46d5-879c-7faaba77935e", "name": "Format output", "type": "n8n-nodes-base.code", "position": [ 1280, 336 ], "parameters": { "jsCode": "// Get the input string from the previous node\nconst rawOutput = items[0].json.output;\n\n// 1. Clean the string to remove the Markdown \"```json\" fences\n// This isolates the pure JSON array text.\nconst jsonString = rawOutput.replace(/```json\\n|\\n```/g, '');\n\n// 2. Parse the clean string into a JavaScript array\nconst dataArray = JSON.parse(jsonString);\n\n// 3. Return the data so n8n creates a separate item for each object\n// This is the key step to get separate objects as the output.\nreturn dataArray.map(item => {\n return {\n json: item\n };\n});" }, "typeVersion": 2 }, { "id": "6a1a89ce-61dd-4c4f-be86-af3164ee49f7", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ -880, 560 ], "parameters": { "color": 4, "width": 1008, "height": 384, "content": "## 4. Contact Page Scan\nIf a specific \"Contact Us\" page was found, this branch visits it to extract emails and phone numbers hidden in the code." }, "typeVersion": 1 }, { "id": "47384a1c-cff3-464c-9c18-7ef37d177f66", "name": "Linked in Details", "type": "n8n-nodes-base.code", "position": [ 416, 928 ], "parameters": { "jsCode": "// Get the raw output string from the AI Agent\nconst rawOutput = items[0].json.output;\n\n// Parse the JSON string into a real JavaScript object\nconst parsedData = JSON.parse(rawOutput);\n\n// Return the clean object for the next node to use\nreturn {\n json: parsedData\n};" }, "typeVersion": 2 }, { "id": "19e0c312-ce38-4dff-a0cb-3ff2682c86fd", "name": "LinkedIN scraper", "type": "n8n-nodes-base.httpRequest", "position": [ -368, 1152 ], "parameters": { "url": "=https://www.linkedin.com/", "options": { "redirect": { "redirect": {} }, "response": { "response": { "responseFormat": "text" } } }, "sendQuery": true, "queryParameters": { "parameters": [ { "name": "api_key", "value": "your-api-key" }, { "name": "type", "value": "company" }, { "name": "linkId", "value": "={{ $json.linkId }}" } ] } }, "typeVersion": 4.2 }, { "id": "eb636666-ad63-4bf3-b771-04607e07c866", "name": "convert scrapable link", "type": "n8n-nodes-base.code", "position": [ -592, 1152 ], "parameters": { "jsCode": "// Get the full LinkedIn URL from the incoming item\nconst fullUrl = items[0].json.Detail;\n\n// This logic safely splits the URL by '/' and gets the last part,\n// even if there's a trailing slash.\nconst parts = fullUrl.split('/').filter(part => part); // Split and remove empty parts\nconst linkId = parts[parts.length - 1];\n\n// Add the new 'linkId' field to the data\nitems[0].json.linkId = linkId;\n\n// Pass the updated item to the next node\nreturn items;" }, "typeVersion": 2 }, { "id": "6ddab130-b3db-42ee-9ff5-0d6e63c96abb", "name": "filter redirecting links", "type": "n8n-nodes-base.if", "position": [ -848, 688 ], "parameters": { "options": {}, "conditions": { "options": { "version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "or", "conditions": [ { "id": "6ab66308-bd6a-4545-ad5a-5de3ab4766aa", "operator": { "name": "filter.operator.equals", "type": "string", "operation": "equals" }, "leftValue": "={{ $json['Contact Method'] }}", "rightValue": "Contact Page Link" }, { "id": "1f729687-7ba3-47ad-8e58-8e7c3bbb2fde", "operator": { "name": "filter.operator.equals", "type": "string", "operation": "equals" }, "leftValue": "={{ $json['Contact Method'] }}", "rightValue": "About Page Link" }, { "id": "fe1d229e-e833-44c9-ad6e-07727dda37ff", "operator": { "name": "filter.operator.equals", "type": "string", "operation": "equals" }, "leftValue": "={{ $json['Contact Method'] }}", "rightValue": "Careers Page Link" }, { "id": "faba28a0-d13b-4891-af75-a7f6358cc93b", "operator": { "name": "filter.operator.equals", "type": "string", "operation": "equals" }, "leftValue": "={{ $json['Contact Method'] }}", "rightValue": "LinkedIn Profile" }, { "id": "63572f9d-3f08-4e84-8fd3-0f26933b7ef0", "operator": { "name": "filter.operator.equals", "type": "string", "operation": "equals" }, "leftValue": "={{ $json[\"Contact Method\"] }}", "rightValue": "Contact Form Link" } ] } }, "typeVersion": 2.2 }, { "id": "ec33ee8b-2175-4b78-b922-54f22fa8636b", "name": "Filter Linked In specific link", "type": "n8n-nodes-base.if", "position": [ -816, 1152 ], "parameters": { "options": {}, "conditions": { "options": { "version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "18501573-a673-401a-a0e1-6ce83883ef11", "operator": { "name": "filter.operator.equals", "type": "string", "operation": "equals" }, "leftValue": "={{ $json['Contact Method'] }}", "rightValue": "LinkedIn Profile" } ] } }, "typeVersion": 2.2 }, { "id": "725ff34c-786a-436a-a7f1-d5fe01890f4c", "name": "Append row in sheet", "type": "n8n-nodes-base.googleSheets", "position": [ 1088, 1024 ], "parameters": { "columns": { "value": { "Phone": "={{ $json.phone_string }}", "Emails": "={{ $json.email_string }}", "Website": "={{ $json.website_url }}", "industry": "={{ $json.industry }}", "Company Name": "={{ $('Loop Over Items').item.json.company_name }}", "Linked Summary": "={{ $json.linkedin_summary }}", "employee count": "={{ $json.employee_count }}" }, "schema": [ { "id": "Company Name", "type": "string", "display": true, "required": false, "displayName": "Company Name", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Name", "type": "string", "display": true, "removed": true, "required": false, "displayName": "Name", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Linked Summary", "type": "string", "display": true, "required": false, "displayName": "Linked Summary", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "employee count", "type": "string", "display": true, "required": false, "displayName": "employee count", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "industry", "type": "string", "display": true, "required": false, "displayName": "industry", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "headquarter", "type": "string", "display": true, "required": false, "displayName": "headquarter", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Emails", "type": "string", "display": true, "required": false, "displayName": "Emails", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Website", "type": "string", "display": true, "required": false, "displayName": "Website", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Phone", "type": "string", "display": true, "required": false, "displayName": "Phone", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Source", "type": "string", "display": true, "required": false, "displayName": "Source", "defaultMatch": false, "canBeUsedToMatch": true } ], "mappingMode": "defineBelow", "matchingColumns": [], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {}, "operation": "append", "sheetName": { "__rl": true, "mode": "list", "value": "gid=0", "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1-YOUR_AWS_SECRET_KEY_HERE-g/edit#gid=0", "cachedResultName": "Sheet1" }, "documentId": { "__rl": true, "mode": "list", "value": "1-YOUR_AWS_SECRET_KEY_HERE-g", "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1-YOUR_AWS_SECRET_KEY_HERE-g/edit?usp=drivesdk", "cachedResultName": "Linked in details" } }, "credentials": { "googleSheetsOAuth2Api": { "id": "credential-id", "name": "googleSheetsOAuth2Api Credential" } }, "typeVersion": 4.7 }, { "id": "fce4e3f4-ceba-401e-a7ec-2c338ffeb134", "name": "Send a message", "type": "n8n-nodes-base.gmail", "position": [ 320, 576 ], "webhookId": "5b42f61d-58e3-420c-840f-b256e7d5cce7", "parameters": { "sendTo": "user@example.com", "message": "=The Extraction has been completed and google sheets has been filled with the extracted data from the scrapers", "options": {}, "subject": "Extraction Completed", "emailType": "text" }, "credentials": { "gmailOAuth2": { "id": "credential-id", "name": "gmailOAuth2 Credential" } }, "typeVersion": 2.1 }, { "id": "789e3dc4-dfe3-4f58-ba94-aed8cfe5fe85", "name": "Sticky Note6", "type": "n8n-nodes-base.stickyNote", "position": [ -832, 1040 ], "parameters": { "color": 4, "width": 960, "height": 464, "content": "## 5. LinkedIn Enrichment\nIf a LinkedIn profile was found, this branch scrapes it to gather company stats (Size, Industry, HQ)." }, "typeVersion": 1 }, { "id": "829726fc-30d2-4374-b922-c2c665a1db2a", "name": "Sticky Note8", "type": "n8n-nodes-base.stickyNote", "position": [ 272, 784 ], "parameters": { "color": 4, "width": 992, "height": 496, "content": "## 6. Merge & Save\nConsolidates data from all branches, formats lists (like multiple emails) into text, and saves to Google Sheets." }, "typeVersion": 1 }, { "id": "ab1a2b35-716d-41ff-b1f4-2fcf56499500", "name": "Sticky Note9", "type": "n8n-nodes-base.stickyNote", "position": [ -1744, 144 ], "parameters": { "width": 656, "height": 592, "content": "## 🕵️ Overview\nThis is an \"Agentic\" lead generation workflow that behaves like a human researcher, not just a dumb scraper. It turns a simple Google Search into a verified lead list.\n\n1. **Smart Search:** It searches Google for your niche (e.g., \"Web Agencies in Toronto\") \n2. **Homepage Scout:** An AI Agent visits every company website to identify the *correct* \"Contact Us\" page and look for LinkedIn profiles.\n3. **Deep Dive (Parallel Processing):**\n - **Branch A:** Visits the specific Contact page to extract hidden emails and phone numbers.\n - **Branch B:** Visits the LinkedIn company profile to grab headcount, industry, and location data.\n4. **Aggregation:** Merges all data, formats it into a clean row, and saves it to Google Sheets.\n\n## ⚙️ Setup steps\n1. **Credentials:** You need **Serper** (Search), **ScraperAPI** (Proxy/Bypassing), and **Google Gemini** (Intelligence).\n2. **Google Sheet:** Create a new sheet with these exact headers in Row 1:\n `Company Name`, `Linked Summary`, `employee count`, `industry`, `Emails`, `Phone`, `Website`\n3. **Configuration:**\n - Open the **Edit Fields** node (Start).\n - Enter your keyword in the `search_query` field.\n4. **Run:** The workflow uses a loop with a 1.5s delay to be polite to servers. A batch of 50 leads takes ~10-15 mins." }, "typeVersion": 1 }, { "id": "af061e90-b596-4ef1-b215-2e13acdcbd4e", "name": "Google Gemini Chat Model3", "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini", "position": [ -144, 1344 ], "parameters": { "options": {} }, "credentials": { "googlePalmApi": { "id": "credential-id", "name": "googlePalmApi Credential" } }, "typeVersion": 1 }, { "id": "18292c48-65b4-4d3e-a92c-eccea81cb5af", "name": "Linked In Agent", "type": "@n8n/n8n-nodes-langchain.chainLlm", "position": [ -144, 1152 ], "parameters": { "text": "=Analyze this page and give me the Details of this linked profile or company : {{ $json.data }}", "batching": {}, "messages": { "messageValues": [ { "message": "=## System Prompt You are an expert at analyzing the data from a LinkedIn company page. Your goal is to extract key business information. ## Your Task Analyze the provided data and extract the following details. If a detail is not present, use an empty string \"\" or null. ## Output Format You MUST output a single, clean JSON object. Do not use markdown. { \"linkedin_summary\":, \"employee_count\": , \"industry\": , \"headquarters\": \"email\":. \"contact details\":, \"adresss\":, }" } ] }, "promptType": "define" }, "typeVersion": 1.7 }, { "id": "9347b69b-e5d1-4237-84ac-75188bfdc6f9", "name": "Contact Page Scan Agent", "type": "@n8n/n8n-nodes-langchain.chainLlm", "position": [ -400, 688 ], "parameters": { "text": "=Analyze the following HTML and extract all contact details: {{ $json.data }}", "batching": {}, "messages": { "messageValues": [ { "message": "=## System Prompt\nYou are a specialist data extractor. Your only goal is to find email addresses and phone numbers within the provided HTML of a company's contact page.\n\n## Your Task\n1. Meticulously scan the entire HTML for any email addresses (mailto: links or plain text) and any phone numbers (tel: links or plain text in various formats).\n2. If you cannot find any direct emails or phone numbers, check for the presence of a contact form (look for
tags associated with text like \"contact us,\" \"send a message,\" or \"get in touch\").\n3. You MUST output your findings as a single, clean JSON object. Do not provide any text, explanation, or markdown fences before or after the JSON.\n\n## Output Format\nYour entire output must be ONLY the JSON object, structured exactly like this:\n{\n \"emails\": [\"email@example.com\", \"another@example.com\"],\n \"phones\": [\"+1-555-123-4567\", \"(555) 987-6543\"],\n \"contact_form_present\": true,\n \"summary\": \"Found two emails and a contact form on the page.\"\n}\n\n## Rules\n- If no emails are found, the \"emails\" array should be empty: [].\n- If no phone numbers are found, the \"phones\" array should be empty: [].\n- If no contact form is detected, \"contact_form_present\" should be false.\n- The summary should be a brief, one-sentence description of what you found." } ] }, "promptType": "define" }, "typeVersion": 1.7 }, { "id": "a7d816ce-cc0b-43c3-81bf-fdf89ca6973c", "name": "Home Page Analyst", "type": "@n8n/n8n-nodes-langchain.chainLlm", "position": [ 928, 336 ], "parameters": { "text": "=Analyze the following HTML and extract all contact details: {{ $json.data }}", "batching": {}, "messages": { "messageValues": [ { "message": "=## System Prompt\nYou are an expert-level Data Extraction Bot. Your sole purpose is to meticulously analyze raw HTML content from a company's webpage and extract every possible piece of contact information. You must be thorough, accurate, and systematic in your approach.\n\n## Your Task\n\nYou will be given raw HTML as input. Your task is to parse this HTML and identify all contact details. You must then structure this information into a single, clean Markdown table.\n\n## Information to Extract\n\nYou must find the following types of information:\n\nContact-Related Page Links:\n\nScour all tags.\n\nIdentify any link that helps a user contact or learn more about the company.\n\nLook for anchor text containing keywords like: Contact, About Us, Support, Connect, Get in Touch, Help, Locations, Careers, Press, Inquiries.\n\nExtract the full, absolute URL from the href attribute. If the link is relative (e.g., /contact), construct the full URL based on the main website's domain.\n\nCapture the link's anchor text (the visible text of the link).\n\nEmail Addresses:\n\nSearch for all mailto: links within href attributes.\n\nScan the entire text content of the HTML for email address patterns (e.g., name@domain.com). Be thorough, as they are often not in tags.\n\nPhone Numbers:\n\nSearch for all tel: links within href attributes.\n\nScan the entire text content for common phone number formats, including international codes, extensions, and different uses of parentheses, dashes, and spaces.\n\nPhysical Addresses:\n\nLook for structured address formats, often found in the page