{ "workflow": { "id": 5976, "name": "Analyze sales territory performance with Bright Data MCP & GPT-4o", "views": 399, "recentViews": 0, "totalViews": 399, "createdAt": "2025-07-13T19:03:34.690Z", "description": "This workflow automatically analyzes sales territory performance, comparing revenue, win rates, and activity across regions. Remove the guesswork from territory planning and drive balanced growth.\n\n## Overview\n\nOn a weekly schedule, the workflow pulls CRM data for each territory, merges it with demographic and market size info scraped via Bright Data, and feeds everything into OpenAI for performance benchmarking. Outliers—both high and low performers—are highlighted in a Google Data Studio dashboard and summarized in a Slack message.\n\n## Tools Used\n\n- **n8n** – Orchestrates data collection and analysis\n- **CRM API** – Source of sales metrics by territory\n- **Bright Data** – Scrapes external market indicators (population, GDP, etc.)\n- **OpenAI** – Normalizes and benchmarks territories\n- **Google Sheets / Data Studio** – Stores and visualizes results\n- **Slack** – Sends the weekly summary\n\n## How to Install\n\n1. **Import the Workflow** into n8n.\n2. **Connect Your CRM** API credentials.\n3. **Configure Bright Data** credentials.\n4. **Set Up OpenAI** API key.\n5. **Authorize Google services & Slack**.\n6. **Customize Territory Definitions** in the Set node.\n\n## Use Cases\n\n- **Sales Leadership**: Rebalance territories based on potential.\n- **Revenue Operations**: Identify underserved regions.\n- **Financial Planning**: Allocate resources where ROI is highest.\n- **Incentive Design**: Reward reps fairly based on potential.\n\n## Connect with Me\n\n- **Website**: https://www.nofluff.online\n- **YouTube**: https://www.youtube.com/@YaronBeen/videos\n- **LinkedIn**: https://www.linkedin.com/in/yaronbeen/\n- **Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission)\n\n#n8n #automation #territorymanagement #salesanalytics #brightdata #openai #n8nworkflow #nocode #revenueops", "workflow": { "id": "LIOY5U6r4W8pmkrH", "meta": { "instanceId": "60046904b104f0f72b2629a9d88fe9f676be4035769f1f08dad1dd38a76b9480", "templateCredsSetupCompleted": true }, "name": "33 Analyze Territory Performance", "tags": [], "nodes": [ { "id": "0568c8de-17a3-4a9b-a3d7-cf9a9e937888", "name": "Weekly Territory Check", "type": "n8n-nodes-base.scheduleTrigger", "position": [ 0, 0 ], "parameters": { "rule": { "interval": [ { "field": "weeks", "triggerAtDay": [ 1 ], "triggerAtHour": 9 } ] } }, "typeVersion": 1.2 }, { "id": "065eaed2-28b0-4866-8173-5ec742a40e47", "name": "Prepare Request Params", "type": "n8n-nodes-base.set", "position": [ 200, 0 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "01dce1c2-299c-4def-a8d0-0194cae94ea2", "name": "url", "type": "string", "value": "example.com" } ] } }, "typeVersion": 3.4 }, { "id": "71435f74-f8bf-485e-b16e-0a9092dfdec7", "name": "Run Bright Data Scraper", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 440, 0 ], "parameters": { "text": "=From the following URL, extract fields the below fields.\n\nStore ID\nName\nAddress\nRegion\n\nURL: {{ $json.url }}", "options": {}, "promptType": "define", "hasOutputParser": true }, "typeVersion": 2 }, { "id": "26363bd7-51e1-4a01-a489-e16f4b877b52", "name": "Send Notification Email", "type": "n8n-nodes-base.gmail", "position": [ 1040, -240 ], "webhookId": "0c0b75ed-5603-4dd4-a38f-fdf95c10e71d", "parameters": { "sendTo": "user@example.com", "message": "=Hello Team!\n\nThe Regional sales data has updated in the google sheets. So go and check it out fast.\n\nRegards,\nYour Name", "options": {}, "subject": "Regional Sales data has updated", "emailType": "text" }, "credentials": { "gmailOAuth2": { "id": "credential-id", "name": "gmailOAuth2 Credential" } }, "typeVersion": 2.1 }, { "id": "bf4afd69-2309-4aed-89d5-34c704812bc9", "name": "Split Stores to Items", "type": "n8n-nodes-base.code", "position": [ 1040, 0 ], "parameters": { "jsCode": "// n8n Function Node\n// Purpose: Takes wrapped `output` array, returns each store as its own item\n\nconst results = [];\n\n// Grab the output array from first item\nconst stores = items[0].json.output;\n\n// Loop through each store\nfor (const store of stores) {\n results.push({ json: store });\n}\n\nreturn results;\n" }, "typeVersion": 2 }, { "id": "6c9ea046-2bc1-40e5-a95e-66a7afbc3bad", "name": "Update Regional Data Sheet", "type": "n8n-nodes-base.googleSheets", "position": [ 1260, 0 ], "parameters": { "columns": { "value": { "Region": "={{ $json.region }}", "Address": "={{ $json.address }}", "Store ID": "={{ $json.store_id }}", "Store name": "={{ $json.store_name }}", "Last updated": "={{ $json.last_updated }}", "Estimated sales": "={{ $json.estimated_sales }}" }, "schema": [ { "id": "Store ID", "type": "string", "display": true, "removed": false, "required": false, "displayName": "Store ID", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Store name", "type": "string", "display": true, "removed": false, "required": false, "displayName": "Store name", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Address", "type": "string", "display": true, "removed": false, "required": false, "displayName": "Address", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Region", "type": "string", "display": true, "removed": false, "required": false, "displayName": "Region", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Estimated sales", "type": "string", "display": true, "removed": false, "required": false, "displayName": "Estimated sales", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "Last updated", "type": "string", "display": true, "removed": false, "required": false, "displayName": "Last updated", "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/12hjlzSEhesN4r05t4Bq9w4ttEBzwXEmddDSJZ30N0tA/edit#gid=0", "cachedResultName": "Sheet1" }, "documentId": { "__rl": true, "mode": "list", "value": "12hjlzSEhesN4r05t4Bq9w4ttEBzwXEmddDSJZ30N0tA", "cachedResultUrl": "https://docs.google.com/spreadsheets/d/12hjlzSEhesN4r05t4Bq9w4ttEBzwXEmddDSJZ30N0tA/edit?usp=drivesdk", "cachedResultName": "Store data" } }, "credentials": { "googleSheetsOAuth2Api": { "id": "credential-id", "name": "googleSheetsOAuth2Api Credential" } }, "typeVersion": 4.6 }, { "id": "f4d0b4ea-5842-4730-8cf5-e6627ab54329", "name": "Bright Data MCP Tool", "type": "n8n-nodes-mcp.mcpClientTool", "position": [ 580, 280 ], "parameters": { "toolName": "scrape_as_markdown", "operation": "executeTool", "toolParameters": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Tool_Parameters', ``, 'json') }}" }, "credentials": { "mcpClientApi": { "id": "credential-id", "name": "mcpClientApi Credential" } }, "typeVersion": 1 }, { "id": "d3e22591-7bc7-494d-b917-934a4ffe419a", "name": "LLM Prompt Handler", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 400, 280 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o-mini" }, "options": {} }, "credentials": { "openAiApi": { "id": "credential-id", "name": "openAiApi Credential" } }, "typeVersion": 1.2 }, { "id": "f100c167-e28b-480a-87d5-6c143812a96a", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -40, -640 ], "parameters": { "color": 6, "width": 380, "height": 820, "content": "## ✅ **🔹 Section 1: Trigger & Prepare**\n\n**Nodes:**\n1️⃣ `Weekly Territory Check` *(Schedule Trigger)*\n2️⃣ `Prepare Request Params` *(Edit Fields)*\n\n**💡 What it does:**\n\n* ⏰ **Weekly Trigger**: The automation starts **automatically** on a set schedule — e.g., every Monday at 6 AM — so you don’t need to remember to run it.\n* 📝 **Prepare Request**: This step **sets up the input** — like choosing which regions to scrape, date filters, or any special parameters you want the scraper to use.\n\n**✨ Why it’s powerful for you:**\n\n* You never forget to update your data.\n* Anyone can **adjust input** (e.g., change region) **without touching the rest of the workflow**.\n* Perfect for **zero-code team edits**.\n\n---\n\n" }, "typeVersion": 1 }, { "id": "8548ff7c-748e-464c-87b4-39b1bd973bde", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 420, -1000 ], "parameters": { "color": 3, "width": 300, "height": 1180, "content": "## ✅ **🔹 Section 2: Smart Data Collection & Parsing**\n\n**Nodes:**\n3️⃣ `Run Bright Data Scraper` *(AI Agent)*\n— uses ⏬\n\n* `Bright Data MCP Tool` *(MCP Client)*\n* `LLM Prompt Handler` *(OpenAI Chat Model)*\n* `Fix/Format Scraper Output` *(Auto-fixing Output Parser)*\n* `Validation Assistant` *(OpenAI Chat Model1)*\n* `Final Output Formatter` *(Structured Output Parser)*\n\n**💡 What it does:**\n\n* 🤖 The **AI Agent** talks to the **Bright Data MCP Tool** to **scrape live data** from your target sites.\n* 🧠 The **LLM Prompt Handler** helps the AI figure out what pages to crawl, what data to look for, and how to handle tricky websites.\n* 🧹 The **Auto-fixer & Parsers** make sure your scraped data is always **clean JSON**, so you never get errors in the next steps.\n\n**✨ Why it’s powerful for you:**\n\n* This section handles all the **dirty work** of web scraping & fixing messy data.\n* You **don’t write code or worry about site changes** — the agent + model fix issues automatically.\n* If a page changes, the AI adjusts how it scrapes, so you’re not stuck fixing broken scrapers every week.\n\n---\n" }, "typeVersion": 1 }, { "id": "17a66823-bde6-4205-b1e4-3c6cd1969f2b", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ 1000, -780 ], "parameters": { "color": 5, "width": 400, "height": 960, "content": "## ✅ **🔹 Section 3: Process, Save & Notify**\n\n**Nodes:**\n4️⃣ `Split Stores to Items` *(Code)*\n5️⃣ `Update Regional Data Sheet` *(Google Sheets)*\n6️⃣ `Send Notification Email` *(Gmail)*\n\n**💡 What it does:**\n\n* ⚙️ The `Split Stores to Items` node **unwraps** the single big output from the scraper → breaks it into **one item per store** so you can work with them individually.\n* 📊 The `Update Regional Data Sheet` node **writes each store’s info** into Google Sheets — creating a clean, always-up-to-date record of all your stores & their sales.\n* 📧 The `Send Notification Email` node **alerts you automatically** if anything needs attention — for example, “Hey, Region North has 20% higher load than Region South. Reassign recommended!”\n\n" }, "typeVersion": 1 }, { "id": "666629f1-e070-49f3-a31b-1c6375c75734", "name": "Sticky Note5", "type": "n8n-nodes-base.stickyNote", "position": [ 1480, -780 ], "parameters": { "color": 7, "width": 380, "height": 240, "content": "## I’ll receive a tiny commission if you join Bright Data through this link—thanks for fueling more free content!\n\n### https://get.brightdata.com/1tndi4600b25" }, "typeVersion": 1 }, { "id": "f4e125ba-f6df-465d-8ce0-b0a6c996f3e0", "name": "Sticky Note9", "type": "n8n-nodes-base.stickyNote", "position": [ -1680, -640 ], "parameters": { "color": 4, "width": 1300, "height": 320, "content": "=======================================\n WORKFLOW ASSISTANCE\n=======================================\nFor any questions or support, please contact:\n Yaron@nofluff.online\n\nExplore more tips and tutorials here:\n - YouTube: https://www.youtube.com/@YaronBeen/videos\n - LinkedIn: https://www.linkedin.com/in/yaronbeen/\n=======================================\n" }, "typeVersion": 1 }, { "id": "ec9115e3-3507-44e6-b57c-37e5fe7ed046", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ -1680, -300 ], "parameters": { "color": 4, "width": 1289, "height": 2398, "content": "## 🎯 **Your Workflow: Territory Performance Analyzer & Reassigner**\n\n**✨ Purpose:**\nEvery week, this automation **scrapes fresh regional sales data**, splits it cleanly, saves it to a tracking sheet, and sends a smart email update if anything needs your attention — **all without you doing a thing!**\n\n---\n\n## ✅ **🔹 Section 1: Trigger & Prepare**\n\n**Nodes:**\n1️⃣ `Weekly Territory Check` *(Schedule Trigger)*\n2️⃣ `Prepare Request Params` *(Edit Fields)*\n\n**💡 What it does:**\n\n* ⏰ **Weekly Trigger**: The automation starts **automatically** on a set schedule — e.g., every Monday at 6 AM — so you don’t need to remember to run it.\n* 📝 **Prepare Request**: This step **sets up the input** — like choosing which regions to scrape, date filters, or any special parameters you want the scraper to use.\n\n**✨ Why it’s powerful for you:**\n\n* You never forget to update your data.\n* Anyone can **adjust input** (e.g., change region) **without touching the rest of the workflow**.\n* Perfect for **zero-code team edits**.\n\n---\n\n## ✅ **🔹 Section 2: Smart Data Collection & Parsing**\n\n**Nodes:**\n3️⃣ `Run Bright Data Scraper` *(AI Agent)*\n— uses ⏬\n\n* `Bright Data MCP Tool` *(MCP Client)*\n* `LLM Prompt Handler` *(OpenAI Chat Model)*\n* `Fix/Format Scraper Output` *(Auto-fixing Output Parser)*\n* `Validation Assistant` *(OpenAI Chat Model1)*\n* `Final Output Formatter` *(Structured Output Parser)*\n\n**💡 What it does:**\n\n* 🤖 The **AI Agent** talks to the **Bright Data MCP Tool** to **scrape live data** from your target sites.\n* 🧠 The **LLM Prompt Handler** helps the AI figure out what pages to crawl, what data to look for, and how to handle tricky websites.\n* 🧹 The **Auto-fixer & Parsers** make sure your scraped data is always **clean JSON**, so you never get errors in the next steps.\n\n**✨ Why it’s powerful for you:**\n\n* This section handles all the **dirty work** of web scraping & fixing messy data.\n* You **don’t write code or worry about site changes** — the agent + model fix issues automatically.\n* If a page changes, the AI adjusts how it scrapes, so you’re not stuck fixing broken scrapers every week.\n\n---\n\n## ✅ **🔹 Section 3: Process, Save & Notify**\n\n**Nodes:**\n4️⃣ `Split Stores to Items` *(Code)*\n5️⃣ `Update Regional Data Sheet` *(Google Sheets)*\n6️⃣ `Send Notification Email` *(Gmail)*\n\n**💡 What it does:**\n\n* ⚙️ The `Split Stores to Items` node **unwraps** the single big output from the scraper → breaks it into **one item per store** so you can work with them individually.\n* 📊 The `Update Regional Data Sheet` node **writes each store’s info** into Google Sheets — creating a clean, always-up-to-date record of all your stores & their sales.\n* 📧 The `Send Notification Email` node **alerts you automatically** if anything needs attention — for example, “Hey, Region North has 20% higher load than Region South. Reassign recommended!”\n\n**✨ Why it’s powerful for you:**\n\n* You get **fresh, structured data** to review any time.\n* No manual copy/paste → your team sees **live performance trends**.\n* Automated emails keep your sales leads or managers in the loop without extra work.\n\n---\n\n## 🎉 **🔑 How a Beginner Can Use This**\n\n✅ **No coding:** Everything runs with prebuilt nodes.\n✅ **Flexible:** Change your schedule, filters, or regions without breaking the flow.\n✅ **Scalable:** Easily add more stores, new regions, or extra tools — e.g., auto-publish to Airtable or a dashboard.\n✅ **Fully Automated:** Wake up Monday → check your sheet → know exactly where to adjust territories.\n\n---\n\n## ✅ **🌟 Visual Summary with Icons**\n\n| Section | Steps | Icon |\n| ---------------------- | -------------------------------------------------- | -------- |\n| **1️⃣ Trigger & Prep** | 🕒 Weekly schedule → 📝 set scrape parameters | ⏰ ✏️ |\n| **2️⃣ Collect & Fix** | 🤖 AI scrapes → 🧠 LLM helps → 🧹 Fix bad data | 🤖 🧠 🧹 |\n| **3️⃣ Process & Act** | 🔗 Split items → 📊 Save to Sheets → 📧 Send email | ⚙️ 📊 📧 |\n\n---\n\n## 🗂️ **Result:**\n\n**Zero-hassle, fully automated territory management**, designed for **non-coders**, **sales ops**, or any small business owner.\n\n---\n\n" }, "typeVersion": 1 }, { "id": "8d32a7e9-423f-4c22-8356-d59302e9afd5", "name": "Auto-fixing Output Parser", "type": "@n8n/n8n-nodes-langchain.outputParserAutofixing", "position": [ 720, 280 ], "parameters": { "options": {} }, "typeVersion": 1 }, { "id": "02d03b02-4186-4c9a-aa7c-dd9a0eddf39c", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 700, 500 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o-mini" }, "options": {} }, "credentials": { "openAiApi": { "id": "credential-id", "name": "openAiApi Credential" } }, "typeVersion": 1.2 }, { "id": "85bbb951-75ca-48ba-bd56-9763b0d8bd58", "name": "Structured Output Parser1", "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "position": [ 860, 500 ], "parameters": { "jsonSchemaExample": "[\n {\n \"store_id\": \"ST-101\",\n \"store_name\": \"SuperMart Downtown\",\n \"address\": \"123 Main St, Springfield\",\n \"region\": \"North Region\",\n \"estimated_sales\": 125000,\n \"last_updated\": \"2025-07-09\"\n },\n {\n \"store_id\": \"ST-102\",\n \"store_name\": \"SuperMart East Side\",\n \"address\": \"456 Maple Ave, Springfield\",\n \"region\": \"North Region\",\n \"estimated_sales\": 98000,\n \"last_updated\": \"2025-07-09\"\n },\n {\n \"store_id\": \"ST-103\",\n \"store_name\": \"SuperMart Riverside\",\n \"address\": \"789 River Rd, Rivertown\",\n \"region\": \"South Region\",\n \"estimated_sales\": 73000,\n \"last_updated\": \"2025-07-09\"\n },\n {\n \"store_id\": \"ST-104\",\n \"store_name\": \"SuperMart Uptown\",\n \"address\": \"321 Oak St, Rivertown\",\n \"region\": \"South Region\",\n \"estimated_sales\": 67000,\n \"last_updated\": \"2025-07-09\"\n },\n {\n \"store_id\": \"ST-105\",\n \"store_name\": \"SuperMart West End\",\n \"address\": \"555 Elm St, Lake City\",\n \"region\": \"West Region\",\n \"estimated_sales\": 115000,\n \"last_updated\": \"2025-07-09\"\n }\n]\n" }, "typeVersion": 1.2 } ], "active": false, "pinData": { "Run Bright Data Scraper": [ { "json": { "output": [ { "region": "North Region", "address": "123 Main St, Springfield", "store_id": "ST-101", "store_name": "SuperMart Downtown", "last_updated": "2025-07-09", "estimated_sales": 125000 }, { "region": "North Region", "address": "456 Maple Ave, Springfield", "store_id": "ST-102", "store_name": "SuperMart East Side", "last_updated": "2025-07-09", "estimated_sales": 98000 }, { "region": "South Region", "address": "789 River Rd, Rivertown", "store_id": "ST-103", "store_name": "SuperMart Riverside", "last_updated": "2025-07-09", "estimated_sales": 73000 }, { "region": "South Region", "address": "321 Oak St, Rivertown", "store_id": "ST-104", "store_name": "SuperMart Uptown", "last_updated": "2025-07-09", "estimated_sales": 67000 }, { "region": "West Region", "address": "555 Elm St, Lake City", "store_id": "ST-105", "store_name": "SuperMart West End", "last_updated": "2025-07-09", "estimated_sales": 115000 } ] } } ] }, "settings": { "executionOrder": "v1" }, "versionId": "e5410ef0-2611-442f-894a-4ebf7ab5ba3d", "connections": { "OpenAI Chat Model": { "ai_languageModel": [ [ { "node": "Auto-fixing Output Parser", "type": "ai_languageModel", "index": 0 } ] ] }, "LLM Prompt Handler": { "ai_languageModel": [ [ { "node": "Run Bright Data Scraper", "type": "ai_languageModel", "index": 0 } ] ] }, "Bright Data MCP Tool": { "ai_tool": [ [ { "node": "Run Bright Data Scraper", "type": "ai_tool", "index": 0 } ] ] }, "Split Stores to Items": { "main": [ [ { "node": "Update Regional Data Sheet", "type": "main", "index": 0 } ] ] }, "Prepare Request Params": { "main": [ [ { "node": "Run Bright Data Scraper", "type": "main", "index": 0 } ] ] }, "Weekly Territory Check": { "main": [ [ { "node": "Prepare Request Params", "type": "main", "index": 0 } ] ] }, "Run Bright Data Scraper": { "main": [ [ { "node": "Split Stores to Items", "type": "main", "index": 0 }, { "node": "Send Notification Email", "type": "main", "index": 0 } ] ] }, "Auto-fixing Output Parser": { "ai_outputParser": [ [ { "node": "Run Bright Data Scraper", "type": "ai_outputParser", "index": 0 } ] ] }, "Structured Output Parser1": { "ai_outputParser": [ [ { "node": "Auto-fixing Output Parser", "type": "ai_outputParser", "index": 0 } ] ] } } }, "lastUpdatedBy": 29, "workflowInfo": { "nodeCount": 17, "nodeTypes": { "n8n-nodes-base.set": { "count": 1 }, "n8n-nodes-base.code": { "count": 1 }, "n8n-nodes-base.gmail": { "count": 1 }, "n8n-nodes-base.stickyNote": { "count": 6 }, "n8n-nodes-base.googleSheets": { "count": 1 }, "n8n-nodes-mcp.mcpClientTool": { "count": 1 }, "@n8n/n8n-nodes-langchain.agent": { "count": 1 }, "n8n-nodes-base.scheduleTrigger": { "count": 1 }, "@n8n/n8n-nodes-langchain.lmChatOpenAi": { "count": 2 }, "@n8n/n8n-nodes-langchain.outputParserAutofixing": { "count": 1 }, "@n8n/n8n-nodes-langchain.outputParserStructured": { "count": 1 } } }, "status": "published", "user": { "name": "Yaron Been", "username": "yaron-nofluff", "bio": "Building AI Agents and Automations | Growth Marketer | Entrepreneur | Book Author & Podcast Host\n\nIf you need any help with Automations, feel free to reach out via linkedin:\nhttps://www.linkedin.com/in/yaronbeen/\n\nAnd check out my Youtube channel:\nhttps://www.youtube.com/@YaronBeen/videos", "verified": true, "links": [ "https://www.nofluff.online/automation-services/" ], "avatar": "https://gravatar.com/avatar/a4e4dcaa1f76ff5266bbf80e8df86d22efda890474c68f7796e72fd82e3f2375?r=pg&d=retro&size=200" }, "nodes": [ { "id": 18, "icon": "file:googleSheets.svg", "name": "n8n-nodes-base.googleSheets", "codex": { "data": { "alias": [ "CSV", "Sheet", "Spreadsheet", "GS" ], "resources": { "generic": [ { "url": 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