# templates

name: Extract & Enrich LinkedIn Comments to Leads with Apify → Google Sheets/CSV
author_name: Saverflow AI
author_username: saverflowai
nodes_used: ["n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.code"]
categories: []
created_at: 2025-06-04T02:39:54.846Z
updated_at: 2025-06-04T02:39:54.846Z
url: https://n8n.io/workflows/4635
scraped_at: 2025-06-19 22:07:56
description:
  🚀 LinkedIn Comments to Leads Extractor & Enricher (Apify) → Google Sheets / CSV

  Overview

  Automate LinkedIn lead generation by scraping comments from targeted posts and enriching profiles with detailed data

  This n8n workflow automatically extracts leads from LinkedIn post comments using Apify's powerful scrapers (no LinkedIn login required), enriches the data with additional profile information, and exports everything to Google Sheets or CSV format.

  ✨ Key Features

  🔍 No Login Required: Scrape LinkedIn data without sharing credentials
  💰 Cost-Effective: First 1,000 comments are free with Apify
  📊 Data Enrichment: Enhance basic comment data with full profile details
  📈 Export Options: Choose between Google Sheets or CSV output
  🎯 Targeted Scraping: Focus on specific posts for quality leads

  🛠️ Apify Scrapers Used

  1. LinkedIn Post Comments Scraper
  Tool**: LinkedIn Post Comments, Replies, Engagements Scraper | No Cookies
  Pricing**: $5.00 per 1,000 results
  Function**: Extracts all comments and engagement data from specified LinkedIn posts

  2. LinkedIn Profile Batch Scraper
  Tool**: LinkedIn Profile Details Batch Scraper (No Cookies Required)
  Pricing**: $5.00 per 1,000 results
  Function**: Enriches scraped profiles with detailed information

  &gt; 💡 Free Tier: Apify provides 1,000 free scraped comments to get you started!

  📋 Prerequisites

  Required API Credentials

  Apify Token
     Add your APIFY_TOKEN to the workflow credentials
     Get your token from Apify Console

  Google Sheets Credentials (if using Sheets export)
     Configure OAuth credentials for Google Sheets integration
     Follow n8n's Google Sheets setup guide

  🔄 Workflow Process

  Default Mode: Form-Based Execution

  Manual Trigger → Launches the workflow
  Form Submission → User-friendly form for inputting LinkedIn post URLs
  Comment Scraping → Apify extracts all comments from specified posts
  Profile Enrichment → Additional profile data gathered for each commenter
  Data Processing → Creates unique, enriched lead list
  Google Sheets Export → Automatically populates your spreadsheet

  Result: You'll be redirected to a Google Sheets document containing all enriched leads

  Alternative Mode: CSV Export

  For users preferring CSV output:

  Disable: Form trigger nodes
  Enable: Manual trigger node
  Disable: Google Sheets export nodes
  Enable: CSV download nodes
  Configure: Add post IDs/URLs in "Set manual fields" node
  Execute: Run workflow and download CSV from the CSV node

  📊 Output Data Structure

  Your exported data will include:

  Basic Info**: Name, headline, location
  Profile Details**: Company, position, industry
  Engagement Data**: Comment content, engagement metrics
  Contact Info**: Available profile links and connections
  Enriched Data**: Additional profile insights from Apify

  💡 Pro Tips

  Quality over Quantity**: Target posts with high-quality, relevant engagement
  Monitor Costs**: Track your Apify usage to stay within budget
  Data Hygiene**: Regularly clean and deduplicate your lead lists
  Compliance**: Ensure your scraping activities comply with LinkedIn's terms of service

  🆘 Troubleshooting

  Common Issues:
  Authentication Errors**: Verify your Apify token is correctly configured
  Empty Results**: Check that your LinkedIn post URLs are valid and public
  Export Failures**: Ensure Google Sheets credentials are properly set up

  Need Help? Contact Saverflow.ai for support and custom workflow development.

workflow_json:
  {"meta":{"instanceId":"30e5848b346369e688a90590edfdf72f9e085f2b2ed50b31cf94f3bfb8c384ed","templateId":"4635"},"nodes":[{"id":"66c32da3-373c-42a9-bf51-5bec1fd6c4cd","name":"Trigger manually","type":"n8n-nodes-base.manualTrigger","disabled":true,"position":[1240,1360],"parameters":{},"typeVersion":1},{"id":"6a0decfe-9539-4c10-9474-0031fa91dfc5","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[380,240],"parameters":{"color":5,"width":1620,"height":1000,"content":"# 🚀 LinkedIn Comments to Leads Extractor & Enricher (Apify) → Google Sheets / CSV\n\n## Overview\n\n**Automate LinkedIn lead generation by scraping comments from targeted posts and enriching profiles with detailed data**\n\nThis n8n workflow automatically extracts leads from LinkedIn post comments using Apify's powerful scrapers (no LinkedIn login required), enriches the data with additional profile information, and exports everything to Google Sheets or CSV format.\n\n---\n\n## ✨ Key Features\n\n- 🔍 **No Login Required**: Scrape LinkedIn data without sharing credentials\n- 💰 **Cost-Effective**: First 1,000 comments are free with Apify\n- 📊 **Data Enrichment**: Enhance basic comment data with full profile details\n- 📈 **Export Options**: Choose between Google Sheets or CSV output\n- 🎯 **Targeted Scraping**: Focus on specific posts for quality leads\n\n---\n\n## 🛠️ Apify Scrapers Used\n\n### 1. LinkedIn Post Comments Scraper\n- **Tool**: [LinkedIn Post Comments, Replies, Engagements Scraper | No Cookies](https://apify.com/apimaestro/linkedin-post-comments-replies-engagements-scraper-no-cookies/api/python)\n- **Pricing**: $5.00 per 1,000 results\n- **Function**: Extracts all comments and engagement data from specified LinkedIn posts\n\n### 2. LinkedIn Profile Batch Scraper\n- **Tool**: [LinkedIn Profile Details Batch Scraper (No Cookies Required)](https://apify.com/apimaestro/linkedin-profile-batch-scraper-no-cookies-required)\n- **Pricing**: $5.00 per 1,000 results\n- **Function**: Enriches scraped profiles with detailed information\n\n> 💡 **Free Tier**: Apify provides 1,000 free scraped comments to get you started!\n\n---\n"},"typeVersion":1},{"id":"e206b173-eb8a-4721-8251-8b0ebb2aa38f","name":"On form submission","type":"n8n-nodes-base.formTrigger","position":[1300,1760],"webhookId":"a81d04b5-169a-4368-b925-91fb0660b6b2","parameters":{"options":{"ignoreBots":true,"buttonLabel":"Submit","appendAttribution":false,"respondWithOptions":{"values":{"redirectUrl":"=https://docs.google.com/spreadsheets/u/0/","respondWith":"redirect"}}},"formTitle":"Linkedin Posts Comments Leads Scraper","formFields":{"values":[{"fieldType":"textarea","fieldLabel":"Post IDs/URLs","placeholder":"e.g. 7334385757769416704, https://www.linkedin.com/feed/update/urn:li:activity:7334385757769416704/","requiredField":true},{"fieldType":"number","fieldLabel":"How many comments you want to scrape?","placeholder":"Default: 100"}]},"responseMode":"lastNode","formDescription":"=<div style=\"font-family: Arial, sans-serif; line-height: 1.8; color: #333; word-wrap: break-word; overflow-wrap: break-word;\">\n\n<p style=\"margin-bottom: 20px;\"><strong style=\"color: #0077b5;\">🔸 Step 1: Add Posts URLs</strong></p>\n<p style=\"margin-bottom: 20px;\">The workflow accepts various LinkedIn post identifier formats:</p>\n\n<p style=\"margin-bottom: 18px;\"><strong>📊 Numeric post IDs:</strong><br>\n<span style=\"font-family: monospace; background: #f5f5f5; padding: 4px 6px; word-break: break-all; margin-top: 8px; display: inline-block;\">7334385757769416704</span></p>\n\n<p style=\"margin-bottom: 18px;\"><strong>🔗 Activity URLs:</strong><br>\n<span style=\"font-family: monospace; background: #f5f5f5; padding: 4px 6px; word-break: break-all; display: block; margin-top: 8px;\">https://www.linkedin.com/feed/update/urn:li:activity:273437/</span></p>\n\n<p style=\"margin-bottom: 25px;\"><strong>🌐 Full post URLs:</strong><br>\n<span style=\"font-family: monospace; background: #f5f5f5; padding: 4px 6px; word-break: break-all; display: block; margin-top: 8px;\">https://www.linkedin.com/posts/username_title-activity-7334385757769416704-dMnz</span></p>\n\n<hr style=\"margin: 30px 0; border: 1px solid #ddd;\">-------------------\n\n<p style=\"margin-bottom: 20px;\"><strong style=\"color: #28a745;\">✅ Step 2: Results</strong></p>\n<p style=\"margin-bottom: 20px;\">Once finished, you'll be redirected to Google Sheets where you can find your result with the name:</p>\n<p style=\"background: #f0f8ff; padding: 15px; border-left: 4px solid #0077b5; margin: 20px 0;\">\n<strong>\"LinkedIn-Posts-Comments-Leads-{{ $now.format('yyyy-MM-dd') }}\"</strong>\n</p>\n\n<hr style=\"margin: 30px 0; border: 1px solid #ddd;\">-------------------\n\n\n<p style=\"color: #666; font-size: 14px; margin-top: 25px;\"><em>💡 Tip: Make sure your LinkedIn URLs are publicly accessible for the best results.</em></p>\n\n</div>"},"typeVersion":2.2},{"id":"d90f3510-b326-4352-90f4-772eca3d5b9b","name":"Set APIFY Token","type":"n8n-nodes-base.set","position":[1820,1460],"parameters":{"options":{},"assignments":{"assignments":[{"id":"20febbcc-0a59-4a89-8932-9bbb34b0e9f9","name":"APIFY_TOKEN","type":"string","value":""},{"id":"291758a3-c73f-4db5-a2b3-5f88664a8b53","name":"limit","type":"number","value":"={{ $json.limit }}"},{"id":"921b72d5-3688-4324-9d36-50668548344e","name":"postIds","type":"array","value":"={{ $json.postIds }}"}]},"includeOtherFields":true},"notesInFlow":true,"typeVersion":3.4},{"id":"36eb3f1f-e0db-4175-964e-9200bf503918","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[1740,1360],"parameters":{"color":3,"width":260,"height":300,"content":"## 1. Set APIFY TOKEN\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n### _(CLICK ME)_"},"typeVersion":1},{"id":"5237c7a8-e2b4-49ae-a070-9cd885e1bc18","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[1420,1280],"parameters":{"color":2,"height":300,"content":"_(or if run manually ENABLE this)_  \n### 4. Set Post ID/URL\n\n\n\n\n\n\n\n\n\n\n\n\n\n### _(CLICK ME)_ and Connect\n"},"typeVersion":1},{"id":"9637dc03-c409-477d-a596-c84f4df12d63","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[1160,1620],"parameters":{"color":3,"width":400,"height":380,"content":"## 3. You can run through the form (check Form URL)\n\n\n\n\n\n\n\n\n\n\n\n\n\n### _(CLICK ME)_ \nIf run manually DISABLE this node"},"typeVersion":1},{"id":"efe492e1-78f7-4f55-89d7-caea70767044","name":"Set fields from the form","type":"n8n-nodes-base.set","position":[1620,1760],"parameters":{"options":{},"assignments":{"assignments":[{"id":"916bb8f7-34c5-4f2f-b13d-f60d228abc1f","name":"postIds","type":"array","value":"={{ $json['Post IDs/URLs'].replaceAll('\\n','').replaceAll('\\r', '').split(',').filter(item => $ifEmpty(item, false))  }}"},{"id":"7ff3d2f4-ab8a-444f-9747-f95166a950bd","name":"limit","type":"number","value":"={{ $json['How many comments you want to scrape?'] || 100 }}"}]}},"notesInFlow":true,"typeVersion":3.4},{"id":"aea3954b-94cf-4785-a692-a46d45b9add5","name":"Set manual fields","type":"n8n-nodes-base.set","disabled":true,"position":[1480,1360],"parameters":{"options":{},"assignments":{"assignments":[{"id":"af09ad86-b3dd-45b7-b17d-cb810e4b48d6","name":"postIds","type":"array","value":"['https://www.linkedin.com/feed/update/urn:li:activity:7334385757769416704/', 'https://www.linkedin.com/feed/update/urn:li:activity:7318557377971023872/']"},{"id":"b2f9412e-4c81-454f-ad84-a1d5f6547a26","name":"limit","type":"number","value":100}]}},"notesInFlow":true,"typeVersion":3.4},{"id":"c9d2e385-9c38-43ea-88bd-e7363f215cc7","name":"More runs needed?","type":"n8n-nodes-base.if","notes":"Per run it's only 100 comments","position":[2620,1460],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"19224073-c84d-456f-b694-318df4223d2a","operator":{"type":"number","operation":"gt"},"leftValue":"={{ $json.summary.totalComments }}","rightValue":100},{"id":"8156447e-75dd-4cad-8fc8-17366667d165","operator":{"type":"number","operation":"gt"},"leftValue":"={{ $('Set APIFY Token').first().json.limit }}","rightValue":100}]}},"notesInFlow":true,"typeVersion":2.2},{"id":"021e8ae2-6773-4154-ab59-61dab49b16e9","name":"Set pagination","type":"n8n-nodes-base.code","position":[2840,1320],"parameters":{"jsCode":"const limitPages = Math.ceil($('Set APIFY Token').first().json.limit / 100)\nconst totalComments = $('Run Apify Comments Scraper').last().json.summary.totalComments \nconst availablePages = Math.ceil(totalComments / 100)\n\nconst totalRuns = availablePages <= limitPages ? availablePages : limitPages\n\nlet currentPage = 2\n\n\nreturn {\n  json: {\n    items:\n      Array.from({ length: totalRuns }, (_, i) => {\n        return { currentPage: currentPage + i }\n      })\n  }\n};"},"typeVersion":2},{"id":"5371649d-d032-452d-8af6-13a679b254fd","name":"Split Out","type":"n8n-nodes-base.splitOut","position":[3060,1320],"parameters":{"options":{},"fieldToSplitOut":"items"},"typeVersion":1},{"id":"4003ce75-3e13-4d2a-9199-bac9f5bda6de","name":"Aggregate","type":"n8n-nodes-base.aggregate","position":[2400,1460],"parameters":{"options":{},"aggregate":"aggregateAllItemData","destinationFieldName":"items"},"typeVersion":1},{"id":"c25710e8-c47c-4fb9-91e1-d9fe37ad53fa","name":"Gather All Comments","type":"n8n-nodes-base.set","position":[3720,1120],"parameters":{"options":{},"assignments":{"assignments":[{"id":"b4280f11-a50b-4065-8742-9f994c6ee84d","name":"items","type":"array","value":"={{ $json.items.concat($('Aggregate').item.json.items) }}"}]}},"typeVersion":3.4},{"id":"2876c38b-ed23-4d6b-9a65-97c1b70d245a","name":"Create Unique List of Leads","type":"n8n-nodes-base.code","position":[4180,1480],"parameters":{"jsCode":"// Get all input items\nconst items = $input.first().json.items;\n\n// Use reduce to filter unique profile_url values\nconst uniqueProfiles = items.reduce((acc, item) => {\n  // Check if this profile_url is already in the accumulator\n  console.log(item)\n  if (item.author && !acc.some(i => i.profile_url === item.author.profile_url)) {\n    acc.push(item.author);\n  }\n  return acc;\n}, []);\n\nreturn uniqueProfiles"},"typeVersion":2},{"id":"80336fb8-a783-4907-89eb-82b9877a7b36","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[2000,1040],"parameters":{"color":7,"width":2040,"height":840,"content":"## 🛠 Running Apify LinkedIn Comments Scrapers and processing data\nWe want to run an Apify scraper once to validate the number of available comments and then run it multiple times if necessary."},"typeVersion":1},{"id":"3877d666-27e5-4bf7-8a1c-3c1e4dce32d8","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[4040,1040],"parameters":{"width":1700,"height":840,"content":"## Create a unique list of Leads, ✨ enrich ✨ it with more data\nOnce we have all of the comment authors, we need to enrich them with more information from LinkedIn\n"},"typeVersion":1},{"id":"fdbf8690-b517-4773-819a-e8bd92ddcd25","name":"Run Apify Comments Scraper","type":"n8n-nodes-base.httpRequest","position":[2180,1460],"parameters":{"url":"={{ 'https://api.apify.com/v2/acts/apimaestro~linkedin-post-comments-replies-engagements-scraper-no-cookies/run-sync-get-dataset-items?token=' +$json.APIFY_TOKEN}}","method":"POST","options":{},"sendBody":true,"bodyParameters":{"parameters":[{"name":"postIds","value":"={{ $json.postIds }}"},{"name":"page_number","value":"={{ 1 }}"},{"name":"sortOrder","value":"most recent"},{"name":"limit","value":"={{ $json.limit }}"}]}},"typeVersion":4.2},{"id":"5b426074-2649-48cf-9a55-cedfe1cb0bf7","name":"Run Apify Comments Scraper Loop","type":"n8n-nodes-base.httpRequest","position":[3500,1340],"parameters":{"url":"={{ 'https://api.apify.com/v2/acts/apimaestro~linkedin-post-comments-replies-engagements-scraper-no-cookies/run-sync-get-dataset-items?token=' + $('Set APIFY Token').first().json.APIFY_TOKEN}}","method":"POST","options":{},"sendBody":true,"bodyParameters":{"parameters":[{"name":"postIds","value":"={{ $('Set APIFY Token').first().json.postIds }}"},{"name":"page_number","value":"={{ $json.currentPage }}"},{"name":"sortOrder","value":"most recent"},{"name":"limit","value":"={{ $('Set APIFY Token').first().json.limit }}"}]}},"typeVersion":4.2},{"id":"b3038b0f-d025-4843-8204-1bb498b193ce","name":"Run Apify Profile Enrichment","type":"n8n-nodes-base.httpRequest","position":[5220,1500],"parameters":{"url":"={{ 'https://api.apify.com/v2/acts/apimaestro~linkedin-profile-batch-scraper-no-cookies-required/run-sync-get-dataset-items?token=' + $('Set APIFY Token').first().json.APIFY_TOKEN}}","method":"POST","options":{},"sendBody":true,"bodyParameters":{"parameters":[{"name":"usernames","value":"={{ $json.batches }}"}]}},"typeVersion":4.2},{"id":"87194dbf-8d63-438a-8081-8ed7a4e93169","name":"Split Out Batches","type":"n8n-nodes-base.splitOut","position":[4640,1480],"parameters":{"include":"allOtherFields","options":{},"fieldToSplitOut":"batches"},"typeVersion":1},{"id":"4fe12998-abdd-4659-aaa7-1aeef2ad241a","name":"Loop Over Comments","type":"n8n-nodes-base.splitInBatches","position":[3280,1320],"parameters":{"options":{}},"typeVersion":3},{"id":"e1a29bde-cfe6-4c89-a601-3ae0b582e052","name":"Loop Over Profiles","type":"n8n-nodes-base.splitInBatches","position":[4940,1480],"parameters":{"options":{}},"typeVersion":3},{"id":"1eb148c9-17a9-4d02-99a0-e647b3398ea4","name":"Split in batches","type":"n8n-nodes-base.code","position":[4400,1480],"parameters":{"jsCode":"const uniqueProfiles = $input.all().map(item => item.json.profile_url)\n\n// Split the uniqueProfiles in batches of 500 for the Apify\nconst chunkSize = 500;\nconst batches = [];\n\nfor (let i = 0; i < uniqueProfiles.length; i += chunkSize) {\n  batches.push(uniqueProfiles.slice(i, i + chunkSize));\n}\n\n\nreturn { batches };"},"typeVersion":2},{"id":"596572a6-7ffe-4cfa-b662-8cd083dbaccc","name":"Aggregate Comments","type":"n8n-nodes-base.aggregate","position":[3780,1340],"parameters":{"options":{},"aggregate":"aggregateAllItemData","destinationFieldName":"items"},"typeVersion":1},{"id":"1dc0b52f-3c86-40ea-8c4b-399872e22259","name":"Aggregate Profiles","type":"n8n-nodes-base.aggregate","position":[5440,1500],"parameters":{"options":{},"aggregate":"aggregateAllItemData","destinationFieldName":"items"},"typeVersion":1},{"id":"21cc6cc8-d4dc-40d9-9cfb-0069786af878","name":"Aggregate All Profiles","type":"n8n-nodes-base.aggregate","position":[5220,1320],"parameters":{"options":{"mergeLists":true},"fieldsToAggregate":{"fieldToAggregate":[{"fieldToAggregate":"items"}]}},"typeVersion":1},{"id":"84708fbf-bed4-4ce3-85c9-3ea16a9358c5","name":"Aggregate All Comments","type":"n8n-nodes-base.aggregate","position":[3500,1120],"parameters":{"options":{"mergeLists":true},"fieldsToAggregate":{"fieldToAggregate":[{"fieldToAggregate":"items"}]}},"typeVersion":1},{"id":"ecab78f3-dcf3-4ddc-afdd-e0d49df4eaca","name":"Convert to File","type":"n8n-nodes-base.convertToFile","disabled":true,"position":[6100,1140],"parameters":{"options":{"headerRow":true}},"executeOnce":true,"typeVersion":1.1},{"id":"36b226d0-a07b-48f1-9a28-85aebf794fd9","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[5840,1040],"parameters":{"color":2,"width":660,"height":380,"content":"## You can either run it manually (not through form) and download the CSV\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYou need to activate \"Trigger manual\" nodes first\n\n### _(CLICK ME) and Connect_"},"typeVersion":1},{"id":"f278ede4-329d-4248-9f6c-b9bb14fa13aa","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[5840,1500],"parameters":{"color":3,"width":660,"height":380,"content":"## 2. Add credentials to Google Sheets\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n### _(CLICK ME)_"},"typeVersion":1},{"id":"49cf9532-0e2f-487e-b6d4-eeb15fec3885","name":"Prepare the list for export","type":"n8n-nodes-base.code","position":[5440,1320],"parameters":{"jsCode":"function flatten(obj, prefix = '', res = {}) {\n  for (const key in obj) {\n    const value = obj[key];\n    const prefixedKey = prefix ? `${prefix}_${key}` : key;\n    if (typeof value === 'object' && value !== null && !Array.isArray(value)) {\n      flatten(value, prefixedKey, res);\n    } else {\n      res[prefixedKey] = value;\n    }\n  }\n  return res;\n}\n\nreturn $input.first().json.items.map(item => ({\n  json: flatten(item)\n}));"},"typeVersion":2},{"id":"33928b41-e209-4240-a636-41874b87b2f8","name":"Create Google Sheet","type":"n8n-nodes-base.googleSheets","position":[5920,1640],"parameters":{"title":"=LinkedIn-Posts-Comments-Leads-{{ $now.format('yyyy-MM-dd+T') }}","options":{},"resource":"spreadsheet","sheetsUi":{"sheetValues":[{"title":"Leads"}]}},"credentials":{"googleSheetsOAuth2Api":{"id":"w7X41eUzPcqOkYCg","name":"Google Sheets account"}},"executeOnce":true,"typeVersion":4.5},{"id":"d6e02f6e-0031-4dd8-922b-7981def48097","name":"Add 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$('Create Google Sheet').first().json.sheets[0].properties.sheetId }}"},"documentId":{"__rl":true,"mode":"id","value":"={{ $('Create Google Sheet').first().json.spreadsheetId }}"}},"credentials":{"googleSheetsOAuth2Api":{"id":"w7X41eUzPcqOkYCg","name":"Google Sheets account"}},"executeOnce":false,"typeVersion":4.5},{"id":"fdb1fb37-bc5a-435d-97b4-aef5fbb8175f","name":"Set the list for sheets","type":"n8n-nodes-base.code","position":[6140,1640],"parameters":{"jsCode":"\nreturn $('Prepare the list for export').all();"},"typeVersion":2},{"id":"fe6d63a8-dadd-4187-814a-7a421bbb6d3f","name":"Sticky Note8","type":"n8n-nodes-base.stickyNote","position":[380,2200],"parameters":{"color":5,"width":1180,"height":280,"content":"\n---\n\n## 📊 Output Data Structure\n\nYour exported data will include:\n\n- **Basic Info**: Name, headline, location\n- **Profile Details**: Company, position, industry\n- **Engagement Data**: Comment content, engagement metrics\n- **Contact Info**: Available profile links and connections\n- **Enriched Data**: Additional profile insights from Apify\n\n"},"typeVersion":1},{"id":"cc6512e8-8b8b-42b6-ba41-02a647bf6f5d","name":"Sticky Note9","type":"n8n-nodes-base.stickyNote","position":[1560,2200],"parameters":{"width":760,"height":540,"content":"---\n\n## 💡 Pro Tips\n\n- **Quality over Quantity**: Target posts with high-quality, relevant engagement\n- **Monitor Costs**: Track your Apify usage to stay within budget\n- **Data Hygiene**: Regularly clean and deduplicate your lead lists\n- **Compliance**: Ensure your scraping activities comply with LinkedIn's terms of service\n\n---\n\n## 🆘 Troubleshooting\n\n**Common Issues:**\n- **Authentication Errors**: Verify your Apify token is correctly configured\n- **Empty Results**: Check that your LinkedIn post URLs are valid and public\n- **Export Failures**: Ensure Google Sheets credentials are properly set up\n\n**Need Help?** Contact [Saverflow.ai](https://saverflow.ai) for support and custom workflow development.\n\n---\n\n*Powered by Saverflow.ai | Streamline your lead generation with intelligent automation*"},"typeVersion":1},{"id":"ad786f97-cc63-4259-a469-5c5d271b17f8","name":"Sticky Note10","type":"n8n-nodes-base.stickyNote","position":[380,1240],"parameters":{"color":6,"width":1620,"height":960,"content":"\n## 📋 Prerequisites\n\n### Required API Credentials\n\n1. **Apify Token**\n   - Add your `APIFY_TOKEN` to the workflow credentials\n   - [Get your token from Apify Console](https://apify.com/account#/integrations)\n\n2. **Google Sheets Credentials** *(if using Sheets export)*\n   - Configure OAuth credentials for Google Sheets integration\n   - [Follow n8n's Google Sheets setup guide](https://docs.n8n.io/integrations/builtin/credentials/google/oauth-single-service/?utm_source=n8n_app&utm_medium=credential_settings&utm_campaign=create_new_credentials_modal)\n\n---\n\n## 🔄 Workflow Process\n\n### Default Mode: Form-Based Execution\n\n1. **Manual Trigger** → Launches the workflow\n2. **Form Submission** → User-friendly form for inputting LinkedIn post URLs\n3. **Comment Scraping** → Apify extracts all comments from specified posts\n4. **Profile Enrichment** → Additional profile data gathered for each commenter\n5. **Data Processing** → Creates unique, enriched lead list\n6. **Google Sheets Export** → Automatically populates your spreadsheet\n\n**Result**: You'll be redirected to a Google Sheets document containing all enriched leads\n\n### Alternative Mode: CSV Export\n\nFor users preferring CSV output:\n\n1. **Disable**: Form trigger nodes\n2. **Enable**: Manual trigger node\n3. **Disable**: Google Sheets export nodes\n4. **Enable**: CSV download nodes\n5. **Configure**: Add post IDs/URLs in \"Set manual fields\" node\n6. **Execute**: Run workflow and download CSV from the CSV node\n"},"typeVersion":1},{"id":"66685e68-953e-41b7-bc24-3de11ec8798a","name":"Done","type":"n8n-nodes-base.noOp","position":[7060,1360],"parameters":{},"typeVersion":1}],"pinData":{},"connections":{"Add Leads":{"main":[[{"node":"Done","type":"main","index":0}]]},"Aggregate":{"main":[[{"node":"More runs needed?","type":"main","index":0}]]},"Split Out":{"main":[[{"node":"Loop Over Comments","type":"main","index":0}]]},"Set pagination":{"main":[[{"node":"Split Out","type":"main","index":0}]]},"Convert to File":{"main":[[]]},"Set APIFY Token":{"main":[[{"node":"Run Apify Comments Scraper","type":"main","index":0}]]},"Split in batches":{"main":[[{"node":"Split Out Batches","type":"main","index":0}]]},"Trigger manually":{"main":[[{"node":"Set manual fields","type":"main","index":0}]]},"More runs needed?":{"main":[[{"node":"Set pagination","type":"main","index":0}],[{"node":"Create Unique List of Leads","type":"main","index":0}]]},"Set manual fields":{"main":[[]]},"Split Out 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name: Automate Social Media Content with AI for Instagram, Facebook, LinkedIn & X
author_name: LukaszB
author_username: lukaszb
categories: []
created_at: 2025-06-04T03:00:43.654Z
updated_at: 2025-06-04T03:00:43.654Z
url: https://n8n.io/workflows/4637
scraped_at: 2025-06-19 22:07:56
description:
  AI Social Media Content Automation – n8n Workflow

  This workflow is built for creators, solopreneurs, SaaS founders, and agencies looking to automate their social media content process from idea to publication. It combines the power of OpenAI, Google Sheets, and official APIs for Instagram, Facebook, X (Twitter), and LinkedIn to deliver fully automated, brand-consistent social media posts — including text, images, hashtags, CTAs, and scheduling.

  No more switching tools, rewriting content, or forgetting to post. Just set it up once and let your brand grow automatically.

  🔧 What the Workflow Does
  Generates platform-specific post ideas based on your brand tone.
  Selects the best idea (or lets you manually input one).
  Writes tailored posts for Instagram, Facebook, X, and LinkedIn.
  Auto-generates platform-specific visuals using OpenAI (DALL·E) and uploads to Cloudinary.
  Publishes content directly using official platform APIs.
  Logs all actions to Google Sheets and optionally sends a summary email via Gmail.

  ⚙️ How It Works
  Trigger the workflow manually, on a schedule, or from a chatbot.
  AI generates multiple post ideas and filters the best one.
  Based on the selected idea, it generates full posts for each platform.
  Custom visuals are created using OpenAI and uploaded to Cloudinary.
  Final post content is merged and sent to Instagram, Facebook, X, and LinkedIn.
  Logs and optional reports are created in Google Sheets and Gmail.

  🛠️ Setup Steps
  Set up your accounts and credentials: OpenAI, Cloudinary, Gmail, Meta, LinkedIn, X, and Google Sheets.
  Import the workflow and subworkflow (Get Brand Brief) into n8n.
  Replace all placeholders (sheet IDs, access tokens, profile/page IDs, brand brief URLs).
  Optionally personalize prompts and output templates to match your brand voice.
  Setup time: ~30–45 minutes (including credentials and testing).

  Detailed configuration notes are included in sticky nodes throughout the workflow.

  📦 Included in Your Package
  JSON workflow file (main flow + brand brief subflow)
  Google Sheets templates for history logging
  Setup documentation (Markdown & PDF)
  Placeholder reference sheet

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.twitter","n8n-nodes-base.gmail","n8n-nodes-base.linkedIn","n8n-nodes-base.notion","n8n-nodes-base.googleDocs","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.openAi"]

workflow_json:
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Fields","type":"n8n-nodes-base.set","position":[1440,560],"parameters":{},"typeVersion":3.4},{"id":"49e958ff-a1e4-4e2c-9fbb-e16840bcc997","name":"Structured Output Parser","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[-400,780],"parameters":{},"typeVersion":1.2},{"id":"9e7f7e6e-b7f8-4457-84eb-fc24354cb666","name":"Idea creator","type":"@n8n/n8n-nodes-langchain.chainLlm","position":[-580,560],"parameters":{},"typeVersion":1.6},{"id":"2ee53b83-7ab2-4018-805b-9d4dbb235ae6","name":"When clicking ‘Test workflow’","type":"n8n-nodes-base.manualTrigger","position":[-1320,640],"parameters":{},"typeVersion":1},{"id":"3c262ffa-b8c7-4477-a825-92b70e0eb4e4","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-940,300],"parameters":{"content":""},"typeVersion":1},{"id":"709ecf38-4467-4c9a-821d-b6933d83d215","name":"Sticky 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name: Competitor Price Monitoring with Web Scraping,Google Sheets & Telegram
author_name: Tony Paul
author_username: tonydatahut
categories: []
created_at: 2025-06-04T03:40:59.974Z
updated_at: 2025-06-04T03:40:59.974Z
url: https://n8n.io/workflows/4640
scraped_at: 2025-06-19 22:07:56
description:
  How it works

  ++Download the google sheet here++ and replace this with the googles sheet node: Google sheet , upload to google sheets and replace in the google sheets node.

  Scheduled trigger: Runs once a day at 8 AM (server time).

  Fetch product list: Reads your “master” sheet (product_url + last known price) from Google Sheets.

  Loop with delay: Iterates over each row (product) one at a time, inserting a short pause (20 s) between HTTP requests to avoid blocking.

  Scrape current price: Loads each product_url, extracts the current price via a simple CSS selector.

  Compare & normalize: Compares the newly scraped price against the “last_price” from your sheet, calculates percentage change, and tags items where price_changed == true.

  On price change:
  Send alert: Formats a Telegram message (“Price Drop” or “Price Hike”) and pushes it to your configured chat.

  Log history: Appends a new row to a separate “price_tracking” tab with timestamp, old price, new price, and % change.

  Update master sheet: After a 1 min pause, writes the updated current_price back to your “master” sheet so future runs use it as the new baseline.

  Set up step
  Google Sheets credentials (~5 min)
  Create a Google Sheets OAuth credential in n8n. 
  Copy your sheet’s ID and ensure you have two tabs: 
  product_data (columns: product_url, price) 
  price_tracking (columns: timestamp, product_url, last_price, current_price, price_diff_pct, price_changed) 
  Paste the sheet ID into both Google Sheets nodes (“Read” and “Append/Update”). 
  Telegram credentials (~5 min) 
  Create a Telegram Bot token via BotFather. 
  Copy your chat_id (for your target group or personal chat). 
  Add those credentials to n8n and drop them into the “Telegram” node.

  Workflow parameters (~5 min)

  Verify the schedule in the Schedule Trigger node is set to 08:00 (or adjust to your preferred run time).

  In the Loop Over Items node, confirm “Batch Size” is 1 (to process one URL at a time).
   
  Adjust the Delay to avoid Request Blocking node if your site requires a longer pause (default is 20 s).
   
  In the Parse Data From The HTML Page node, double-check the CSS selector matches how prices appear on your target site.
   
  Once credentials are in place and your sheet tabs match the expected column names, the flow should be ready to activate. Total setup time is under 15 minutes—detailed notes are embedded as sticky comments throughout the workflow to help you tweak selectors, change timeouts, or adjust sheet names without digging into code.

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.telegram","n8n-nodes-base.code","n8n-nodes-base.html"]

workflow_json:
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Then it Reads the “master” Google Sheet tab, pulling each row’s product_url + last_price for processing**"},"typeVersion":1},{"id":"9b9932ec-8cfd-4096-965b-592b13a1fb8e","name":"Daily 8 AM Trigger","type":"n8n-nodes-base.scheduleTrigger","position":[-2420,290],"parameters":{"rule":{"interval":[{"triggerAtHour":8}]}},"typeVersion":1.2},{"id":"6427045b-8b73-42f0-9dcc-768cbfac9c4a","name":"Fetch Product List from Sheet","type":"n8n-nodes-base.googleSheets","position":[-2200,290],"parameters":{"options":{},"sheetName":{"__rl":true,"mode":"list","value":"gid=0","cachedResultUrl":"Replace with your google sheet url by downloading the googlesheet in the description of this automation","cachedResultName":"product_data"},"documentId":{"__rl":true,"mode":"list","value":"18ZAjxmwanf6MS7JfrEX6ett6J-tOQi58B257hE6sOS4","cachedResultUrl":"https://docs.google.com/spreadsheets/d/18ZAjxmwanf6MS7JfrEX6ett6J-tOQi58B257hE6sOS4/edit?usp=drivesdk","cachedResultName":"competitor_price_drop_automation"}},"credentials":{"googleSheetsOAuth2Api":{"id":"qxXfj438qZiGMViE","name":"Tony@datahut sheet"}},"typeVersion":4.5},{"id":"0b4a1398-bb29-4f42-9475-eb08ab4dabfc","name":"Process Each Product in Batches of 1","type":"n8n-nodes-base.splitInBatches","position":[-1980,290],"parameters":{"options":{"reset":false}},"typeVersion":3},{"id":"ccf9f9ca-b380-457e-9fc2-c6ffe1636984","name":"Pause Between Requests","type":"n8n-nodes-base.wait","position":[-1760,40],"webhookId":"335e69ac-d8ce-4704-a9c6-5a087f2859f8","parameters":{"amount":20},"typeVersion":1.1},{"id":"aa49653b-cf9f-449b-953d-a9a19ac74187","name":"Load Product Page 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scraped = items[0].json;\n\n// Original data from Wait node\nconst original = $items(\"Pause Between Requests\")[0].json;\n\n// Clean and parse price\nconst priceNum = parseFloat((scraped.current_price || \"\").replace(/[^0-9.]+/g, \"\"));\n\n// Original price from Google Sheet\nconst lastPrice = parseFloat(original.price);\n\nreturn [{\n  json: {\n    product_url: original.product_url,\n    row_number: original.row_number,\n    last_price: lastPrice,\n    current_price: priceNum\n  }\n}];\n\n"},"typeVersion":2},{"id":"5feae925-0642-40d1-89be-c220a5a1c8a2","name":"Compute Price Change","type":"n8n-nodes-base.code","position":[-880,40],"parameters":{"jsCode":"// Get the incoming item\nconst item = items[0].json;\n\n// Compute whether the price changed\nconst priceChanged = item.last_price !== item.current_price;\n\n// Calculate percentage difference (will be NaN if last_price is null/0)\nlet priceDiffPct = null;\nif (item.last_price && item.last_price !== 0) {\n  priceDiffPct = (\n    ((item.current_price - item.last_price) / item.last_price) *\n    100\n  ).toFixed(2);\n}\n\n// Return everything, including product_url and last_price\nreturn [\n  {\n    json: {\n      product_url: item.product_url,\n      last_price: item.last_price,\n      current_price: item.current_price,\n      price_changed: priceChanged,\n      price_diff_pct: priceDiffPct !== null ? parseFloat(priceDiffPct) : null,\n      timestamp: new Date().toISOString(),\n    },\n  },\n];\n\n\n\n"},"typeVersion":2},{"id":"5dea2082-d5c5-4c13-8c71-ab7ac498632d","name":"Clean Up Parsed Fields","type":"n8n-nodes-base.code","position":[-660,40],"parameters":{"jsCode":"// Function node: 'items' is an array of incoming items\nif (!items.length) {\n  return [];\n}\n\nconst input = items[0].json;\nconst cleaned = {};\n\nfor (const key in input) {\n  const cleanKey = key.replace(/\\t/g, '').trim();\n  cleaned[cleanKey] = input[key];\n}\n\nreturn [\n  {\n    json: {\n      product_url: cleaned.product_url,\n      last_price: cleaned.last_price,\n      current_price: cleaned.current_price,\n      price_changed: cleaned.price_changed,\n      price_diff_pct: cleaned.price_diff_pct,\n      timestamp: cleaned.timestamp,\n    },\n  },\n];\n\n\n"},"typeVersion":2},{"id":"21c27cb9-c6bc-4cc3-9ad1-3d37fc6fbc3d","name":"Is Price Changed?","type":"n8n-nodes-base.if","position":[-440,40],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"or","conditions":[{"id":"5e285834-82ef-4c77-b96b-97661760f996","operator":{"type":"boolean","operation":"true","singleValue":true},"leftValue":"={{ $json.price_changed}}","rightValue":"true"}]}},"typeVersion":2.2},{"id":"50274daa-48c7-4c58-aaf5-264013582f97","name":"Build Telegram Alert Message","type":"n8n-nodes-base.code","position":[-220,140],"parameters":{"jsCode":"const itemsFormatted = [];\n\nfor (const item of items) {\n  const data = item.json;\n\n  // Determine alert type\n  const isDrop = data.price_diff_pct < 0;\n  const alertType = isDrop ? \"📉 *Price Drop Alert*\" : \"📈 *Price Hike Alert*\";\n\n  // Format timestamp to IST\n  const date = new Date(data.timestamp);\n  const istOffset = 5.5 * 60 * 60 * 1000;\n  const istDate = new Date(date.getTime() + istOffset);\n  const formattedDate = istDate.toLocaleString(\"en-IN\", {\n    day: \"2-digit\",\n    month: \"short\",\n    year: \"numeric\",\n    hour: \"2-digit\",\n    minute: \"2-digit\",\n    hour12: true\n  });\n\n  // Build message\n  const message = `${alertType}\n\n🛍️ *Product:* (${data.product_url})\n💸 *Last Price:* $${data.last_price}\n💰 *Current Price:* $${data.current_price}\n📊 *Change:* ${data.price_diff_pct > 0 ? \"+\" : \"\"}${data.price_diff_pct}%\n🕒 *Time:* ${formattedDate}`;\n\n  itemsFormatted.push({ json: { message } });\n}\n\nreturn itemsFormatted;\n\n\n"},"typeVersion":2},{"id":"f62b1f32-fe79-43c3-ad17-0ff31fcdc489","name":"Log Price History to Sheet","type":"n8n-nodes-base.googleSheets","position":[-220,-60],"parameters":{"columns":{"value":{"last_price":"={{ $json.last_price }}","timestamp\t":"={{ new Date($json.timestamp).toLocaleString('en-IN', {\n  timeZone: 'Asia/Kolkata',\n  day: '2-digit',\n  month: 'short',\n  year: 'numeric',\n  hour: '2-digit',\n  minute: '2-digit',\n  hour12: true\n}) }}\n","product_url":"={{ $json.product_url }}","current_price":"={{ $json.current_price }}","price_diff_pct":"={{ $json.price_diff_pct}}","price_changed        ":"={{ $json.price_changed }}"},"schema":[{"id":"timestamp\t","type":"string","display":true,"required":false,"displayName":"timestamp\t","defaultMatch":false,"canBeUsedToMatch":true},{"id":"product_url","type":"string","display":true,"required":false,"displayName":"product_url","defaultMatch":false,"canBeUsedToMatch":true},{"id":"current_price","type":"string","display":true,"removed":false,"required":false,"displayName":"current_price","defaultMatch":false,"canBeUsedToMatch":true},{"id":"last_price","type":"string","display":true,"removed":false,"required":false,"displayName":"last_price","defaultMatch":false,"canBeUsedToMatch":true},{"id":"price_changed        ","type":"string","display":true,"removed":false,"required":false,"displayName":"price_changed        ","defaultMatch":false,"canBeUsedToMatch":true},{"id":"price_diff_pct","type":"string","display":true,"removed":false,"required":false,"displayName":"price_diff_pct","defaultMatch":false,"canBeUsedToMatch":true}],"mappingMode":"defineBelow","matchingColumns":[],"attemptToConvertTypes":false,"convertFieldsToString":false},"options":{"useAppend":true},"operation":"append","sheetName":{"__rl":true,"mode":"list","value":2110110902,"cachedResultUrl":"https://docs.google.com/spreadsheets/d/18ZAjxmwanf6MS7JfrEX6ett6J-tOQi58B257hE6sOS4/edit#gid=2110110902","cachedResultName":"price_tracking"},"documentId":{"__rl":true,"mode":"list","value":"18ZAjxmwanf6MS7JfrEX6ett6J-tOQi58B257hE6sOS4","cachedResultUrl":"https://docs.google.com/spreadsheets/d/18ZAjxmwanf6MS7JfrEX6ett6J-tOQi58B257hE6sOS4/edit?usp=drivesdk","cachedResultName":"competitor_price_drop_automation"}},"credentials":{"googleSheetsOAuth2Api":{"id":"qxXfj438qZiGMViE","name":"Tony@datahut sheet"}},"typeVersion":4.5},{"id":"d5555b69-d7ee-4ab0-a851-5288de0d39cf","name":"Pause Before Updating Sheet","type":"n8n-nodes-base.wait","position":[0,-60],"webhookId":"f8ba192a-211d-4b40-be9c-9994343eb162","parameters":{"unit":"minutes","amount":1},"typeVersion":1.1},{"id":"ade41bb7-fbe3-4e08-a7fa-6c85291dc9fa","name":"Update Last Price in Master Sheet","type":"n8n-nodes-base.googleSheets","position":[220,-60],"parameters":{"columns":{"value":{"price":"={{ $json.current_price }}","product_url":"={{ $json.product_url }}"},"schema":[{"id":"product_url","type":"string","display":true,"removed":false,"required":false,"displayName":"product_url","defaultMatch":false,"canBeUsedToMatch":true},{"id":"price","type":"string","display":true,"required":false,"displayName":"price","defaultMatch":false,"canBeUsedToMatch":true},{"id":"row_number","type":"string","display":true,"removed":true,"readOnly":true,"required":false,"displayName":"row_number","defaultMatch":false,"canBeUsedToMatch":true}],"mappingMode":"defineBelow","matchingColumns":["product_url"],"attemptToConvertTypes":false,"convertFieldsToString":false},"options":{},"operation":"update","sheetName":{"__rl":true,"mode":"list","value":"gid=0","cachedResultUrl":"Replace with your google sheet url by downloading the googlesheet in the description of this automation","cachedResultName":"product_data"},"documentId":{"__rl":true,"mode":"list","value":"18ZAjxmwanf6MS7JfrEX6ett6J-tOQi58B257hE6sOS4","cachedResultUrl":"https://docs.google.com/spreadsheets/d/18ZAjxmwanf6MS7JfrEX6ett6J-tOQi58B257hE6sOS4/edit?usp=drivesdk","cachedResultName":"competitor_price_drop_automation"}},"credentials":{"googleSheetsOAuth2Api":{"id":"qxXfj438qZiGMViE","name":"Tony@datahut sheet"}},"typeVersion":4.5},{"id":"9ebdbe49-fda1-42f5-99d9-a5e1e3ab800f","name":"Send Price Alert via Telegram","type":"n8n-nodes-base.telegram","position":[0,215],"webhookId":"39287ce3-3a7e-4c7e-b8ee-b11ebb83bfb8","parameters":{"text":"={{$json[\"message\"]}}","chatId":"7739958732","additionalFields":{}},"credentials":{"telegramApi":{"id":"aoNA5zsMeU9KNpGg","name":"Telegram Tab Jio"}},"typeVersion":1.2},{"id":"dcb4eff8-9d44-4016-a89a-787be943d0e9","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-2040,440],"parameters":{"height":100,"content":"**Iterates through all products in groups (e.g., 1 at a time) so we don’t overwhelm the target site with parallel requests.**"},"typeVersion":1},{"id":"1a306c68-e652-4d63-b11e-96366c4a59ed","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[-1560,-60],"parameters":{"width":360,"height":80,"content":"**fires an HTTP GET for each product_url, then uses a CSS selector to pull the live price text from the returned HTML.**"},"typeVersion":1},{"id":"81f8701a-219f-43a3-aceb-78f2298273cc","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[-1120,180],"parameters":{"width":380,"height":100,"content":"**Strips currency symbols/commas to convert strings into numbers; compares last_price vs. current_price to calculate % change and set price_changed=true if different**"},"typeVersion":1},{"id":"7fd2108e-5fcb-4ba6-938b-6c940f8a305f","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[-740,-100],"parameters":{"width":220,"height":120,"content":"\n**Removes stray whitespace or malformed keys (e.g., stray tabs) so downstream nodes can read fields consistently**"},"typeVersion":1},{"id":"7a525b63-e871-46b9-b0b0-4e25a6c7f2bb","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[-540,180],"parameters":{"height":100,"content":"**Only products with price_changed=true proceed to alerting and logging; unchanged items exit here.**"},"typeVersion":1},{"id":"a6352172-9fa3-47b7-8bf0-c948fbe03701","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[-180,420],"parameters":{"width":380,"height":80,"content":"\n**Formats a human‐readable message (product name/URL, old vs. new price, % change, timestamp) and pushes it to the configured Telegram chat**\n"},"typeVersion":1},{"id":"90b702f6-6e75-452d-9315-3f70c4157aab","name":"Sticky Note8","type":"n8n-nodes-base.stickyNote","position":[-200,-160],"parameters":{"width":520,"height":80,"content":"\n**Appends a row to Price_History with timestamp, % change, old vs. new price; then waits 1 minute before overwriting last_price in the master sheet with current_price.**\n"},"typeVersion":1},{"id":"3e3a4498-248d-408e-903b-916a035db039","name":"Sticky Note9","type":"n8n-nodes-base.stickyNote","position":[-1300,-520],"parameters":{"width":580,"content":"## Detailed Explanations of this automation\n \nYou can see the detailed step-by-step instructions here:\n\n- [Free n8n Web Scraping Competitor Price Tracking](https://www.blog.datahut.co/post/free-n8n-web-scraping-competitor-price-tracking)\n- [YouTube Tutorial](https://www.youtube.com/watch?v=a9esT732mmE)\n\n\n\n\n\n\nIs this conversation helpful so far?\n\n\n"},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"2c1bce5e-8208-42c3-8fe8-308b5855695c","connections":{"Is Price Changed?":{"main":[[{"node":"Build Telegram Alert Message","type":"main","index":0},{"node":"Log Price History to Sheet","type":"main","index":0}]]},"Daily 8 AM Trigger":{"main":[[{"node":"Fetch Product List from Sheet","type":"main","index":0}]]},"Compute Price Change":{"main":[[{"node":"Clean Up Parsed Fields","type":"main","index":0}]]},"Clean Up Parsed Fields":{"main":[[{"node":"Is Price Changed?","type":"main","index":0}]]},"Load Product Page HTML":{"main":[[{"node":"Extract Current Price from HTML","type":"main","index":0},{"node":"Process Each Product in Batches of 1","type":"main","index":0}]]},"Normalize Price Values":{"main":[[{"node":"Compute Price Change","type":"main","index":0}]]},"Pause Between Requests":{"main":[[{"node":"Load Product Page HTML","type":"main","index":0}]]},"Log Price History to Sheet":{"main":[[{"node":"Pause Before Updating Sheet","type":"main","index":0}]]},"Pause Before Updating Sheet":{"main":[[{"node":"Update Last Price in Master Sheet","type":"main","index":0}]]},"Build Telegram Alert Message":{"main":[[{"node":"Send Price Alert via Telegram","type":"main","index":0}]]},"Fetch Product List from Sheet":{"main":[[{"node":"Process Each Product in Batches of 1","type":"main","index":0}]]},"Send Price Alert via Telegram":{"main":[[{"node":"Process Each Product in Batches of 1","type":"main","index":0}]]},"Extract Current Price from HTML":{"main":[[{"node":"Normalize Price Values","type":"main","index":0}]]},"Process Each Product in Batches of 1":{"main":[[],[{"node":"Pause Between Requests","type":"main","index":0}]]}}}


name: Summarize Calls & Notify Teams via HubSpot, Slack, Email, WhatsApp
author_name: Encoresky
author_username: encoresky
categories: []
created_at: 2025-06-04T08:02:04.432Z
updated_at: 2025-06-04T08:02:04.432Z
url: https://n8n.io/workflows/4641
scraped_at: 2025-06-19 22:07:56
description:
  This workflow automates the process of handling conversation transcriptions and distributing key information across your organization. Here's what it does:

  Trigger: The workflow is initiated via a webhook that receives a transcription (e.g., from a call or meeting).

  Summarization & Extraction: Using AI, the transcription is summarized, and key information is extracted — such as action items, departments involved, and client details.

  Department Notifications: The relevant summarized information is automatically routed to specific departments via email based on content classification.

  CRM Sync: The summarized version is saved to the associated contact or deal in HubSpot for future reference and visibility.

  *Multi-Channel Alerts: *The summary is also sent via WhatsApp and Slack to keep internal teams instantly informed, regardless of platform.

  Use Case:
  Ideal for sales, customer service, or operations teams who manage client conversations and want to ensure seamless cross-departmental communication, documentation, and follow-up.

  Apps Used:

  Webhook (Trigger)

  OpenAI (or other AI/NLP for summarization)

  HubSpot

  Email

  Slack

  WhatsApp (via Twilio or third-party provider)

nodes_used:
  ["n8n-nodes-base.slack","n8n-nodes-base.hubspot","n8n-nodes-base.whatsApp","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chainSummarization","@n8n/n8n-nodes-langchain.lmChatOpenAi"]

workflow_json:
  {"id":"Gn5M1FfXUAVy6h9l","meta":{"instanceId":"f70454a5d1bedfb04805f13a51a4a63e308e7fc8821155a97e93d697feaee047","templateCredsSetupCompleted":true},"tags":[],"nodes":[{"id":"3d621f41-b73a-406a-bfda-fac0d2ed1499","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[800,340],"parameters":{"color":4,"width":540,"height":560,"content":"## Router agent\nMakes decisions with the help of an LLM  \n- Analyzes the content\n- Decides which part of the transcript is relevant to the different departments\n- Sends the emails to the departments\n"},"typeVersion":1},{"id":"249c22aa-58ca-40df-9157-7a00fbfd3dff","name":"Router Agent","type":"@n8n/n8n-nodes-langchain.agent","notes":"Route the client feedback topics to the relevant department ","position":[960,540],"parameters":{"text":"={{ $('Webhook').item.json.body['Client Conversation'] }}","options":{"systemMessage":"=You are provided with some client-company conversation and should decide who has to be informed about the feedback. Always only inform one person. Those are your options: \n- It's about a product, send an email to {{ $('Define routing emails').item.json['Support Email'] }}\n- It's about an invoicing problem, send an email to {{ $('Define routing emails').item.json['Administrative Email'] }}\n- It's  related to a travelling/trip, send an email to {{ $('Define routing emails').item.json['Commercial Email'] }}\n\nAdd the email of the person (\"{{ $('Webhook').item.json.body['Client Email'] }}\") at the beginning of the text preceded by \"FROM CLIENT: \"\nUse the Mailjet tool to inform each of the most related department. Provide mailjet with a subject, an email, and the email body formated as html which is the client conversation itself."},"promptType":"define"},"notesInFlow":true,"typeVersion":1.8},{"id":"cffeafac-75e0-4a60-91d3-0a7d3bdb7371","name":"Summarization","type":"@n8n/n8n-nodes-langchain.chainSummarization","notes":"The transcript summarized here","position":[360,140],"parameters":{"options":{"summarizationMethodAndPrompts":{"values":{"prompt":"=Summarize the following Converstaion in 2-3 sentences:\n\n\"{{ $('Webhook').item.json.body['Client Conversation'] }}\"\n\nJust output the summarized conversation and nothing else. Use the same language as the input","summarizationMethod":"stuff"}}}},"notesInFlow":true,"typeVersion":2,"alwaysOutputData":false},{"id":"ac5a16de-efb5-4603-bb52-2d85b007bf86","name":"OpenAI Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[460,420],"parameters":{"model":{"__rl":true,"mode":"list","value":"gpt-4o-mini"},"options":{}},"credentials":{"openAiApi":{"id":"7mydLbpOJgXQKswc","name":"OpenAi account"}},"typeVersion":1.2},{"id":"8644b46e-b64c-415b-acfc-103ef5c71903","name":"Gmail","type":"n8n-nodes-base.gmailTool","position":[1040,760],"webhookId":"be3ad85f-a576-44d6-9383-6a038edfb25a","parameters":{"sendTo":"={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('To', ``, 'string') }}","message":"={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Message', ``, 'string') }}","options":{"appendAttribution":false},"subject":"={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Subject', ``, 'string') }}"},"credentials":{"gmailOAuth2":{"id":"Eo7V1LrxcAy38kKf","name":"Harsh"}},"typeVersion":2.1},{"id":"b02e1dad-6f07-4215-a89b-fb0763232bcb","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[-20,40],"parameters":{"color":4,"width":260,"height":260,"content":"## Set emails here\nFor each responsible person"},"typeVersion":1},{"id":"fec711e6-837a-4722-83b9-dc838770fcb3","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[-300,40],"parameters":{"color":4,"width":260,"height":260,"content":"## Webhook \nTo be used to send the transcription."},"typeVersion":1},{"id":"2af225aa-95b9-4578-9c8c-0c49bb5e8510","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[800,40],"parameters":{"color":4,"width":540,"height":280,"content":"## Save transcription to Hubspot\n- Search for the client ID based on his email\n- Upload the summarized conversation as meeting notes"},"typeVersion":1},{"id":"fb226ebf-b62f-48ef-b291-17b4beb30ebc","name":"Slack","type":"n8n-nodes-base.slack","position":[880,-160],"webhookId":"ffba5d59-965e-4042-a5cd-d6d89c443e75","parameters":{"text":"=Below is the latest summary.\n\n{{ $json.response.text }}","select":"channel","channelId":{"__rl":true,"mode":"list","value":"C08VCE4AYNL","cachedResultName":"n8n"},"otherOptions":{},"authentication":"oAuth2"},"credentials":{"slackOAuth2Api":{"id":"SbSspavlddGUo8F0","name":"Slack account"}},"typeVersion":2.3},{"id":"5622a2af-726d-468c-88b2-22d66a449128","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[800,-280],"parameters":{"color":4,"width":540,"height":300,"content":"## Send the summary to Slack and WhatsApp\n- Configure Slack Channel\n- Configure WhatsApp Recepient"},"typeVersion":1},{"id":"d8b2181f-1c52-417b-9fdd-776e64a920ab","name":"WhatsApp Business Cloud","type":"n8n-nodes-base.whatsApp","position":[1120,-160],"webhookId":"36dba7ba-f6f2-45a0-ad5d-cfd5c1622a49","parameters":{"template":"n8n_workflow_latest|en","components":{"component":[{"bodyParameters":{"parameter":[{"text":"={{ $('Webhook').item.json.body['Your Name'] }}"},{"text":"={{ $('Summarization').item.json.response.text }}"}]}}]},"phoneNumberId":"702894569566382","recipientPhoneNumber":"={{ $('Webhook').item.json.body['WhatsApp Contact Number'] }}"},"credentials":{"whatsAppApi":{"id":"bvkhSq7q9MuoWneA","name":"WhatsApp account"}},"typeVersion":1},{"id":"039c62d8-3a33-4436-9577-e2dc45f9e694","name":"Webhook","type":"n8n-nodes-base.webhook","position":[-220,140],"webhookId":"1ffeacc2-1dbd-4a9e-bf3f-6811ddd31642","parameters":{"path":"1ffeacc2-1dbd-4a9e-bf3f-6811ddd31642","options":{},"httpMethod":"POST"},"typeVersion":2},{"id":"941ea710-68a5-46c7-b622-bf88b96430a7","name":"Define routing emails","type":"n8n-nodes-base.set","position":[60,140],"parameters":{"options":{},"assignments":{"assignments":[{"id":"099d5326-3452-47b8-9dc0-acc0e6fd951e","name":"Support Email","type":"string","value":"info@encoresky.com"},{"id":"4ed84290-dbf7-47f7-8693-4f95e0c2fd7e","name":"Administrative Email","type":"string","value":"hr@encoresky.com"},{"id":"614d4a5c-c9f2-4d82-bfcb-cfdcc8a4b07d","name":"Commercial Email","type":"string","value":"sales@encoresky.com"}]}},"typeVersion":3.4},{"id":"8af03b9d-b647-4d4c-8903-dd32e4b6a434","name":"HubSpot Search Client","type":"n8n-nodes-base.hubspot","notes":"Search for the id","position":[880,160],"parameters":{"operation":"search","authentication":"oAuth2","filterGroupsUi":{"filterGroupsValues":[{"filtersUi":{"filterValues":[{"value":"={{ $('Webhook').item.json.body['Client Email'] }}","propertyName":"email|string"}]}}]},"additionalFields":{}},"credentials":{"hubspotOAuth2Api":{"id":"pQcxnwCMnLiFiQOJ","name":"HubSpot account"}},"notesInFlow":true,"typeVersion":2.1},{"id":"cb2bb828-ca8c-473a-a485-c8cde6116388","name":"HubSpot Save Notes","type":"n8n-nodes-base.hubspot","notes":"Add meeting notes in the contact form","position":[1120,160],"parameters":{"type":"meeting","metadata":{"body":"={{ $('Summarization').item.json.response.text }}","title":"New meeting"},"resource":"engagement","authentication":"oAuth2","additionalFields":{"associations":{"contactIds":"={{ $json.properties.hs_object_id }}"}}},"credentials":{"hubspotOAuth2Api":{"id":"pQcxnwCMnLiFiQOJ","name":"HubSpot account"}},"notesInFlow":true,"typeVersion":2.1}],"active":true,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"428e832a-7b0e-4e88-9695-6d793067deb8","connections":{"Gmail":{"ai_tool":[[{"node":"Router Agent","type":"ai_tool","index":0}]]},"Slack":{"main":[[{"node":"WhatsApp Business Cloud","type":"main","index":0}]]},"Webhook":{"main":[[{"node":"Define routing emails","type":"main","index":0}]]},"Router Agent":{"main":[[]]},"Summarization":{"main":[[{"node":"Router Agent","type":"main","index":0},{"node":"HubSpot Search Client","type":"main","index":0},{"node":"Slack","type":"main","index":0}]]},"OpenAI Chat Model":{"ai_languageModel":[[{"node":"Summarization","type":"ai_languageModel","index":0},{"node":"Router Agent","type":"ai_languageModel","index":0}]]},"Define routing emails":{"main":[[{"node":"Summarization","type":"main","index":0}]]},"HubSpot Search Client":{"main":[[{"node":"HubSpot Save Notes","type":"main","index":0}]]}}}


name: SSL Certificate Expiry Notifier (No Paid APIs)
author_name: Evoort Solutions
author_username: evoortsolutions
nodes_used: ["n8n-nodes-base.emailSend","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest"]
categories: []
created_at: 2025-06-04T09:49:31.793Z
updated_at: 2025-06-04T09:49:31.793Z
url: https://n8n.io/workflows/4643
scraped_at: 2025-06-19 22:07:56
description:
  Great — here’s a complete Workflow Description for your n8n Creator submission based on the JSON you shared:

  🔒 SSL Expiry Notifier (No Paid APIs)

  🧩 How it Works

  This workflow automatically checks SSL certificate expiry dates for a list of websites and sends timely email alerts if any certificate is close to expiring. It’s ideal for DevOps, IT Operations, or Web Admin teams to stay ahead of certificate issues —
   without relying on any paid API services.

  Here's the high-level logic:

  Trigger: Runs daily at 8 AM using the Schedule Trigger node.
  Fetch URLs: Reads URLs to monitor from a Google Sheet.
  Check SSL: Calls the free ssl-checker.io API to retrieve SSL certificate details (valid from, valid till, days left).
  Conditional Check: Filters any certificates expiring in 7 days or less.
  Send Email: Sends a styled HTML email alert to predefined recipients.
  Update Sheet: Updates the same Google Sheet with the latest SSL info for each domain.

  ⚙️ Set Up Steps (Quick Overview)

  🕐 Estimated setup time: \~10–15 minutes

  Connect Google Sheets – OAuth2 credentials required. Sheet must include a column titled URL with domains to monitor.
  Set Up SMTP – Add your email credentials under the SMTP node to enable notifications.
  Optional – Customize the threshold days in the "Expiry Alert" IF node if you want alerts for a different timeframe.
  Deploy and Activate – That’s it. Once active, the flow runs automatically every day.

  💡 Detailed field mappings and logic notes are included inside the workflow as sticky notes.

  🚀 Ready to Get Started?

  Let’s start your journey on n8n by creating your free account here.  

workflow_json:
  {"id":"NTSkPEG2pcvG3qoH","meta":{"instanceId":"60c025075753afcab9f803964b4caaca9402f435deb4efafbb8e3b93b54d8752","templateCredsSetupCompleted":true},"name":"SSL Expiry Notifier","tags":[{"id":"b2GfRjIENZV9WKpD","name":"IT Ops","createdAt":"2025-05-29T08:33:47.664Z","updatedAt":"2025-05-29T08:33:47.664Z"},{"id":"sfoRiNMRAXjQ31IO","name":"DevOps","createdAt":"2025-05-29T08:33:47.654Z","updatedAt":"2025-05-29T08:33:47.654Z"}],"nodes":[{"id":"b8c160c6-29a3-4b7d-ab78-e126206627b1","name":"URLs to Monitor","type":"n8n-nodes-base.googleSheets","position":[60,140],"parameters":{"columns":{"value":{"URL":"={{ $json.result.host }}","Days Left":"={{ $json.result.days_left }}","Valid From":"={{ $json.result.valid_from }}","Valid Till":"={{ $json.result.valid_till }}"},"schema":[{"id":"URL","type":"string","display":true,"removed":false,"required":false,"displayName":"URL","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Valid From","type":"string","display":true,"required":false,"displayName":"Valid From","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Valid Till","type":"string","display":true,"required":false,"displayName":"Valid Till","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Days Left","type":"string","display":true,"required":false,"displayName":"Days Left","defaultMatch":false,"canBeUsedToMatch":true},{"id":"row_number","type":"string","display":true,"removed":true,"readOnly":true,"required":false,"displayName":"row_number","defaultMatch":false,"canBeUsedToMatch":true}],"mappingMode":"defineBelow","matchingColumns":["URL"],"attemptToConvertTypes":false,"convertFieldsToString":false},"options":{},"operation":"update","sheetName":{"__rl":true,"mode":"url","value":"https://docs.google.com/spreadsheets/d/1TakDfkHpgB8qQcvy98mgUfEsEZGnIVNlkwqnsWhGDwI/edit?gid=0#gid=0","__regex":"https:\\/\\/docs\\.google\\.com\\/spreadsheets\\/d\\/[0-9a-zA-Z\\-_]+.*\\#gid=([0-9]+)"},"documentId":{"__rl":true,"mode":"url","value":"https://docs.google.com/spreadsheets/d/1TakDfkHpgB8qQcvy98mgUfEsEZGnIVNlkwqnsWhGDwI/edit?gid=0#gid=0"}},"credentials":{"googleSheetsOAuth2Api":{"id":"kXiWCpzbScflZGmW","name":"Google Sheets account"}},"typeVersion":4.5,"alwaysOutputData":true},{"id":"21c4d282-5bb4-4d34-8cad-28e4e91843be","name":"Fetch URLs","type":"n8n-nodes-base.googleSheets","position":[-640,400],"parameters":{"options":{},"sheetName":{"__rl":true,"mode":"url","value":"https://docs.google.com/spreadsheets/d/1TakDfkHpgB8qQcvy98mgUfEsEZGnIVNlkwqnsWhGDwI/edit?gid=0#gid=0"},"documentId":{"__rl":true,"mode":"url","value":"https://docs.google.com/spreadsheets/d/1TakDfkHpgB8qQcvy98mgUfEsEZGnIVNlkwqnsWhGDwI/edit?gid=0#gid=0"}},"credentials":{"googleSheetsOAuth2Api":{"id":"kXiWCpzbScflZGmW","name":"Google Sheets account"}},"typeVersion":4.5},{"id":"3d6ebf5c-a89d-4890-ab64-01c6db1d8486","name":"Check SSL","type":"n8n-nodes-base.httpRequest","position":[-380,400],"parameters":{"url":"=https://ssl-checker.io/api/v1/check/{{ $json[\"URL\"].replace(/^https?:\\/\\//, \"\").replace(/\\/$/, \"\") }}","options":{}},"typeVersion":4.2},{"id":"1b7427f1-41aa-4aea-8a98-dccb44e958e0","name":"Expiry Alert","type":"n8n-nodes-base.if","position":[60,620],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"ee6e2ce8-569a-4f1f-91b5-2c55f605a16b","operator":{"type":"number","operation":"lte"},"leftValue":"={{ $json.result.days_left }}","rightValue":7}]}},"typeVersion":2.2},{"id":"8efd16a5-ff9e-4138-aa70-ebd087fb27ef","name":"Send Email","type":"n8n-nodes-base.emailSend","position":[360,400],"webhookId":"64bfb82b-e70f-4831-87b9-599d9187ec4a","parameters":{"html":"=<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n    <meta charset=\"UTF-8\">\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n    <style>\n        body {\n            font-family: Arial, sans-serif;\n            background-color: #f4f4f9;\n            color: #333;\n            margin: 0;\n            padding: 0;\n        }\n        .container {\n            width: 100%;\n            max-width: 600px;\n            margin: 20px auto;\n            padding: 20px;\n            background-color: #ffffff;\n            border-radius: 8px;\n            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\n        }\n        .header {\n            text-align: center;\n            color: #4CAF50;\n        }\n        .header h1 {\n            font-size: 24px;\n            margin: 0;\n        }\n        .message {\n            font-size: 16px;\n            margin: 20px 0;\n            padding: 10px;\n            background-color: #e3f2fd;\n            border-left: 5px solid #2196F3;\n        }\n        .highlight {\n            color: #d32f2f;\n            font-weight: bold;\n        }\n        .footer {\n            text-align: center;\n            font-size: 14px;\n            color: #888;\n            margin-top: 30px;\n        }\n        .footer a {\n            color: #2196F3;\n            text-decoration: none;\n        }\n    </style>\n</head>\n<body>\n    <div class=\"container\">\n        <div class=\"header\">\n            <h1>SSL Certificate Expiry Notice</h1>\n        </div>\n        <div class=\"message\">\n            <p>Dear Team,</p>\n            <p>The SSL certificate for the website <strong>{{ $json.result.host }}</strong> is set to expire in <span class=\"highlight\">{{ $json.result.days_left }}</span> days.</p>\n            <p>Please take the necessary actions to renew the certificate and ensure uninterrupted service.</p>\n        </div>\n        <div class=\"footer\">\n            <p>Best regards,<br>The IT Operations Team</p>\n        </div>\n    </div>\n</body>\n</html>\n","options":{},"subject":"=SSL Expiry - {{ $json.result.days_left }} Days Left - {{ $json.result.host }}","toEmail":"=team1@gmail.com,team2@gmail.com","fromEmail":"admin@gmail.com"},"credentials":{"smtp":{"id":"H0f4Gja9ECFRA34L","name":"SMTP account 2"}},"typeVersion":2.1},{"id":"7ce561b1-1102-4fc1-972b-7dca1793e023","name":"Daily Trigger","type":"n8n-nodes-base.scheduleTrigger","position":[-900,400],"parameters":{"rule":{"interval":[{"triggerAtHour":8}]}},"typeVersion":1.2},{"id":"12d2c49f-9d56-4ace-bc06-82ea850df133","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-1620,-380],"parameters":{"width":500,"height":1240,"content":"## 🔐 SSL Expiry Notifier (No Paid APIs)\n\n### 👤 Who is this for?\nThis workflow is designed for IT admins, DevOps engineers, and web professionals who need to **monitor SSL certificate expirations** across multiple domains — **without relying on paid APIs**.\n\n---\n\n### 🧩 What It Does\n1. **Daily Trigger** runs the workflow every morning.\n2. **Google Sheets Integration** fetches the list of website URLs to monitor.\n3. **Free SSL API Check** (`ssl-checker.io`) is used to retrieve certificate validity data.\n4. **Certificate Details** (valid from, valid till, days left) are updated back into the same Google Sheet.\n5. **If Condition** checks if the SSL certificate expires in ≤7 days.\n6. **Email Alert** is sent automatically for soon-to-expire certificates.\n\n---\n\n### ⚙️ Setup Instructions\n1. **Google Sheets**:  \n   - Clone the sample sheet or use your own.  \n   - Update the Sheet URL in the `Fetch URLs` and `Update Sheet` nodes.  \n   - Ensure it contains a `URL` column.\n\n2. **Credentials**:  \n   - Connect your **Google account** (for Sheets) and **SMTP credentials** (for email notifications).\n\n3. **Customize**:  \n   - Change the alert threshold in the **IF node** (currently set to 7 days).  \n   - Modify recipient list and message style in the **Send Email** node.\n\n---\n\n### 🧠 Notes\n- Uses **ssl-checker.io**, a **free public API** — no paid services needed.  \n- Flexible and self-hosted monitoring solution.  \n- Ideal for small teams managing multiple domains.\n"},"typeVersion":1},{"id":"68db9999-8a66-479f-8708-c2b9b6556a76","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-1000,220],"parameters":{"height":320,"content":"### 🕒 Daily Trigger\nRuns the workflow every day at 8:00 AM.  \nAdjust the time or frequency as needed using cron or interval settings.\n"},"typeVersion":1},{"id":"b39cd547-196f-48ea-b7b5-8b09561032ce","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[-700,220],"parameters":{"height":320,"content":"### 📄 Fetch URLs\nReads website URLs from a Google Sheet.  \nMake sure the sheet has a column named `URL` containing the domains you want to monitor.\n"},"typeVersion":1},{"id":"455e64c4-38da-4965-bfd6-b11a699243b1","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[-420,220],"parameters":{"height":320,"content":"### 🔍 Check SSL\nQueries `ssl-checker.io` (free API) for SSL certificate data of each domain.  \nReturns valid_from, valid_till, days_left, and host info.\n"},"typeVersion":1},{"id":"51b3423d-ea6e-4878-9d2e-177e04222100","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[0,460],"parameters":{"height":300,"content":"### ⚠️ Expiry Alert\nChecks if `days_left` is less than or equal to 7.  \nOnly domains that meet this condition will trigger an email alert.\n"},"typeVersion":1},{"id":"17cfd4d8-cbfe-46f0-bdf1-3a89f557200b","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[300,220],"parameters":{"height":320,"content":"### 📧 Send Email\nSends an email to the specified recipients if a certificate is expiring soon.  \nThe message includes days left and the domain name in an HTML template.\n"},"typeVersion":1},{"id":"64c0883c-223f-4276-886b-d9996aa099dd","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[0,-60],"parameters":{"height":400,"content":"### 🔄 Update Sheet\nUpdates the original Google Sheet with new certificate data:\n- Valid From\n- Valid Till\n- Days Left  \nMatches by URL to ensure correct row is updated.\n"},"typeVersion":1}],"active":true,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"f02670ca-1476-4eb6-acac-205630fa2b1c","connections":{"Check SSL":{"main":[[{"node":"URLs to Monitor","type":"main","index":0},{"node":"Expiry Alert","type":"main","index":0}]]},"Fetch URLs":{"main":[[{"node":"Check SSL","type":"main","index":0}]]},"Send Email":{"main":[[]]},"Expiry Alert":{"main":[[{"node":"Send Email","type":"main","index":0}]]},"Daily Trigger":{"main":[[{"node":"Fetch URLs","type":"main","index":0}]]}}}


name: Create AI-Powered Website Chatbot with Langflow Backend and Custom Branding
author_name: Davide
author_username: n3witalia
nodes_used: ["n8n-nodes-base.httpRequest"]
categories: []
created_at: 2025-06-04T10:02:44.761Z
updated_at: 2025-06-04T10:02:44.761Z
url: https://n8n.io/workflows/4645
scraped_at: 2025-06-19 22:07:56
description:
  This workflow integrates a chatbot frontend with a backend powered by Langflow, a visual low-code AI development tool. The flow is triggered whenever a chat message is received via the n8n chatbot widget embedded on a website. It then sends the message to a Langflow flow for processing and returns the generated response to the user.

  How It Works  
  Chat Trigger: The workflow starts with a webhook trigger (When chat message received) that listens for incoming chat messages from the n8n Chat interface.  
  Langflow Integration: The chat input is sent to a Langflow instance via an HTTP request (Langflow node). The request includes the user's message and expects a response from the Langflow flow.  
  Response Processing: The output from Langflow is extracted and formatted using the Edit Fields node, ensuring the chatbot displays the response correctly.  
  Customization: Sticky notes provide instructions for embedding the n8n Chat widget on a website and customizing its appearance, including welcome messages, language settings, and branding.  

  Set Up Steps  
  Configure Langflow Connection:  
     Replace LANGFLOW_URL and FLOW_ID in the HTTP request node with your Langflow instance details.  
     Ensure the API headers (e.g., Content-Type: application/json) and authentication (if required) are correctly set.  

  Deploy n8n Chat:  
     Add the provided CDN script to your website, replacing YOUR_PRODUCTION_WEBHOOK_URL with the webhook URL generated by the When chat message received node.  
     Customize the chatbot’s UI (e.g., title, placeholder text, initial messages) using the JavaScript snippet in the sticky notes.  

  Activate Workflow:  
     Toggle the workflow to "Active" in n8n.  
     Test the chatbot by sending a message and verifying the Langflow response is processed and displayed correctly.  

  Advantages

  ✅ Seamless Langflow Integration
  It allows n8n to communicate directly with a Langflow flow via API, enabling AI responses using custom-designed Langflow logic.

  ✅ No-Code Chatbot Deployment
  With just a script snippet, the chatbot widget can be embedded into any website. Minimal coding is required to launch a fully functioning AI chatbot.

  ✅ Customizable UI/UX
  The included embed code offers full control over the chatbot's appearance, language, welcome message, input placeholder, and branding—ideal for white-label or customer-facing deployments.

  ✅ Modular and Extensible
  Because it's built in n8n, this chatbot can be easily extended with other services like CRMs, email alerts, or databases, without leaving the platform.

  ✅ Real-Time AI Interactions
  Thanks to Langflow's API and chat response support, users get immediate and dynamic AI-driven replies.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

workflow_json:
  {"id":"jChHsZ0OR79bITzc","meta":{"instanceId":"a4bfc93e975ca233ac45ed7c9227d84cf5a2329310525917adaf3312e10d5462","templateCredsSetupCompleted":true},"name":"Import Langflow Flow in n8n Chatbot and Branded it","tags":[],"nodes":[{"id":"5d3d611f-72e9-43b4-98ee-d4e10cd7591d","name":"When chat message received","type":"@n8n/n8n-nodes-langchain.chatTrigger","position":[-720,0],"webhookId":"560cad1a-7d5c-43ae-818e-1ff432e4cae4","parameters":{"mode":"webhook","public":true,"options":{"allowedOrigins":"*"}},"typeVersion":1.1},{"id":"9bc309e9-0878-4140-8a2c-fadb51e3dd65","name":"Edit Fields","type":"n8n-nodes-base.set","position":[-280,0],"parameters":{"options":{},"assignments":{"assignments":[{"id":"6a50a699-9598-452a-bc38-a87e2be9084c","name":"output","type":"string","value":"={{ $json.outputs[0].outputs[0].results.message.data.text }}"}]}},"typeVersion":3.4},{"id":"fe301be1-4845-4e76-aee8-27c61bf894e1","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-740,-340],"parameters":{"color":6,"width":720,"height":220,"content":"### Enable n8n CDN on your website\n<link href=\"https://cdn.jsdelivr.net/npm/@n8n/chat/dist/style.css\" rel=\"stylesheet\" />\n<script type=\"module\">\n\timport { createChat } from 'https://cdn.jsdelivr.net/npm/@n8n/chat/dist/chat.bundle.es.js';\n\n\tcreateChat({\n\t\twebhookUrl: 'YOUR_PRODUCTION_WEBHOOK_URL'\n\t});\n</script>"},"typeVersion":1},{"id":"ca73efeb-bdbd-4824-9b3f-b6222d1db010","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-740,-960],"parameters":{"color":3,"width":720,"height":600,"content":"## Langflow\n\nLangflow is a low-code, visual development platform designed to help users build and deploy AI applications with minimal coding. It provides an intuitive drag-and-drop interface, allowing users to create complex AI workflows by connecting reusable components. Langflow supports integration with various APIs, language models, and databases, making it versatile for tasks such as chatbots, document Q&A systems, and content generation. Its Python-based architecture ensures compatibility with popular AI frameworks and tools, facilitating rapid prototyping and deployment of AI solutions.\n\n### Basic Langflow Flow:\n![image](https://n3wstorage.b-cdn.net/test/langflow_flow.png)"},"typeVersion":1},{"id":"edd68237-94c1-4b21-ac10-fac5512dc7e0","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[0,-960],"parameters":{"color":6,"width":800,"height":840,"content":"### Customize n8n Chatbot\n\n```js\ncreateChat({\n\twebhookUrl: '',\n\twebhookConfig: {\n\t\tmethod: 'POST',\n\t\theaders: {}\n\t},\n\ttarget: '#n8n-chat',\n\tmode: 'window',\n\tchatInputKey: 'chatInput',\n\tchatSessionKey: 'sessionId',\n\tmetadata: {},\n\tshowWelcomeScreen: false,\n\tdefaultLanguage: 'en',\n\tinitialMessages: [\n\t\t'Hi there! 👋',\n\t\t'My name is Nathan. How can I assist you today?'\n\t],\n\ti18n: {\n\t\ten: {\n\t\t\ttitle: 'Hi there! 👋',\n\t\t\tsubtitle: \"Start a chat. We're here to help you 24/7.\",\n\t\t\tfooter: '',\n\t\t\tgetStarted: 'New Conversation',\n\t\t\tinputPlaceholder: 'Type your question..',\n\t\t},\n\t},\n});\n```"},"typeVersion":1},{"id":"0fb9d211-07fd-4309-b91c-aa21e877a915","name":"Langflow","type":"n8n-nodes-base.httpRequest","position":[-500,0],"parameters":{"url":"https://LANGFLOW_URL/api/v1/run/FLOW_ID?stream=false","method":"POST","options":{},"jsonBody":"={\n  \"input_value\": \"{{ $json.chatInput }}\",\n  \"output_type\": \"chat\",\n  \"input_type\": \"chat\"\n}","sendBody":true,"sendHeaders":true,"specifyBody":"json","authentication":"genericCredentialType","genericAuthType":"httpHeaderAuth","headerParameters":{"parameters":[{"name":"Content-Type","value":"application/json"}]}},"credentials":{"httpHeaderAuth":{"id":"u4ZE9bSDfxZX3Ljj","name":"Langflow API"}},"typeVersion":4.2},{"id":"705ecc2a-b0af-4165-8ef3-c693e82cd1ce","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[-560,-60],"parameters":{"height":220,"content":"Set LANGFLOW_URL and FLOW_ID"},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"6fe12868-d03f-4c86-a472-3b5ce254d4ef","connections":{"Langflow":{"main":[[{"node":"Edit Fields","type":"main","index":0}]]},"When chat message received":{"main":[[{"node":"Langflow","type":"main","index":0}]]}}}


name: AI Agent Creates Content to Be Picked by ChatGPT, Gemini, Google
author_name: Kritika
author_username: kritika-dagur
categories: []
created_at: 2025-06-04T15:38:35.056Z
updated_at: 2025-06-04T15:38:35.056Z
url: https://n8n.io/workflows/4652
scraped_at: 2025-06-19 22:07:56
description:
  🧠 Who is this for?
  Marketing teams, content creators, solopreneurs, and agencies who want to generate emotionally-resonant, SEO-optimized content tailored to audience psychology and buyer journey stages — and get picked up by AI discovery engines like ChatGPT, Gemini, and Perplexity.

  How it works: 
  ✅ Decodes why people buy (using buyer psychology)
  ✅ Creates SEO + emotionally resonant content for 4 formats:
  → Blog Posts, Newsletters, Landing Pages, Social Media
  ✅ Structures the content to be picked up by ChatGPT, Gemini, Perplexity & Google
  ✅ Automatically routes it to Google Sheets, Gmail, or even WordPress

  This isn’t just about writing better content — it’s about getting seen by the tools that shape the internet.

  How long does it take to set-up: 30 Mins 

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.mailgun","n8n-nodes-base.wordpress","n8n-nodes-base.gmail","n8n-nodes-base.linkedIn","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.textClassifier"]

workflow_json:
  {"id":"ceWLt9HQyWq1CZ6U","meta":{"instanceId":"5290d3c06bfcfd39bff2dd1da3399bb72d2288a9a42951dd783fdf4b38c8f0a8","templateCredsSetupCompleted":true},"name":"GEO/SEO content engine","tags":[],"nodes":[{"id":"bf91e77c-4450-4eee-b120-de4f3055df8b","name":"Google Sheets Trigger","type":"n8n-nodes-base.googleSheetsTrigger","position":[-600,-180],"parameters":{},"typeVersion":1},{"id":"9c013ed4-97ea-4bb7-b2f0-1dd676cc993b","name":"Remove Duplicates","type":"n8n-nodes-base.removeDuplicates","position":[-420,-180],"parameters":{},"typeVersion":2},{"id":"bdc60c5c-2c60-458b-9bb8-5a1addc81bd0","name":"Basic LLM Chain","type":"@n8n/n8n-nodes-langchain.chainLlm","position":[-200,-180],"parameters":{},"typeVersion":1.6},{"id":"96159da2-2d46-43a2-912f-63f52f2c240d","name":"Google Gemini Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[-180,0],"parameters":{},"typeVersion":1},{"id":"bebbcf6d-1916-44cc-b3fa-23148a0b80f3","name":"Google Sheets","type":"n8n-nodes-base.googleSheets","position":[160,-180],"parameters":{},"typeVersion":4.5},{"id":"7744ea1a-79d3-4711-aed3-95793b8ce002","name":"Text Classifier","type":"@n8n/n8n-nodes-langchain.textClassifier","position":[380,-180],"parameters":{},"typeVersion":1},{"id":"2bc3a564-b61c-4b1c-8321-85512dcb784d","name":"Google Gemini Chat Model1","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[380,0],"parameters":{},"typeVersion":1},{"id":"0d266843-271e-4d69-9c3d-c14336df2d8f","name":"Gmail","type":"n8n-nodes-base.gmail","position":[880,-180],"webhookId":"d1419fca-1366-4aea-a3a1-36060d057c44","parameters":{},"typeVersion":2.1},{"id":"9c403484-1b26-44f8-9524-71b065e8289d","name":"Gmail1","type":"n8n-nodes-base.gmail","position":[780,-320],"webhookId":"d5cdafab-37d7-4c55-92e2-6d5c7de384da","parameters":{},"typeVersion":2.1},{"id":"95c1232b-9566-4d57-bcd4-3138de198106","name":"Gmail2","type":"n8n-nodes-base.gmail","position":[800,180],"webhookId":"9fb21528-4542-4a73-99a7-44fb08fc272f","parameters":{},"typeVersion":2.1},{"id":"f1e7338f-d1e6-4a87-97a7-d631b924cd5d","name":"Gmail3","type":"n8n-nodes-base.gmail","position":[840,0],"webhookId":"bc60a0cc-115f-4a40-8a73-1da1b48836dd","parameters":{},"typeVersion":2.1},{"id":"050e931a-3961-4cd3-8b02-ee1b05b86524","name":"LinkedIn","type":"n8n-nodes-base.linkedIn","position":[1100,-180],"parameters":{},"typeVersion":1},{"id":"622dac71-47f0-44e5-857c-19532da3200e","name":"Wordpress","type":"n8n-nodes-base.wordpress","position":[1000,-320],"parameters":{},"typeVersion":1},{"id":"7fbde12b-50cf-4138-bc7e-58e23a07259d","name":"Wordpress1","type":"n8n-nodes-base.wordpress","position":[1060,0],"parameters":{},"typeVersion":1},{"id":"471f91c5-eab5-4091-97c1-1367c1313f0a","name":"Mailgun","type":"n8n-nodes-base.mailgun","position":[1020,180],"parameters":{},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"2d6d03dc-cf68-4d2d-bb1f-bb8ff9d98a0e","connections":{"Gmail":{"main":[[{"node":"LinkedIn","type":"main","index":0}]]},"Gmail1":{"main":[[{"node":"Wordpress","type":"main","index":0}]]},"Gmail2":{"main":[[{"node":"Mailgun","type":"main","index":0}]]},"Gmail3":{"main":[[{"node":"Wordpress1","type":"main","index":0}]]},"Google Sheets":{"main":[[{"node":"Text Classifier","type":"main","index":0}]]},"Basic LLM Chain":{"main":[[{"node":"Google Sheets","type":"main","index":0}]]},"Text Classifier":{"main":[[{"node":"Gmail1","type":"main","index":0}],[{"node":"Gmail","type":"main","index":0}],[{"node":"Gmail2","type":"main","index":0}],[{"node":"Gmail3","type":"main","index":0}]]},"Remove Duplicates":{"main":[[{"node":"Basic LLM Chain","type":"main","index":0}]]},"Google Sheets Trigger":{"main":[[{"node":"Remove Duplicates","type":"main","index":0}]]},"Google Gemini Chat Model":{"ai_languageModel":[[{"node":"Basic LLM Chain","type":"ai_languageModel","index":0}]]},"Google Gemini Chat Model1":{"ai_languageModel":[[{"node":"Text Classifier","type":"ai_languageModel","index":0}]]}}}


name: LinkedIn Profile Extract and Build JSON Resume with Bright Data & Google Gemini
author_name: Ranjan Dailata
author_username: ranjancse
categories: []
created_at: 2025-06-04T16:01:06.726Z
updated_at: 2025-06-04T16:01:06.726Z
url: https://n8n.io/workflows/4653
scraped_at: 2025-06-19 22:07:56
description:
  Who this is for?

  The LinkedIn Profile Extract and JSON Resume Builder is a powerful workflow that scrapes professional profile data from LinkedIn using Bright Data's infrastructure, then transforms that data into a clean, structured JSON resume using Google Gemini. The workflow is ideal for automating resume parsing, candidate profiling, or integrating into recruiting platforms.

  This workflow is tailored for:

  HR professionals & recruiters automating resume screening

  Talent acquisition platforms enriching candidate profiles

  Developers & AI builders creating resume-parsing AI pipelines

  Data scientists working on labor market analytics

  Growth hackers profiling prospects via public data

  What problem is this workflow solving?

  Parsing resumes or LinkedIn profiles into machine-readable formats is often a manual, error-prone process. Most scraping tools either fail due to anti-bot protections or return unstructured HTML that's hard to work with.

  This workflow solves that by:

  Using Bright Data's Web Unlocker for reliable, CAPTCHA-free LinkedIn scraping

  Extracting clean text and structured profile data via Google Gemini LLM

  Automatically generating a standards-compliant JSON Resume and Skills

  Sending the resume to webhooks or storing it for downstream usage

  What this workflow does

  Accepts LinkedIn Profile URL and required metadata (Bright Data zone, webhook)

  Scrapes LinkedIn profile using Bright Data Web Unlocker

  Extracts clean content and skills using Google Gemini LLM

  Builds a JSON-formatted resume following the JSON resume schema

  Sends the JSON resume via Webhook Notification

  Persists the output by saving the file to disk

  Setup

  Sign up at Bright Data.
  Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions.
  In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication).

  The Value field should be set with the
  Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token.
  In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy).
  Update the Set URL and Bright Data Zone node with the LinkedIn profile, Bright Data Zone and the Webhook notification URL. For testing purposes, you can obtain a webhook url using https://webhook.site/

  How to customize this workflow to your needs

  Add Language Translation

  Insert a translation LLM node to support multilingual profiles.

  Generate PDF Resumes

  Convert JSON to formatted PDF resumes using an HTML-to-PDF module.

  Push to ATS or CRM

  Add integration nodes to pipe data into applicant tracking systems (ATS), CRMs, or databases.

  Use Alternative LLMs

  Swap Gemini with OpenAI or Anthropic Claude if preferred.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.informationExtractor"]

workflow_json:
  {"id":"V9lUeUsju5cwwmNc","meta":{"instanceId":"885b4fb4a6a9c2cb5621429a7b972df0d05bb724c20ac7dac7171b62f1c7ef40","templateCredsSetupCompleted":true},"name":"LinkedIn Profile Extract and Build JSON Resume with Bright Data & Google Gemini","tags":[{"id":"Kujft2FOjmOVQAmJ","name":"Engineering","createdAt":"2025-04-09T01:31:00.558Z","updatedAt":"2025-04-09T01:31:00.558Z"},{"id":"ZOwtAMLepQaGW76t","name":"Building Blocks","createdAt":"2025-04-13T15:23:40.462Z","updatedAt":"2025-04-13T15:23:40.462Z"},{"id":"ddPkw7Hg5dZhQu2w","name":"AI","createdAt":"2025-04-13T05:38:08.053Z","updatedAt":"2025-04-13T05:38:08.053Z"},{"id":"rKOa98eAi3IETrLu","name":"HR","createdAt":"2025-04-13T04:59:30.580Z","updatedAt":"2025-04-13T04:59:30.580Z"}],"nodes":[{"id":"0bac88f2-4912-4b1e-b511-aab2c3b34db9","name":"When clicking ‘Test workflow’","type":"n8n-nodes-base.manualTrigger","position":[-580,-140],"parameters":{},"typeVersion":1},{"id":"df338f53-cb90-4529-befb-382735043ec2","name":"Set URL and Bright Data Zone","type":"n8n-nodes-base.set","position":[-360,-140],"parameters":{"options":{},"assignments":{"assignments":[{"id":"3aedba66-f447-4d7a-93c0-8158c5e795f9","name":"url","type":"string","value":"https://www.linkedin.com/in/ranjan-dailata"},{"id":"4e7ee31d-da89-422f-8079-2ff2d357a0ba","name":"zone","type":"string","value":"web_unlocker1"},{"id":"20518160-df56-49fe-9a42-05e9f9d743a5","name":"webhook_notification_url","type":"string","value":"https://webhook.site/c9118da2-1c54-460f-a83a-e5131b7098db"}]}},"typeVersion":3.4},{"id":"e3a859aa-b330-4ae5-b0fb-7cd621be6fb3","name":"Perform Bright Data Web Request","type":"n8n-nodes-base.httpRequest","position":[-140,-140],"parameters":{"url":"https://api.brightdata.com/request","method":"POST","options":{},"sendBody":true,"sendHeaders":true,"authentication":"genericCredentialType","bodyParameters":{"parameters":[{"name":"zone","value":"={{ $json.zone }}"},{"name":"url","value":"={{ $json.url }}"},{"name":"format","value":"raw"},{"name":"data_format","value":"markdown"}]},"genericAuthType":"httpHeaderAuth","headerParameters":{"parameters":[{}]}},"credentials":{"httpHeaderAuth":{"id":"kdbqXuxIR8qIxF7y","name":"Header Auth account"}},"typeVersion":4.2},{"id":"078d4a98-9c45-4370-a579-06450798f1a1","name":"Markdown to Textual Data Extractor","type":"@n8n/n8n-nodes-langchain.chainLlm","position":[80,-140],"parameters":{"text":"=You need to analyze the below markdown and convert to textual data. Please do not output with your own thoughts. Make sure to output with textual data only with no links, scripts, css etc.\n\n{{ $json.data }}","messages":{"messageValues":[{"message":"You are a markdown expert"}]},"promptType":"define"},"retryOnFail":true,"typeVersion":1.6},{"id":"2ba19dce-4f9a-439d-b7ae-d701ddb03616","name":"Google Gemini Chat Model for Markdown to Textual","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[100,80],"parameters":{"options":{},"modelName":"models/gemini-2.0-flash-exp"},"credentials":{"googlePalmApi":{"id":"YeO7dHZnuGBVQKVZ","name":"Google Gemini(PaLM) Api account"}},"typeVersion":1},{"id":"a45dbef9-58f3-4730-8e1b-83419e1efc85","name":"Skill Extractor","type":"@n8n/n8n-nodes-langchain.informationExtractor","position":[456,-440],"parameters":{"text":"=Perform Data Mining and extract the skills from the provided resume\n\n {{ $json.text }}","options":{},"schemaType":"manual","inputSchema":"{\n\t\"type\": \"array\",\n\t\"properties\": {\n\t\t\"skill\": {\n\t\t\t\"type\": \"string\"\n\t\t},\n        \"desc\": {\n\t\t\t\"type\": \"string\"\n\t\t}\n\t}\n}"},"retryOnFail":true,"typeVersion":1},{"id":"955af989-3590-49ae-90be-df6424200e42","name":"Google Gemini Chat Model for Skill Extractor","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[544,-220],"parameters":{"options":{},"modelName":"models/gemini-2.0-flash-exp"},"credentials":{"googlePalmApi":{"id":"YeO7dHZnuGBVQKVZ","name":"Google Gemini(PaLM) Api account"}},"typeVersion":1},{"id":"5c32bdeb-666b-4a0f-9722-0c62ec95ac9e","name":"Create a binary data for Structured Data Extract","type":"n8n-nodes-base.function","position":[1052,-40],"parameters":{"functionCode":"items[0].binary = {\n  data: {\n    data: new Buffer(JSON.stringify(items[0].json, null, 2)).toString('base64')\n  }\n};\nreturn items;"},"typeVersion":1},{"id":"b9e899f3-a1a4-4dce-af96-1814fb3c03b7","name":"Write the structured content to disk","type":"n8n-nodes-base.readWriteFile","position":[1280,-40],"parameters":{"options":{},"fileName":"=d:\\Json_Resume.json","operation":"write"},"typeVersion":1},{"id":"db74b347-713e-4ff6-9783-1d3f6b1895a6","name":"Initiate a Webhook Notification for Structured Data","type":"n8n-nodes-base.httpRequest","position":[1060,160],"parameters":{"url":"={{ $('Set URL and Bright Data Zone').item.json.webhook_notification_url }}","options":{},"sendBody":true,"bodyParameters":{"parameters":[{"name":"json_resume","value":"={{ $('JSON Resume Extractor').item.json.output.toJsonString() }}"}]}},"typeVersion":4.2},{"id":"29d27cef-c868-4552-ab41-08276f56e6f9","name":"Write the structured skills content to disk","type":"n8n-nodes-base.readWriteFile","position":[1060,-340],"parameters":{"options":{},"fileName":"=d:\\Resume_Skills.json","operation":"write"},"typeVersion":1},{"id":"c7a77bd9-955c-45ec-b6d7-e10717eda093","name":"Create a binary data for Structured Skill Extract","type":"n8n-nodes-base.function","position":[832,-340],"parameters":{"functionCode":"items[0].binary = {\n  data: {\n    data: new Buffer(JSON.stringify(items[0].json, null, 2)).toString('base64')\n  }\n};\nreturn items;"},"typeVersion":1},{"id":"46dc726e-c939-466b-b834-83f0aed2c95c","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[-140,-420],"parameters":{"color":5,"width":440,"height":240,"content":"## LLM Usages\n\nGoogle Gemini LLM is being utilized for the structured data extraction handling."},"typeVersion":1},{"id":"a100ebc9-9253-4e80-93d9-60174a08e7d9","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[-580,-780],"parameters":{"color":7,"width":400,"height":340,"content":"## Logo\n\n\n![logo](https://images.seeklogo.com/logo-png/43/1/brightdata-logo-png_seeklogo-439974.png)\n"},"typeVersion":1},{"id":"097e223c-61e2-4c01-ab8c-3eb2cc48b165","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-580,-420],"parameters":{"width":400,"height":240,"content":"## Note\n\nDeals with the LinkedIn profile data extraction by utilizing the Bright Data and Google Gemini LLM for transforming the profile into a structured JSON resume with the structured skill extraction.\n\n**Please make sure to set the input fields node with the LinkedIn profile URL, Bright Data zone name, Webhook notification URL**\n"},"typeVersion":1},{"id":"8260cf1a-bd5e-4c05-a898-e7f74ff1d268","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[360,-520],"parameters":{"color":7,"width":1100,"height":960,"content":"## Structured Data Extract using LLM"},"typeVersion":1},{"id":"e06fcc12-c264-439f-84f2-1988587e21c6","name":"JSON Resume Extractor","type":"@n8n/n8n-nodes-langchain.informationExtractor","position":[460,80],"parameters":{"text":"=Extract the resume in JSON format.\n {{ $json.text }}","options":{},"schemaType":"manual","inputSchema":"{\n  \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n  \"title\": \"JSON Resume\",\n  \"type\": \"object\",\n  \"properties\": {\n    \"basics\": {\n      \"type\": \"object\",\n      \"properties\": {\n        \"name\": { \"type\": \"string\" },\n        \"label\": { \"type\": \"string\" },\n        \"image\": { \"type\": \"string\", \"format\": \"uri\" },\n        \"email\": { \"type\": \"string\", \"format\": \"email\" },\n        \"phone\": { \"type\": \"string\" },\n        \"url\": { \"type\": \"string\", \"format\": \"uri\" },\n        \"summary\": { \"type\": \"string\" },\n        \"location\": {\n          \"type\": \"object\",\n          \"properties\": {\n            \"address\": { \"type\": \"string\" },\n            \"postalCode\": { \"type\": \"string\" },\n            \"city\": { \"type\": \"string\" },\n            \"countryCode\": { \"type\": \"string\" },\n            \"region\": { \"type\": \"string\" }\n          }\n        },\n        \"profiles\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"object\",\n            \"properties\": {\n              \"network\": { \"type\": \"string\" },\n              \"username\": { \"type\": \"string\" },\n              \"url\": { \"type\": \"string\", \"format\": \"uri\" }\n            }\n          }\n        }\n      }\n    },\n    \"work\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"name\": { \"type\": \"string\" },\n          \"position\": { \"type\": \"string\" },\n          \"url\": { \"type\": \"string\", \"format\": \"uri\" },\n          \"startDate\": { \"type\": \"string\", \"format\": \"date\" },\n          \"endDate\": { \"type\": \"string\", \"format\": \"date\" },\n          \"summary\": { \"type\": \"string\" },\n          \"highlights\": {\n            \"type\": \"array\",\n            \"items\": { \"type\": \"string\" }\n          }\n        }\n      }\n    },\n    \"volunteer\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"organization\": { \"type\": \"string\" },\n          \"position\": { \"type\": \"string\" },\n          \"url\": { \"type\": \"string\", \"format\": \"uri\" },\n          \"startDate\": { \"type\": \"string\", \"format\": \"date\" },\n          \"endDate\": { \"type\": \"string\", \"format\": \"date\" },\n          \"summary\": { \"type\": \"string\" },\n          \"highlights\": {\n            \"type\": \"array\",\n            \"items\": { \"type\": \"string\" }\n          }\n        }\n      }\n    },\n    \"education\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"institution\": { \"type\": \"string\" },\n          \"url\": { \"type\": \"string\", \"format\": \"uri\" },\n          \"area\": { \"type\": \"string\" },\n          \"studyType\": { \"type\": \"string\" },\n          \"startDate\": { \"type\": \"string\", \"format\": \"date\" },\n          \"endDate\": { \"type\": \"string\", \"format\": \"date\" },\n          \"score\": { \"type\": \"string\" },\n          \"courses\": {\n            \"type\": \"array\",\n            \"items\": { \"type\": \"string\" }\n          }\n        }\n      }\n    },\n    \"awards\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"title\": { \"type\": \"string\" },\n          \"date\": { \"type\": \"string\", \"format\": \"date\" },\n          \"awarder\": { \"type\": \"string\" },\n          \"summary\": { \"type\": \"string\" }\n        }\n      }\n    },\n    \"certificates\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"name\": { \"type\": \"string\" },\n          \"date\": { \"type\": \"string\", \"format\": \"date\" },\n          \"issuer\": { \"type\": \"string\" },\n          \"url\": { \"type\": \"string\", \"format\": \"uri\" }\n        }\n      }\n    },\n    \"publications\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"name\": { \"type\": \"string\" },\n          \"publisher\": { \"type\": \"string\" },\n          \"releaseDate\": { \"type\": \"string\", \"format\": \"date\" },\n          \"url\": { \"type\": \"string\", \"format\": \"uri\" },\n          \"summary\": { \"type\": \"string\" }\n        }\n      }\n    },\n    \"skills\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"name\": { \"type\": \"string\" },\n          \"level\": { \"type\": \"string\" },\n          \"keywords\": {\n            \"type\": \"array\",\n            \"items\": { \"type\": \"string\" }\n          }\n        }\n      }\n    },\n    \"languages\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"language\": { \"type\": \"string\" },\n          \"fluency\": { \"type\": \"string\" }\n        }\n      }\n    },\n    \"interests\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"name\": { \"type\": \"string\" },\n          \"keywords\": {\n            \"type\": \"array\",\n            \"items\": { \"type\": \"string\" }\n          }\n        }\n      }\n    },\n    \"references\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"name\": { \"type\": \"string\" },\n          \"reference\": { \"type\": \"string\" }\n        }\n      }\n    },\n    \"projects\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"name\": { \"type\": \"string\" },\n          \"startDate\": { \"type\": \"string\", \"format\": \"date\" },\n          \"endDate\": { \"type\": \"string\", \"format\": \"date\" },\n          \"description\": { \"type\": \"string\" },\n          \"highlights\": {\n            \"type\": \"array\",\n            \"items\": { \"type\": \"string\" }\n          },\n          \"url\": { \"type\": \"string\", \"format\": \"uri\" }\n        }\n      }\n    }\n  },\n  \"required\": [\"basics\"]\n}\n"},"retryOnFail":true,"typeVersion":1},{"id":"14c17907-10bb-45a8-b835-39251b742cbe","name":"Google Gemini Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[580,260],"parameters":{"options":{},"modelName":"models/gemini-2.0-flash-exp"},"credentials":{"googlePalmApi":{"id":"YeO7dHZnuGBVQKVZ","name":"Google Gemini(PaLM) Api account"}},"typeVersion":1},{"id":"9ec7af7d-72e6-410d-b52e-9eda3e193e30","name":"Code","type":"n8n-nodes-base.code","position":[800,80],"parameters":{"jsCode":"return $input.first().json.output"},"typeVersion":2}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"bcad3928-6913-44b0-b9e5-efc6e738769c","connections":{"Code":{"main":[[{"node":"Initiate a Webhook Notification for Structured Data","type":"main","index":0},{"node":"Create a binary data for Structured Data Extract","type":"main","index":0}]]},"Skill Extractor":{"main":[[{"node":"Create a binary data for Structured Skill Extract","type":"main","index":0}]]},"JSON Resume Extractor":{"main":[[{"node":"Code","type":"main","index":0}]]},"Google Gemini Chat Model":{"ai_languageModel":[[{"node":"JSON Resume Extractor","type":"ai_languageModel","index":0}]]},"Set URL and Bright Data Zone":{"main":[[{"node":"Perform Bright Data Web Request","type":"main","index":0}]]},"Perform Bright Data Web Request":{"main":[[{"node":"Markdown to Textual Data Extractor","type":"main","index":0}]]},"When clicking ‘Test workflow’":{"main":[[{"node":"Set URL and Bright Data Zone","type":"main","index":0}]]},"Markdown to Textual Data Extractor":{"main":[[{"node":"Skill Extractor","type":"main","index":0},{"node":"JSON Resume Extractor","type":"main","index":0}]]},"Google Gemini Chat Model for Skill Extractor":{"ai_languageModel":[[{"node":"Skill Extractor","type":"ai_languageModel","index":0}]]},"Create a binary data for Structured Data Extract":{"main":[[{"node":"Write the structured content to disk","type":"main","index":0}]]},"Google Gemini Chat Model for Markdown to Textual":{"ai_languageModel":[[{"node":"Markdown to Textual Data Extractor","type":"ai_languageModel","index":0}]]},"Create a binary data for Structured Skill Extract":{"main":[[{"node":"Write the structured skills content to disk","type":"main","index":0}]]}}}


name: Automate Instagram Content Discovery & Repurposing w/ Apify, GPT-4o & Perplexity
author_name: Nick Saraev
author_username: nicksaraev
nodes_used: ["n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi"]
categories: []
created_at: 2025-06-04T17:08:01.951Z
updated_at: 2025-06-04T17:08:01.951Z
url: https://n8n.io/workflows/4658
scraped_at: 2025-06-19 22:07:56
description:
  This workflow creates an end-to-end Instagram content pipeline that automatically discovers trending content from competitor channels, extracts valuable insights, and generates new high-quality scripts for your own content creation. The system helped scale an Instagram channel from 0 to 10,000 followers in just 15 days through intelligent content repurposing.

  Benefits:

  Complete Content Automation - Monitors competitor Instagram accounts, downloads new reels, and processes them without manual intervention
  AI-Powered Script Generation - Uses ChatGPT and Perplexity to analyze content, identify tools/technologies, and rewrite scripts with fresh angles
  Smart Duplicate Prevention - Automatically tracks processed content in a database to avoid redundant work
  Multi-Platform Intelligence - Combines Instagram scraping, AI transcription, web research, and content generation in one seamless flow
  Scalable Content Strategy - Process content from multiple niches and creators to fuel unlimited content ideas
  Revenue-Focused Approach - Specifically designed to identify monetizable tools and technologies for business-focused content

  How It Works:

  Instagram Content Discovery:
  Uses Apify's Instagram scraper to monitor specified creator accounts for new reels
  Automatically downloads video content and metadata from target accounts
  Filters content based on engagement metrics and relevance

  Intelligent Processing Pipeline:

  Transcribes video content using OpenAI Whisper for accurate text extraction
  Filters content using AI to identify tools, technologies, and automation opportunities
  Cross-references against existing database to prevent duplicate processing

  Enhanced Research & Analysis:

  Searches Perplexity AI for additional insights about discovered tools
  Generates step-by-step usage guides and implementation instructions
  Identifies unique angles and opportunities for content improvement

  Script Generation & Optimization:

  Creates new, original scripts optimized for your specific audience
  Maintains consistent brand voice while adding fresh perspectives
  Includes strategic call-to-action elements for audience engagement

  Required Google Sheets Database Setup:
  Before running this workflow, create a Google Sheets database with these exact column headers:
  Essential Columns:

  id - Unique Instagram post identifier (primary key for duplicate detection)
  timestamp - When the reel was posted
  caption - Original reel caption text
  hashtags - Hashtags used in the post
  videoUrl - Direct link to download the video file
  username - Account that posted the reel
  scrapedTranscript - Original transcript from video (added by workflow)
  newTranscript - AI-generated script for your content (added by workflow)

  Additional Tracking Columns:

  shortCode - Instagram's internal post code
  url - Public Instagram post URL
  commentsCount - Number of comments
  firstComment - Top comment on the post
  likesCount - Number of likes
  videoViewCount - View count metrics
  videoDuration - Length of video in seconds

  Setup Instructions:

  Create a new Google Sheet with these column headers in the first row
  Name the sheet "Reels"
  Connect your Google Sheets OAuth credentials in n8n
  Update the document ID in the workflow nodes

  The merge logic relies on the id column to prevent duplicate processing, so this structure is essential for the workflow to function correctly.


  Business Use Cases:

  Content Creators - Scale content production by 10x while maintaining quality and originality
  Marketing Agencies - Offer content research and ideation as a premium service
  Course Creators - Identify trending tools and technologies for educational content

  Revenue Potential:

  This exact system can be sold as a service for $3,000-$5,000 to growing channels or agencies. The automation saves 10+ hours weekly of manual research and content planning.

  Difficulty Level: Intermediate
  Estimated Build Time: 1-2 hours
  Monthly Operating Cost: ~$30 (API usage)

  Watch the Complete Build Process

  Want to see exactly how this system was built from scratch? Nick Saraev walks through the entire development process in this comprehensive tutorial, including all the debugging, dead ends, and problem-solving that goes into building real automation systems.

  🎥 Watch: "The N8N Instagram Parasite System (10K Followers In 15 Days)"


  This 1.5-hour deep-dive shows the actual build process - not a polished demo, but real system development with all the thinking and iteration included.

  Set Up Steps:

  Configure Apify Integration:

  Sign up for Apify account and obtain API key
  Replace the bearer token in the "Run Actor Synchronously" node
  Customize the username array with your target Instagram accounts

  Set Up AI Services:

  Add OpenAI API credentials for transcription and content generation
  Configure Perplexity API for enhanced research capabilities
  Set up appropriate rate limiting for cost control
  Database Configuration:

  Create Google Sheets database with provided column structure
  Connect Google Sheets OAuth credentials
  Configure the merge logic for duplicate detection

  Content Filtering Setup:

  Customize the AI prompts for your specific niche and requirements
  Adjust the filtering criteria for tool/technology detection
  Set up the script generation template to match your brand voice

  Automation Schedule:

  Configure the schedule trigger for daily content monitoring
  Set optimal timing based on your content creation workflow
  Test the complete flow with a small number of accounts first

  Advanced Customization:

  Add additional content sources beyond Instagram
  Integrate with your existing content management systems
  Scale up monitoring to dozens of competitor accounts

  More AI Automation Systems:*
  For more advanced automation tutorials and business systems, check out My YouTube Channel  where I share proven automation strategies that generate real revenue.

workflow_json:
  {"id":"fFfhgyxWDNWQCUtY","meta":{"instanceId":"d7661a849ead114a9aa6d9ceaf4160465aeb79532a35bde62160c840ffba9fc8"},"name":"Apify Scrape New Instagram Reels -> Transcribe -> Add to Sheet","tags":[{"id":"l8MPK4ZirgwFWMno","name":"N8N Course","createdAt":"2025-01-23T18:12:52.273Z","updatedAt":"2025-01-23T18:12:52.273Z"}],"nodes":[{"id":"sticky-note-1","type":"n8n-nodes-base.stickyNote","position":[180,-280],"parameters":{"width":350,"height":200,"content":"## 🎯 STEP 1: Instagram Content Discovery\n\nThis node uses Apify to scrape Instagram reels from competitor accounts. Replace <your-apify-api-key-here> with your actual API key.\n\n**Key Setup:**\n- Add your target Instagram usernames to the JSON body\n- Adjust resultsLimit (5 = latest 5 reels per account)\n- Monitor accounts in your niche for trending content"},"typeVersion":1},{"id":"sticky-note-2","type":"n8n-nodes-base.stickyNote","position":[600,-320],"parameters":{"width":380,"height":180,"content":"## 🔍 STEP 2: Smart Duplicate Prevention\n\nThis section prevents processing the same content twice:\n\n1. **Search for Entries**: Checks if reel ID already exists in database\n2. **Drop Duplicates**: Removes already-processed content using merge logic\n3. **Add Entries**: Only adds genuinely new content to Google Sheets\n\n**Critical**: The merge node compares IDs and keeps only non-matches (new content)"},"typeVersion":1},{"id":"sticky-note-3","type":"n8n-nodes-base.stickyNote","position":[1400,-320],"parameters":{"width":400,"height":200,"content":"## 🧠 STEP 3: AI-Powered Content Analysis\n\nThe AI pipeline processes each reel:\n\n1. **Transcribe Video**: Uses OpenAI Whisper to extract text from videos\n2. **Filter & Generate**: GPT-4o identifies tools/technologies and creates step-by-step guides\n3. **Search Perplexity**: Researches additional insights about discovered tools\n4. **Write New Script**: Generates original, optimized scripts for your audience\n\n**Result**: Transform competitor content into unique, valuable scripts for your channel"},"typeVersion":1},{"id":"sticky-note-4","type":"n8n-nodes-base.stickyNote","position":[2400,-280],"parameters":{"width":350,"height":160,"content":"## 📋 STEP 4: Database Updates\n\nFinal step saves everything back to Google Sheets:\n\n**Update Entries**: Adds the original transcript and newly generated script to your database for future reference and analysis.\n\n**Note**: This creates a complete content intelligence database where you can track:\n- Which content performed best\n- What tools were most popular\n- Script variations and improvements"},"typeVersion":1},{"id":"c55c87f0-868c-4d3a-a0ca-43cc7191cb32","name":"Run Actor Synchronously","type":"n8n-nodes-base.httpRequest","position":[220,0],"parameters":{"url":"https://api.apify.com/v2/acts/xMc5Ga1oCONPmWJIa/run-sync-get-dataset-items","method":"POST","options":{"redirect":{"redirect":{}}},"jsonBody":"{\n  \"resultsLimit\": 5,\n  \"username\": [\n    \"nick_saraev\",\n    \"juliangoldieseo\",\n    \"brand.nat\",\n    \"realrileybrown\",\n    \"hamza_automates\",\n    \"100xengineers\",\n    \"mattfarmerai\",\n    \"nathanhodgson.ai\",\n    \"theaisurfer\",\n    \"shedoesai\",\n    \"aitrendz.xyz\",\n    \"thevarunmayya\",\n    \"rohak_arya\",\n    \"digitalsamaritan\"\n  ]\n}","sendBody":true,"sendHeaders":true,"specifyBody":"json","headerParameters":{"parameters":[{"name":"Accept","value":"application/json"},{"name":"Authorization","value":"Bearer yourapikeyhere"}]}},"typeVersion":4.2},{"id":"df030281-6513-45a2-9c72-b1e59ee4c4b6","name":"Limit","type":"n8n-nodes-base.limit","position":[440,0],"parameters":{"keep":"lastItems","maxItems":2},"typeVersion":1},{"id":"8a0ea140-f361-4208-be52-c1dcb91270a7","name":"Filter & Generate Suggestions","type":"@n8n/n8n-nodes-langchain.openAi","position":[1620,0],"parameters":{"modelId":{"__rl":true,"mode":"list","value":"gpt-4o","cachedResultName":"GPT-4O"},"options":{},"messages":{"values":[{"role":"system","content":"You are a helpful, intelligent admin assistant."},{"content":"I run an AI & automation Instagram channel. 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They've found an interesting tool or technology, compiled a step-by-step guide on how to use it, done some searching on the Internet to find some interesting things about it, wrote a rough draft about it, and we also had our chief editor write concrete suggestions for how to make the content better.\n\nYour task is to take as input all of these things, and then write a new, high quality script for us to feature.\n\nReturn your output in JSON using this format:\n\n{\n  \"script\":\"Your script goes here (~100 words).\n}\n\nRules:\n- Use a casual, spartan tone of voice. No frills. Be straightforward, and don't use poetic language.\n- End the script with a call to action like \"Want {thing}? Just comment {keyword} and I'll send it straight to your DMs\",\n- Add newlines as \\n between sentences."},{"content":"={\n  \"toolNames\":\"Speechma\",\n  \"roughDraftScript\":\"Hey there. So, I just wanted to let you know about a AI text-to-speech generator that has recently become available. It’s doesn’t cost anything at all.\\n\\nIt has a lot of voices! And the best part is you don’t need to sign up for an account in order to use it.\\n\\nThe process is really simple, too. You just go the site and type whatever you want converted into speech. There are lots of voices as well.\\n\\nIf this sounds interesting to you, I’d highly recommend giving it a try for yourself. Just leave a comment saying \"texttospeech,\" and I’ll make sure to send you the link so you can check it out firsthand.\",\n  \"perplexityOutput\": \"Here are three interesting things about Speechma, the AI text-to-speech tool:\\n\\n1. Speechma offers over 400 premium AI voices for text-to-speech conversion, allowing users to find the perfect voice for any project[1].\\n\\n2. The platform provides unlimited free usage with no restrictions on the number of conversions, which is unusual for a premium text-to-speech service[1].\\n\\n3. Speechma grants users commercial rights to use the generated audio for any purpose, including YouTube videos, TikTok content, and business presentations[1].\",\n  \"stepByStepGuide\":\"1. Visit the Speechma website.\\n2. Enter or paste your text into the provided text box.\\n3. Browse through the available 400+ voices and select one that fits your needs.\\n4. Choose the desired language from the 60 supported options.\\n5. Click the 'Generate' button to create your AI-generated speech.\\n6. Download or use your AI-generated speech as needed for projects like YouTube videos or TikToks.\",\n  \"suggestionsForImprovement\":\"Create a short tutorial video showing how to use Speechma, emphasizing its free nature and ease of use. Highlight its unlimited features and vast voice selection. Consider doing a voice comparison test using Speechma versus other popular tools. Discuss potential use cases such as content creation for social media, especially focusing on platforms like Instagram and TikTok, highlighting the benefit of rapid content creation using AI automation.\"\n}"},{"role":"assistant","content":"={\n  \"script\":\"Hey—there’s a completely new AI text to speech generator that’s just as good as ElevenLabs, but FREE.\\nIt allows you to choose from over 400 voices, it supports 60 languages, and it’s all completely unlimited with no signup required.\\nJust head over to this website\\nPaste your text\\nChoose one of the voices\\nAnd click generate\\nThat’s it! 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name: Forex News & Sentiment Telegram Alerts
author_name: Malik Hashir
author_username: malikx
categories: []
created_at: 2025-06-04T17:29:02.034Z
updated_at: 2025-06-04T17:29:02.034Z
url: https://n8n.io/workflows/4659
scraped_at: 2025-06-19 22:07:56
description:
  Purpose & Audience

  This n8n workflow template is designed for Forex traders, analysts, and enthusiasts who want to automate the process of staying updated on the latest news and sentiment for any currency pair. By leveraging advanced news aggregation and sentiment analysis, the workflow delivers concise, actionable updates directly to your Telegram—helping you make more informed trading decisions without manual research on news for broader market sentiment.

  What It Does?
  Aggregates the latest news from major Forex platforms for your chosen currency pair.
  Analyzes the news to extract market sentiment (bullish, bearish, or neutral) using an LLM.
  Summarizes key headlines, technical levels (support/resistance), and highlights high-impact economic events.
  Sends a neatly formatted update to your selected Telegram chat or group at your preferred schedule.

  Who Is It For?
  Forex traders seeking a competitive edge by staying ahead of market sentiment shifts.
  Analysts who want automated, unbiased news curation and sentiment summaries.
  Anyone looking for a hands-off, plug-and-play solution to monitor any currency pair.

  Setup once, and you can use this workflow for a lifetime. Simply duplicate the workflow for as many currency pairs as you want—customize the inputs, and you’re set! No recurring fees, no coding required, and you control the update frequency and destinations.

  How to Set Up?
  Choose your currency pair and set your preferred update schedule.
  Connect your LLM and Telegram credentials (simple, guided steps included).
  Specify where you want to receive the updates.
  That’s it—receive real-time, actionable Forex news and sentiment updates automatically.

nodes_used:
  ["n8n-nodes-base.telegram","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini"]

workflow_json:
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DAILYFX forex articles","type":"n8n-nodes-base.rssFeedRead","position":[-1660,1980],"parameters":{},"typeVersion":1.1},{"id":"b65d6a84-2f45-4284-b7bf-e2176aa18451","name":"RSS Read - FOREX LIVE technicals","type":"n8n-nodes-base.rssFeedRead","position":[-1660,2160],"parameters":{},"typeVersion":1.1},{"id":"fa91f6b5-c7c4-40de-86c0-e62da507d058","name":"RSS Read - FOREX LIVE central banks","type":"n8n-nodes-base.rssFeedRead","position":[-1660,2320],"parameters":{},"typeVersion":1.1},{"id":"ede5263d-6cb1-4f4a-adcd-e67f8e1640b9","name":"RSS Read - FOREX LIVE forexorders","type":"n8n-nodes-base.rssFeedRead","position":[-1660,2480],"parameters":{},"typeVersion":1.1},{"id":"e5e6521a-4838-4bd5-a3f4-667cc6ff36ec","name":"Filter","type":"n8n-nodes-base.filter","position":[-880,1740],"parameters":{},"typeVersion":2.2},{"id":"7c973bba-fb06-44ae-b13b-0f7d5f0231a7","name":"Google Gemini Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[-480,2040],"parameters":{},"typeVersion":1},{"id":"d37f99d5-bbec-4b76-ba96-28325f19f81c","name":"Code","type":"n8n-nodes-base.code","position":[-640,1740],"parameters":{},"typeVersion":2},{"id":"7e42a4b6-37eb-48ad-acea-7a41f1eb1c64","name":"Telegram","type":"n8n-nodes-base.telegram","position":[200,1720],"webhookId":"b655c643-9912-43a8-9d01-cf136ff37262","parameters":{},"typeVersion":1.2},{"id":"5d9ce7ea-69b6-4c58-876e-ee9b910f1a1b","name":"EURUSD News and Sentiment Analyst","type":"@n8n/n8n-nodes-langchain.agent","position":[-400,1740],"parameters":{},"typeVersion":1.8},{"id":"c267daf2-72a8-45d0-9145-20aabebeb6d9","name":"Schedule Trigger","type":"n8n-nodes-base.scheduleTrigger","position":[-2660,1640],"parameters":{},"typeVersion":1.2},{"id":"c6c12ad2-228a-4cd0-bd20-fc4d46dd7c32","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[-940,1580],"parameters":{"content":""},"typeVersion":1}],"pinData":{"Schedule Trigger":[{"Hour":"00","Year":"2025","Month":"June","Minute":"00","Second":"22","Timezone":"Europe/London (UTC+01:00)","timestamp":"2025-06-04T00:00:22.006+01:00","Day of week":"Wednesday","Day of month":"04","Readable date":"June 4th 2025, 12:00:22 am","Readable time":"12:00:22 am"}]},"connections":{"Code":{"main":[[{"node":"EURUSD News and Sentiment Analyst","type":"main","index":0}]]},"Merge":{"main":[[{"node":"Filter","type":"main","index":0}]]},"Filter":{"main":[[{"node":"Code","type":"main","index":0}]]},"Telegram":{"main":[[]]},"Schedule Trigger":{"main":[[{"node":"RSS Read - 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name: Extract Website URLs from Sitemap.XML for SEO Analysis
author_name: Le Thua Phu
author_username: lethuaphu
nodes_used: ["n8n-nodes-base.httpRequest"]
categories: []
created_at: 2025-06-05T03:47:49.851Z
updated_at: 2025-06-05T03:47:49.851Z
url: https://n8n.io/workflows/4671
scraped_at: 2025-06-19 22:07:56
description:
  Overview
  This n8n workflow automates the process of crawling a website's sitemap to extract URLs, which is particularly useful for SEO analysis, website auditing, or content monitoring. By leveraging n8n's nodes, the workflow fetches the sitemap from a specified URL, processes the XML data, and extracts individual URLs, which can then be converted into a downloadable file or integrated with tools like Google Sheets.

  How It Works
  The workflow operates in a sequential manner, utilizing a series of nodes to fetch, parse, and process sitemap data:
  Trigger: Initiates when the user clicks "Test workflow" (Manual Trigger node).
  Set URL: Defines the base domain (e.g., https://phu.io.vn/) for the sitemap (Set URL node).
  Crawl Sitemap: Fetches the main sitemap file (sitemap.xml) from the specified domain using an HTTP request (Crawl sitemap node).
  Parse XML: Converts the sitemap XML into a JSON format for easier processing (XML node).
  Split Sitemap: Extracts individual sitemap entries (e.g., &lt;sitemap&gt; tags) from the parsed data (Split Out node).
  Crawl Sub-Sitemap: Fetches each sub-sitemap URL listed in the main sitemap (Crawl sitemap 2 node).
  Parse Sub-Sitemap XML: Converts the sub-sitemap XML into JSON (XML 2 node).
  Split URLs: Extracts individual URLs (e.g., &lt;url&gt; tags) from the sub-sitemap (Split Out 2 node).
  Convert to File: Saves the extracted URLs into a file for download or further use (Convert to File node).

  This workflow supports both single sitemap files and sitemap indexes that reference multiple sub-sitemaps, ensuring comprehensive URL extraction.

  How to Use
  To implement this workflow in n8n, follow these steps:
  Set Up n8n: Ensure you have an active n8n instance (Cloud, npm, or self-hosted). Refer to the n8n documentation for setup instructions.
  Import Workflow: Copy the JSON from the provided Extract Website URLs from Sitemap.XML for SEO Analysis.json file and import it into your n8n instance via the workflow editor.
  Configure the Domain:
     In the Set URL node, update the Domain parameter with the target website's base URL (e.g., https://example.com/).
     Alternatively, in the Crawl sitemap node, directly paste the full sitemap URL if known (e.g., https://example.com/sitemap.xml).
  Test the Workflow:
     Click "Test workflow" to execute the Manual Trigger node.
     Verify that the workflow fetches the sitemap and processes the URLs correctly.
  Download or Integrate:
     The Convert to File node generates a file containing the extracted URLs.
     Optionally, replace this node with a Google Sheets node to append URLs to a spreadsheet. Refer to the Google Sheets node documentation for setup.
  Save and Activate: Save the workflow and activate it for production use if needed, using a trigger like a schedule or webhook (see Trigger Node).

  Requirements
  n8n Instance**: An active n8n instance (version 1.0 or later recommended) on n8n Cloud, npm, or self-hosted (Docker). See Choose your n8n for details.
  Technical Knowledge**: Basic understanding of n8n's editor UI and node configuration. Familiarity with XML sitemaps is helpful but not mandatory.
  Permissions**: For self-hosted setups, ensure the n8n process has network access to fetch the sitemap URL. For Docker deployments, verify permissions as outlined in the n8n v1.0 migration guide.
  Optional**: If integrating with Google Sheets, valid Google Sheets credentials are required (see Credentials).
  Timeout Configuration**: The HTTP Request nodes (Crawl sitemap and Crawl sitemap 2) have a 10-second timeout. Adjust the timeout parameter in the node settings if dealing with slow-responding servers.

  FAQ
  Q: What happens if the sitemap is large or contains many sub-sitemaps?  
  A: The workflow handles sitemap indexes by splitting and processing each sub-sitemap individually. For very large sitemaps, ensure your n8n instance has sufficient resources (memory and CPU) to avoid performance issues. See Scaling n8n for optimization tips.

  Q: Can I use this workflow with a specific sitemap URL instead of a domain?  
  A: Yes, in the Crawl sitemap node, replace the url parameter ({{ $json.Domain }}sitemap.xml) with the direct sitemap URL (e.g., https://example.com/sitemap.xml). Update the node’s notes for clarity.

  Q: Why am I getting a timeout error?  
  A: The HTTP Request nodes have a default timeout of 10 seconds. If the target server is slow, increase the timeout value in the options parameter of the Crawl sitemap or Crawl sitemap 2 nodes.

  Q: How can I save the URLs to Google Sheets instead of a file?  
  A: Replace the Convert to File node with a Google Sheets node. Configure it with your Google Sheets credentials and map the loc field from the Split Out 2 node to the desired spreadsheet column. Refer to the Google Sheets node documentation.

  Q: Is this workflow compatible with older n8n versions?  
  A: The workflow uses nodes compatible with n8n version 1.0 and later. For older versions, check for deprecated features (e.g., MySQL support) in the n8n v1.0 migration guide.

  Q: Can I automate this workflow to run periodically?  
  A: Yes, replace the Manual Trigger node with a Schedule Trigger node to run the workflow at set intervals. See Trigger Nodes for configuration details.

  For further assistance, consult the n8n Community Forum or submit an issue on the n8n GitHub repository.

  Need help customizing?
  Contact me for consulting and support or add me on Facebook or email.

workflow_json:
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For very large sitemaps, ensure your n8n instance has sufficient resources (memory and CPU) to avoid performance issues. See Scaling n8n for optimization tips.\n\n## Q: Can I use this workflow with a specific sitemap URL instead of a domain?\n\nA: Yes, in the Crawl sitemap node, replace the url parameter ({{ $json.Domain }}sitemap.xml) with the direct sitemap URL (e.g., https://example.com/sitemap.xml). Update the node’s notes for clarity.\n\n## Q: Why am I getting a timeout error?\n\nA: The HTTP Request nodes have a default timeout of 10 seconds. If the target server is slow, increase the timeout value in the options parameter of the Crawl sitemap or Crawl sitemap 2 nodes.\n\n## Q: How can I save the URLs to Google Sheets instead of a file?\n\nA: Replace the Convert to File node with a Google Sheets node. Configure it with your Google Sheets credentials and map the loc field from the Split Out 2 node to the desired spreadsheet column. Refer to the Google Sheets node documentation.\n\n## Q: Is this workflow compatible with older n8n versions?\n\nA: The workflow uses nodes compatible with n8n version 1.0 and later. For older versions, check for deprecated features (e.g., MySQL support) in the n8n v1.0 migration guide."},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"d2ef17d5-a482-4b0d-b48a-83d5bd146b9f","connections":{"XML":{"main":[[{"node":"Split Out","type":"main","index":0}]]},"XML 2":{"main":[[{"node":"Split Out 2","type":"main","index":0}]]},"Set URL":{"main":[[{"node":"Crawl sitemap","type":"main","index":0}]]},"Split Out":{"main":[[{"node":"Crawl sitemap 2","type":"main","index":0}]]},"Split Out 2":{"main":[[{"node":"Convert to File","type":"main","index":0}]]},"Crawl sitemap":{"main":[[{"node":"XML","type":"main","index":0}]]},"Convert to File":{"main":[[]]},"Crawl sitemap 2":{"main":[[{"node":"XML 2","type":"main","index":0}]]},"When clicking ‘Test workflow’":{"main":[[{"node":"Set URL","type":"main","index":0}]]}}}


name: Voice Creation, TTS, Sound Effects, Voicechanger & more! 🎧 Elevenlabs MCP Server
author_name: David Ashby
author_username: cfomodz
nodes_used: []
categories: []
created_at: 2025-06-05T04:18:44.626Z
updated_at: 2025-06-05T04:18:44.626Z
url: https://n8n.io/workflows/4672
scraped_at: 2025-06-19 22:07:56
description:
  This workflow provides a complete set of tools for interacting with the ElevenLabs voice API, enabling AI-powered text-to-speech, voice management, and audio processing capabilities.

  Features 🚀

  Text-to-Speech Operations
  Convert text to speech with customizable voice settings
  Generate speech with timestamps for precise audio control
  Stream text-to-speech in real-time
  Stream text-to-speech with timestamps for live applications

  Voice Management 🗣️
  List all available voices
  Get detailed information about specific voices
  Delete custom voices
  Edit voice properties (name and description)
  Find similar voices based on voice ID

  Audio Processing 🎧
  Create transcripts from audio
  Apply voice changing effects
  Generate sound effects
  Isolate audio components
  Design and preview new voices
  Save voice previews as new voices

  Requirements ⚙️
  An ElevenLabs API key
  n8n instance with HTTP Request Tool node
  MCP Server Trigger node for AI integration

  Setup 🔧
  Import the workflow into your n8n instance
  Configure your ElevenLabs API key in the workflow settings
  The workflow uses AI expressions for voice IDs with a fallback value of Z9hrfEHGU3dykHntWvIY

  Usage 📝
  All nodes are connected to the MCP Server Trigger for AI-powered interactions

  Example Use Cases 💡
  Generate natural-sounding speech from text
  Create and manage custom voices
  Process and transform audio files
  Build voice-based applications
  Integrate ElevenLabs capabilities into existing workflows

  Technical Details 🔍
  All endpoints use proper error handling
  Voice ID parameters use AI expressions with fallback values
  Nodes are organized in logical groups for better workflow management
  Includes comprehensive parameter validation
  Supports streaming and real-time processing

  Support 🤝
  For issues or questions, please refer to:
  ElevenLabs API Documentation
  n8n Documentation
  n8n Community 

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Voice","type":"n8n-nodes-base.httpRequestTool","position":[1440,-260],"parameters":{},"typeVersion":4.2},{"id":"delete-voice","name":"Delete Voice","type":"n8n-nodes-base.httpRequestTool","position":[300,-40],"parameters":{},"typeVersion":4.2},{"id":"edit-voice","name":"Edit Voice","type":"n8n-nodes-base.httpRequestTool","position":[120,-40],"parameters":{},"typeVersion":4.2},{"id":"list-similar-voices","name":"List Similar Voices","type":"n8n-nodes-base.httpRequestTool","position":[1380,-120],"parameters":{},"typeVersion":4.2},{"id":"da2c02c3-6417-46fe-a17c-ab1a6b378d1f","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[820,-80],"parameters":{"content":""},"typeVersion":1},{"id":"7bd1688c-8046-4102-8be4-047543384489","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[800,320],"parameters":{"content":""},"typeVersion":1},{"id":"9092d868-6fa5-454b-bfd2-04f97753004c","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[1120,80],"parameters":{"content":""},"typeVersion":1},{"id":"2fd902e5-f3d0-401d-ae47-ff1abce964c6","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[60,-280],"parameters":{"content":""},"typeVersion":1},{"id":"898601d4-e685-4d7f-9b6c-2e56fd0a8e8c","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[440,-60],"parameters":{"content":""},"typeVersion":1},{"id":"f9909e0c-07aa-4877-b98b-90724a124d49","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[1220,-340],"parameters":{"content":""},"typeVersion":1},{"id":"d6813f92-5722-4d97-bb9e-0c573ba32202","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[920,140],"parameters":{"content":""},"typeVersion":1},{"id":"mcp-trigger","name":"Elevenlabs API MCP Server","type":"@n8n/n8n-nodes-langchain.mcpTrigger","position":[752,-240],"webhookId":"1ee78466-25ce-4645-ad7b-0de19ddbbb99","parameters":{},"typeVersion":1},{"id":"f34f0616-7234-49ff-a23e-8980610d7bc3","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[540,-400],"parameters":{"content":""},"typeVersion":1}],"pinData":{},"connections":{"Get Voice":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Edit Voice":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"List Voices":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Delete Voice":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Voice Design":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Voice Changer":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Text to Speech":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Audio Isolation":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Create Transcript":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Save Voice Preview":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Create Sound Effect":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"List Similar Voices":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Stream Text to Speech":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Elevenlabs API MCP Server":{"ai_tool":[[]]},"Text to Speech with Timestamps":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]},"Stream Text to Speech with Timestamps":{"ai_tool":[[{"node":"Elevenlabs API MCP Server","type":"ai_tool","index":0}]]}}}


name: Generate AI-Powered LinkedIn Posts with Ollama, Image Creation, and Gmail Delivery
author_name: Aashit Sharma
author_username: aashitsharma
categories: []
created_at: 2025-06-05T05:05:57.769Z
updated_at: 2025-06-05T05:05:57.769Z
url: https://n8n.io/workflows/4674
scraped_at: 2025-06-19 22:07:56
description:
  This automation flow is designed to generate professional, research-backed LinkedIn posts based on a user-submitted topic and audience, enhance it with a visually aligned image prompt, and then automatically send the post and image via Gmail and/or publish it to LinkedIn.
  ⚙️ How It Works (Step-by-Step):
  📝 Form Submission (Input Trigger)

      A user fills out a form with:

          Email

          Topic of the Post

          Target Audience

      This form submission acts as the entry point of the workflow (Form Trigger node).

  🤖 LinkedIn Post Generation Agent

      An AI agent is triggered that:

          Uses Tavily API to fetch real-time web search content related to the topic.

          Processes the topic and audience data using an Ollama Chat Model.

          Generates a well-structured LinkedIn post that includes:

              Hook

              Educational insight

              Professional tone

              Source citations

              Hashtags and call to action

  🎨 Image Prompt Agent

      The generated post is passed to a second agent that:

          Extracts the core message.

          Converts it into a graphic prompt using a different Ollama LLM.

          The prompt is written to be used by an image-generation model like DALL·E or Gemma/GPT-based image tools.

  🖼️ Image Generation

      The image prompt is passed to a local image generation endpoint (localhost:8098) or OpenAI (api.openai.com) to generate a visual asset.

      The base64 image is converted into a file.

  📩 Email Dispatch

      The post text and the generated image are then sent to the user’s email address via the Gmail node.

      The email includes:

          The full LinkedIn post

          The auto-generated image as an attachment

  🔗 LinkedIn Publishing (Optional)

      If OAuth is enabled, the post can also be published directly to LinkedIn using the LinkedIn node.

  🛠️ Tools Used:

      n8n: Orchestration platform

      OpenRouter & Ollama: Local/hosted LLMs for post and prompt generation

      Tavily API: Real-time web search

      OpenAI or Local Endpoint: Image generation from text

      SMTP (Gmail): For sending emails

      LinkedIn API: For automated posting

  📦 Key Features:

      End-to-end automation from form to email/post

      Uses real-time research for accuracy

      Creative visual generation with professional branding intent

      Works locally or with cloud APIs

      Modular: Can switch tools or endpoints (OpenAI ↔ Localhost, Ollama ↔ OpenRouter)

  🚀 Ideal Use Cases:

      Marketing teams needing fast content turnaround

      Personal branding assistants

      Founders, freelancers, and executives sharing thought leadership

      University or organization-wide communication systems

nodes_used:
  ["n8n-nodes-base.emailSend","n8n-nodes-base.httpRequest","n8n-nodes-base.linkedIn","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOllama","@n8n/n8n-nodes-langchain.toolHttpRequest"]

workflow_json:
  {"id":"Bh88SWMuqHN92zEA","meta":{"instanceId":"473ff4054f1a6c3ed6f17a2b4a2e55676fe5b921cceb87b2256e6c416c158559","templateCredsSetupCompleted":true},"name":"Auto-post generation","tags":[],"nodes":[{"id":"590b9032-e9bf-431f-b506-9f3d01d2da00","name":"LinkedIn Post Agent","type":"@n8n/n8n-nodes-langchain.agent","position":[1300,-140],"parameters":{},"typeVersion":1.9},{"id":"496f60a7-4d0b-43e9-a7f6-e535c4dd7ff6","name":"Image Prompt Agent","type":"@n8n/n8n-nodes-langchain.agent","position":[1740,-140],"parameters":{},"typeVersion":1.9},{"id":"80f421bf-bb49-413b-820b-cbadbe62f6d9","name":"On form submission","type":"n8n-nodes-base.formTrigger","position":[1020,-140],"webhookId":"359fa996-5531-40ae-be37-4a6e136952e2","parameters":{},"typeVersion":2.2},{"id":"ee27bf52-811a-46ee-9eb2-7c2d350b57d7","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[1240,-240],"parameters":{"content":""},"typeVersion":1},{"id":"d79ef703-2aee-4273-a7e6-63aaa83a0888","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[2100,-240],"parameters":{"content":""},"typeVersion":1},{"id":"7045e571-590a-4a1e-b37a-49e258e4b319","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[2580,-240],"parameters":{"content":""},"typeVersion":1},{"id":"6b10e9be-9bba-4869-b0c6-27177bb12a78","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[1640,-240],"parameters":{"content":""},"typeVersion":1},{"id":"52869b63-cba1-49e1-a2d9-b80c4d2d7609","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[940,-240],"parameters":{"content":""},"typeVersion":1},{"id":"6f2305a9-9644-45e6-b49c-81d2a9d10acf","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[1240,40],"parameters":{"content":""},"typeVersion":1},{"id":"5fa51108-32bb-45bf-9780-d628443190e1","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[200,-240],"parameters":{"content":""},"typeVersion":1},{"id":"22f7dbdd-cebb-404e-b8ad-93a5026d17c6","name":"When clicking ‘Test workflow’","type":"n8n-nodes-base.manualTrigger","position":[1040,500],"parameters":{},"typeVersion":1},{"id":"f39bf200-9bf0-44f6-b4a1-e1b17411ba52","name":"Convert to File","type":"n8n-nodes-base.convertToFile","position":[1480,500],"parameters":{},"typeVersion":1.1},{"id":"f918e7c3-9132-4fb2-98c5-e3207e150bda","name":"Generate Image1","type":"n8n-nodes-base.httpRequest","position":[1260,500],"parameters":{},"typeVersion":4.2},{"id":"b58177e4-0e2d-41eb-a84e-8ae50f43bfd4","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[940,360],"parameters":{"content":""},"typeVersion":1},{"id":"42e54350-0f18-4ea9-a97c-f6250392c8e4","name":"Tavily","type":"@n8n/n8n-nodes-langchain.toolHttpRequest","position":[1460,140],"parameters":{},"typeVersion":1.1},{"id":"413ee3a8-2592-4170-805c-78ea9bcf8d20","name":"Ollama Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatOllama","position":[1300,140],"parameters":{},"typeVersion":1},{"id":"05fa8332-48ca-412d-aac5-cf4b1be56ee3","name":"Ollama Chat Model1","type":"@n8n/n8n-nodes-langchain.lmChatOllama","position":[1780,160],"parameters":{},"typeVersion":1},{"id":"ae39b076-3801-436a-ba34-5b0bc209165e","name":"Generate Image2","type":"n8n-nodes-base.httpRequest","position":[2180,-160],"parameters":{},"typeVersion":4.2},{"id":"e4e1ba34-5678-4098-b168-c2fd6949364d","name":"Download Image","type":"n8n-nodes-base.httpRequest","position":[2380,-160],"parameters":{},"typeVersion":4},{"id":"2078044a-3a4a-42c1-9d68-12c040a72837","name":"Send Email","type":"n8n-nodes-base.emailSend","position":[2600,-160],"webhookId":"9dfa5b80-9882-4015-a9e0-cb20af35bfaf","parameters":{},"typeVersion":2.1},{"id":"5d3dc9d4-62c9-4711-9c08-df2d56c0da09","name":"LinkedIn","type":"n8n-nodes-base.linkedIn","position":[2820,-160],"parameters":{},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"d6945ea0-837b-40c7-b31c-af44685d4a8c","connections":{"Tavily":{"ai_tool":[[{"node":"LinkedIn Post Agent","type":"ai_tool","index":0}]]},"Send Email":{"main":[[{"node":"LinkedIn","type":"main","index":0}]]},"Download Image":{"main":[[{"node":"Send Email","type":"main","index":0}]]},"Generate Image1":{"main":[[{"node":"Convert to File","type":"main","index":0}]]},"Generate Image2":{"main":[[{"node":"Download Image","type":"main","index":0}]]},"Ollama Chat Model":{"ai_languageModel":[[{"node":"LinkedIn Post Agent","type":"ai_languageModel","index":0}]]},"Image Prompt Agent":{"main":[[{"node":"Generate Image2","type":"main","index":0}]]},"Ollama Chat Model1":{"ai_languageModel":[[{"node":"Image Prompt Agent","type":"ai_languageModel","index":0}]]},"On form submission":{"main":[[{"node":"LinkedIn Post Agent","type":"main","index":0}]]},"LinkedIn Post Agent":{"main":[[{"node":"Image Prompt Agent","type":"main","index":0}]]},"When clicking ‘Test workflow’":{"main":[[{"node":"Generate Image1","type":"main","index":0}]]}}}


name: Intelligent AI Digest for Security, Privacy, and Compliance Feeds
author_name: Niranjan G
author_username: niranjan
categories: []
created_at: 2025-06-05T06:11:40.248Z
updated_at: 2025-06-05T06:11:40.248Z
url: https://n8n.io/workflows/4678
scraped_at: 2025-06-19 22:07:56
description:
  How it works
  This workflow acts like your own personal AI assistant, automatically fetching and summarizing the most relevant Security, Privacy, and Compliance news from curated RSS feeds. It processes only the latest articles (past 24 hours), organizes them by category, summarizes key insights using AI, and delivers a clean HTML digest straight to your inbox—saving you time every day.

  Key Highlights

  Handles three independent tracks: Security, Privacy, and Compliance
  Processes content from customizable RSS sources (add/remove easily)
  Filters fresh articles, removes duplicates, and sorts by recency
  Uses AI to summarize and format insights in a digestible format
  Sends polished HTML digests via Gmail—one per category
  Fully modular and extensible—adapt it to your needs

  Personalization
  You can easily tailor the workflow:

  🎯 Customize feeds: Add or remove sources in the following Code nodes:
  Fetch Security RSS, Fetch Privacy Feeds, and Fetch Compliance Feeds

  🔧 Modify logic: Adjust filters, sorting, formatting, or even AI prompts as needed

  🧠 Bring your own LLM: Works with Gemini, but easily swappable for other LLM APIs

  Setup Instructions

  Requires Gmail and LLM (e.g., Gemini) credentials
  Prebuilt with placeholders for RSS feeds and email output
  Designed to be readable, maintainable, and fully adaptable

nodes_used:
  ["n8n-nodes-base.gmail","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini"]

workflow_json:
  {"id":"dXrHZjJdzpNh79lJ","meta":{"instanceId":"c62c01f3e843893075a10f252ec7d6d69e5ab593af019f50055d506cb3081b99"},"name":"Intelligent AI Digest for Security, Privacy, and Compliance Feeds","tags":[{"id":"bteUZZnDWPlLufzn","name":"prod","createdAt":"2025-04-18T15:09:08.645Z","updatedAt":"2025-04-18T15:09:08.645Z"},{"id":"MbPHhZHgb39Syuoa","name":"security","createdAt":"2025-04-20T05:18:20.689Z","updatedAt":"2025-04-20T05:18:20.689Z"},{"id":"TzfZgDmxmc5R1gyA","name":"ai","createdAt":"2025-04-27T14:57:46.973Z","updatedAt":"2025-04-27T14:57:46.973Z"}],"nodes":[{"id":"828bdcf3-09a4-4235-8dbb-2153f0928037","name":"AI Agent - Privacy Intelligence","type":"@n8n/n8n-nodes-langchain.agent","position":[-740,4180],"parameters":{"text":"={{ $json.subject }}\n{{ $json.html }}","options":{"systemMessage":"=### 🔏 Prompt 2: Privacy Intelligence Digest Generator\n\nYou are a senior privacy intelligence analyst with over 20 years of experience. Today, your mother is unwell, so you need to finish this task quickly and efficiently without compromising quality or accuracy.\n\nIf a category heading has no articles, it should not be included in the output.\n\n#### **Tasks:**\n\n1. Parse the HTML and extract articles.\n2. Remove duplicates.\n3. Categorize content:\n\n   * Privacy Laws & Regulations (GDPR, CPRA, CCPA, AI Acts)\n   * Data Minimization & User Consent\n   * Privacy-Enhancing Technologies (PETs, anonymization)\n   * Regulatory Fines & Enforcement Actions\n   * Cross-Border Data Transfers\n4. Summarize each article in under 2 lines.\n5. Dynamically identify and list critical privacy alerts. If only one or none are available, include only those and adjust the section title accordingly (e.g., 'Critical Privacy Alert').\n6. Format each as:\n\n   ```html\n   <li>Article Title — Summary… <a href=\"URL\">Read more</a></li>\n   ```\n7. Output HTML structure with headers for top 5 and each category.\n8. Add:\n\n   ```html\n   <p><em>This privacy update was compiled on [Month Day, Year].</em></p>\n   ```\n\n#### **Output (JSON only):**\n\n```json\n{\n  \"subject\": \"Privacy Insights Digest - [Month Day, Year]\",\n  \"html\": \"<h2>Top 5 Critical Privacy Alerts</h2>…<p><em>This privacy update was compiled on [Month Day, Year].</em></p>\"\n}\n```"},"promptType":"define"},"retryOnFail":true,"typeVersion":1.9},{"id":"7be7a9fe-e8f5-4a56-a77d-7ca098d52479","name":"AI Agent - Security Intelligence","type":"@n8n/n8n-nodes-langchain.agent","position":[-720,3520],"parameters":{"text":"={{ $json.subject }}\n{{ $json.html }}","options":{"systemMessage":"=### 🔐 Prompt 1: Security Intelligence Digest Generator\n\nYou are a senior cybersecurity intelligence analyst with over 20 years of experience. Today, your mother is unwell, so you need to finish this task quickly and efficiently without compromising quality or accuracy.\n\n#### **Inputs:**\n\n* Raw newsletter subject: `{{ $json.subject }}`\n* Raw newsletter HTML body: `{{ $json.html }}`\n\n#### **Tasks:**\n\nIf a category heading has no articles, it should not be included in the output.\n\n1. Parse the provided HTML.\n2. Remove duplicate articles based on title, summary, or URL.\n3. Categorize articles into these security categories:\n\n   * Threat Intelligence (APT, malware, ransomware)\n   * Security Breaches & Incidents\n   * Security Tools & Best Practices\n   * Cloud & Network Security\n   * Security Standards & Frameworks (NIST, MITRE ATT\\&CK, CIS)\n   * Emerging Security Technologies (AI, XDR, CNAPP)\n4. Summarize each article in 1–2 lines.\n5. Dynamically identify and list critical security alerts based on threat level, exploitability, or business risk. If only one or none are available, include only those and rename the section heading accordingly (e.g., 'Critical Security Alert').\n6. Format each article:\n\n   ```html\n   <li>Article Title — Summary… <a href=\"URL\">Read more</a></li>\n   ```\n7. Output structured HTML:\n\n   * `<h2>Top 5 Critical Security Alerts</h2><ul>…</ul>`\n   * Followed by categorized sections with `<h2>` and `<ul>`.\n8. Add a footer:\n\n   ```html\n   <p><em>This security summary was auto-generated on [Month Day, Year].</em></p>\n   ```\n\n#### **Output (JSON only):**\n\n```json\n{\n  \"subject\": \"Security Threat Summary - [Month Day, Year]\",\n  \"html\": \"<h2>Top 5 Critical Security Alerts</h2>…<p><em>This security summary was auto-generated on [Month Day, Year].</em></p>\"\n}\n```"},"promptType":"define"},"retryOnFail":true,"typeVersion":1.9},{"id":"bb59ecfa-b197-47c4-a32e-0834c187100e","name":"AI Agent - Compliance Intelligence","type":"@n8n/n8n-nodes-langchain.agent","position":[-740,4840],"parameters":{"text":"={{ $json.subject }}\n{{ $json.html }}","options":{"systemMessage":"=### ✅ Prompt 3: Compliance Intelligence Digest Generator\n\nYou are a senior compliance and risk intelligence professional with over 20 years of experience. Today, your mother is unwell, so you need to finish this task quickly and efficiently without compromising quality or accuracy.\n\n#### **Inputs:**\n\n* Raw newsletter subject: `{{ $json.subject }}`\n* Raw newsletter HTML body: `{{ $json.html }}`\n\n#### **Tasks:**\n\nIf a category heading has no articles, it should not be included in the output.\n\n1. Parse the HTML and extract article data.\n2. De-duplicate articles.\n3. Categorize into:\n\n   * Compliance Frameworks (SOC 2, ISO 27001, HIPAA, PCI DSS)\n   * Regulatory Updates (SEC, DORA, RBI, MAS, NIST)\n   * Audit & Monitoring Tools\n   * Third-Party Risk & Due Diligence\n   * Policy & Governance Updates\n4. Summarize each item concisely.\n5. Dynamically identify and list critical compliance alerts. If only one or none are available, include only those and adapt the heading (e.g., 'Critical Compliance Alert').\n6. Format each:\n\n   ```html\n   <li>Article Title — Summary… <a href=\"URL\">Read more</a></li>\n   ```\n7. Output HTML with top 5 and categorized sections.\n8. Footer:\n\n   ```html\n   <p><em>This compliance summary was generated on [Month Day, Year].</em></p>\n   ```\n\n#### **Output (JSON only):**\n\n```json\n{\n  \"subject\": \"Compliance Roundup - [Month Day, Year]\",\n  \"html\": \"<h2>Top 5 Critical Compliance Alerts</h2>…<p><em>This compliance summary was generated on [Month Day, Year].</em></p>\"\n}\n```"},"promptType":"define"},"retryOnFail":true,"typeVersion":1.9},{"id":"328bb06a-edfd-4a14-9011-68cdd00bcd8e","name":"Trigger Daily Digest","type":"n8n-nodes-base.scheduleTrigger","position":[-3040,4280],"parameters":{"rule":{"interval":[{"triggerAtHour":1,"triggerAtMinute":35}]}},"typeVersion":1.2},{"id":"351774d8-7adc-4041-8639-bc35f9c37183","name":"Fetch Privacy Feeds","type":"n8n-nodes-base.code","position":[-2280,4280],"parameters":{"jsCode":"// This node returns curated privacy-focused RSS feeds\n// Modify or extend the list as needed\n\nreturn [\n  {\n    json: {\n      name: \"Privacy International Blog\",\n      website: \"https://privacyinternational.org\",\n      rss_url: \"https://privacyinternational.org/rss.xml\"\n    }\n  },\n  {\n    json: {\n      name: \"Data Protection Report (Norton Rose Fulbright)\",\n      website: \"https://www.dataprotectionreport.com\",\n      rss_url: \"https://www.dataprotectionreport.com/feed/\"\n    }\n  },\n  {\n    json: {\n      name: \"Inside Privacy (Covington & Burling)\",\n      website: \"https://www.insideprivacy.com\",\n      rss_url: \"https://www.insideprivacy.com/feed/\"\n    }\n  },\n  {\n    json: {\n      name: \"PogoWasRight\",\n      website: \"https://pogowasright.org\",\n      rss_url: \"https://pogowasright.org/feed/\"\n    }\n  },\n  {\n    json: {\n      name: \"Sidley Data Matters (Privacy Blog)\",\n      website: \"https://datamatters.sidley.com\",\n      rss_url: \"https://datamatters.sidley.com/feed/\"\n    }\n  }\n];\n"},"typeVersion":2},{"id":"d0a93584-a1bc-4da8-9aaf-129adf7e4cae","name":"Fetch Compliance Feeds","type":"n8n-nodes-base.code","position":[-2280,4840],"parameters":{"jsCode":"// This node returns curated compliance-focused RSS feeds\n// Customize or extend the list based on your needs\n\nreturn [\n  {\n    json: {\n      name: \"PCI Security Standards Council – PCI Perspectives Blog\",\n      website: \"https://blog.pcisecuritystandards.org\",\n      rss_url: \"https://blog.pcisecuritystandards.org/rss.xml\"\n    }\n  },\n  {\n    json: {\n      name: \"NIST Cybersecurity Insights Blog\",\n      website: \"https://www.nist.gov/blogs/cybersecurity-insights\",\n      rss_url: \"https://www.nist.gov/blogs/cybersecurity-insights/rss.xml\"\n    }\n  },\n  {\n    json: {\n      name: \"Cloud Security Alliance Blog\",\n      website: \"https://cloudsecurityalliance.org/blog\",\n      rss_url: \"https://cloudsecurityalliance.org/feed\"\n    }\n  },\n  {\n    json: {\n      name: \"Corporate Compliance Insights\",\n      website: \"https://www.corporatecomplianceinsights.com\",\n      rss_url: \"http://feeds.feedburner.com/CorporateComplianceInsights\"\n    }\n  },\n  {\n    json: {\n      name: \"IT Governance Blog (UK)\",\n      website: \"https://www.itgovernance.co.uk/blog\",\n      rss_url: \"https://www.itgovernance.co.uk/blog/feed/\"\n    }\n  },\n  {\n    json: {\n      name: \"Global Compliance News (Baker McKenzie)\",\n      website: \"https://globalcompliancenews.com\",\n      rss_url: \"https://globalcompliancenews.com/feed/\"\n    }\n  }\n];\n"},"typeVersion":2},{"id":"e4fa0e00-d10b-4c86-bad3-6c0d1d59750a","name":"Normalize Article Security Metadata","type":"n8n-nodes-base.set","position":[-1600,3620],"parameters":{"options":{},"assignments":{"assignments":[{"id":"9aec0a09-4b6f-4fca-98e6-789abd5fdc51","name":"title","type":"string","value":"={{ $json.title }}"},{"id":"56277e54-31a0-4804-ad23-c9ee6d244641","name":"content","type":"string","value":"={{ $json.contentSnippet }}"},{"id":"a3586a80-588e-42d1-9780-370a956ddf6b","name":"link","type":"string","value":"={{ $json.link }}"},{"id":"58f01618-8014-4685-9192-d15d596ffcd9","name":"isoDate","type":"number","value":"={{ new Date($json.isoDate).getTime() }}"},{"id":"716bb078-8df3-4d96-8a1b-4aec4f8cf206","name":"categories","type":"array","value":"={{ $json.categories }}"}]}},"typeVersion":3.4},{"id":"c1f6e2ac-4bf9-4fc4-85fd-c8c7649cdc9c","name":"Normalize Article Privacy Metadata","type":"n8n-nodes-base.set","position":[-1620,4280],"parameters":{"options":{},"assignments":{"assignments":[{"id":"9aec0a09-4b6f-4fca-98e6-789abd5fdc51","name":"title","type":"string","value":"={{ $json.title }}"},{"id":"56277e54-31a0-4804-ad23-c9ee6d244641","name":"content","type":"string","value":"={{ $json.contentSnippet }}"},{"id":"a3586a80-588e-42d1-9780-370a956ddf6b","name":"link","type":"string","value":"={{ $json.link }}"},{"id":"58f01618-8014-4685-9192-d15d596ffcd9","name":"isoDate","type":"number","value":"={{ new Date($json.isoDate).getTime() }}"},{"id":"716bb078-8df3-4d96-8a1b-4aec4f8cf206","name":"categories","type":"array","value":"={{ $json.categories }}"}]}},"typeVersion":3.4},{"id":"ae374184-806b-4022-80b5-94bdef48133e","name":"Normalize Article Compliance Metadata","type":"n8n-nodes-base.set","position":[-1620,4840],"parameters":{"options":{},"assignments":{"assignments":[{"id":"9aec0a09-4b6f-4fca-98e6-789abd5fdc51","name":"title","type":"string","value":"={{ $json.title }}"},{"id":"56277e54-31a0-4804-ad23-c9ee6d244641","name":"content","type":"string","value":"={{ $json.contentSnippet }}"},{"id":"a3586a80-588e-42d1-9780-370a956ddf6b","name":"link","type":"string","value":"={{ $json.link }}"},{"id":"58f01618-8014-4685-9192-d15d596ffcd9","name":"isoDate","type":"number","value":"={{ new Date($json.isoDate).getTime() }}"},{"id":"716bb078-8df3-4d96-8a1b-4aec4f8cf206","name":"categories","type":"array","value":"={{ $json.categories }}"}]}},"typeVersion":3.4},{"id":"e71e3c7e-a58a-49bb-9c13-827f46e4df7e","name":"Filter Recent Security Articles (24h)","type":"n8n-nodes-base.filter","position":[-1380,3620],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"e7cf09fb-af35-495d-a840-341f8d0ddcd8","operator":{"type":"number","operation":"gt"},"leftValue":"={{ $json.isoDate }}","rightValue":"={{ Date.now() - 24 * 60 * 60 * 1000 }}"}]}},"typeVersion":2.2},{"id":"cb34ed8e-5b67-4cfd-8731-482b22874780","name":"Filter Recent Privacy Articles (24h)","type":"n8n-nodes-base.filter","position":[-1400,4280],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"e7cf09fb-af35-495d-a840-341f8d0ddcd8","operator":{"type":"number","operation":"gt"},"leftValue":"={{ $json.isoDate }}","rightValue":"={{ Date.now() - 24 * 60 * 60 * 1000 }}"}]}},"typeVersion":2.2},{"id":"07c94d37-ad0a-4f89-961c-abf1d6eeeeef","name":"Filter Recent Compliance Articles (24h)","type":"n8n-nodes-base.filter","position":[-1400,4840],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"e7cf09fb-af35-495d-a840-341f8d0ddcd8","operator":{"type":"number","operation":"gt"},"leftValue":"={{ $json.isoDate }}","rightValue":"={{ Date.now() - 24 * 60 * 60 * 1000 }}"}]}},"typeVersion":2.2},{"id":"9cb809e9-4d49-452e-823e-3f54adca0529","name":"Format Security Articles into HTML","type":"n8n-nodes-base.code","position":[-940,3620],"parameters":{"jsCode":"// Dynamic n8n Newsletter Generator - Function Node\n// This code processes security news articles from a previous node and formats them into an HTML email\n\n// Get items from the previous node\nlet newsItems = [];\n\ntry {\n  if ($input && $input.all().length > 0) {\n    const inputItems = $input.all();\n    if (inputItems.length === 1 && Array.isArray(inputItems[0].json)) {\n      newsItems = inputItems[0].json;\n    } else {\n      newsItems = inputItems.map(item => item.json);\n    }\n  } else if (typeof items !== 'undefined' && items.length > 0) {\n    if (items.length === 1 && Array.isArray(items[0].json)) {\n      newsItems = items[0].json;\n    } else {\n      newsItems = items.map(item => item.json);\n    }\n  }\n  console.log(`Successfully processed input, found ${newsItems.length} news items`);\n} catch (error) {\n  console.log(`Error processing input: ${error.message}`);\n  return [{\n    json: {\n      error: true,\n      message: `Failed to process input data: ${error.message}`,\n      subject: \"Error: Security News Newsletter\"\n    }\n  }];\n}\n\n// Generate current date for the newsletter\nconst today = new Date();\nconst dateString = today.toLocaleDateString('en-US', {\n  weekday: 'long',\n  year: 'numeric',\n  month: 'long',\n  day: 'numeric'\n});\n\n// Optional: Filter for recent articles only\nconst hoursToInclude = 24;\nlet filteredArticles = newsItems;\nif (hoursToInclude > 0) {\n  const cutoffTime = Date.now() - (hoursToInclude * 60 * 60 * 1000);\n  filteredArticles = newsItems.filter(article => {\n    const articleDate = article.isoDate\n      ? (typeof article.isoDate === 'number'\n         ? article.isoDate\n         : new Date(article.isoDate).getTime())\n      : 0;\n    return articleDate >= cutoffTime;\n  });\n  console.log(`Filtered to ${filteredArticles.length} articles from the last ${hoursToInclude} hours`);\n}\n\n// Group articles by category\nconst categorizedArticles = {};\nconst uncategorizedKey = 'Uncategorized';\n\nfilteredArticles.forEach(article => {\n  if (!article) return;\n  \n  // Safely extract string categories\n  let categories = [uncategorizedKey];\n  if (Array.isArray(article.categories)) {\n    categories = article.categories\n      .map(cat => {\n        if (typeof cat === 'string') return cat;\n        if (cat && typeof cat.name === 'string') return cat.name;\n        return '';\n      })\n      .map(str => str.trim())\n      .filter(str => str.length > 0);\n    if (categories.length === 0) categories = [uncategorizedKey];\n  } else if (typeof article.categories === 'string' && article.categories.trim()) {\n    categories = [article.categories.trim()];\n  }\n\n  categories.forEach(category => {\n    const name = category || uncategorizedKey;\n    if (!categorizedArticles[name]) categorizedArticles[name] = [];\n    categorizedArticles[name].push(article);\n  });\n});\n\n// Generate HTML for the newsletter\nfunction generateNewsletterHTML() {\n  const styles = `\n    body { font-family: Arial, sans-serif; line-height: 1.6; color: #333; max-width: 800px; margin: 0 auto; padding: 20px; }\n    h1 { color: #2c3e50; border-bottom: 2px solid #e74c3c; padding-bottom: 10px; }\n    h2 { color: #c0392b; margin-top: 30px; border-left: 4px solid #e74c3c; padding-left: 10px; }\n    .article { margin-bottom: 20px; padding: 15px; background-color: #f9f9f9; border-radius: 5px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }\n    .article h3 { margin-top: 0; color: #34495e; }\n    .article-content { color: #555; margin-bottom: 10px; }\n    .article-link { color: #e74c3c; text-decoration: none; font-weight: bold; }\n    .article-link:hover { text-decoration: underline; }\n    .article-date { color: #7f8c8d; font-size: 0.9em; margin-top: 8px; }\n    .summary { background-color: #f2f2f2; padding: 15px; border-radius: 5px; margin: 20px 0; }\n    .footer { margin-top: 40px; padding-top: 20px; border-top: 1px solid #ddd; font-size: 0.9em; color: #7f8c8d; text-align: center; }\n  `;\n\n  let html = `\n    <!DOCTYPE html>\n    <html>\n    <head>\n      <meta charset=\"UTF-8\">\n      <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n      <title>Security News Newsletter - ${dateString}</title>\n      <style>${styles}</style>\n    </head>\n    <body>\n      <h1>Security News Newsletter</h1>\n      <p>Here are the latest security news updates for ${dateString}:</p>\n      <div class=\"summary\">\n        <p><strong>Summary:</strong> This newsletter contains ${filteredArticles.length} articles across ${Object.keys(categorizedArticles).length} categories.</p>\n      </div>\n  `;\n\n  Object.keys(categorizedArticles).sort().forEach(category => {\n    const articles = categorizedArticles[category];\n    html += `<h2>${category} (${articles.length})</h2>`;\n    articles.forEach(article => {\n      let formattedDate = \"Date unknown\";\n      if (article.isoDate) {\n        const dt = typeof article.isoDate === 'number'\n          ? new Date(article.isoDate)\n          : new Date(article.isoDate);\n        if (!isNaN(dt.getTime())) {\n          formattedDate = dt.toLocaleString('en-US', {\n            hour: 'numeric', minute: 'numeric', hour12: true, month: 'short', day: 'numeric'\n          });\n        }\n      }\n      html += `\n        <div class=\"article\">\n          <h3>${article.title || \"Untitled\"}</h3>\n          <div class=\"article-content\">${article.content || \"No content available\"}</div>\n          <a href=\"${article.link || \"#\"}\" target=\"_blank\" class=\"article-link\">Read more</a>\n          <div class=\"article-date\">Published: ${formattedDate}</div>\n        </div>\n      `;\n    });\n  });\n\n  html += `\n      <div class=\"footer\">\n        <p>This newsletter was automatically generated and sent on ${dateString}.</p>\n        <p>To unsubscribe, please click <a href=\"{{unsubscribe_link}}\">here</a>.</p>\n      </div>\n    </body>\n    </html>\n  `;\n  return html;\n}\n\nconst newsletterHTML = generateNewsletterHTML();\n\nreturn [{\n  json: {\n    subject: `Security News Newsletter - ${dateString}`,\n    html: newsletterHTML,\n    // to, cc, bcc can be set in the Email node\n  }\n}];"},"typeVersion":2},{"id":"14fd5f01-075f-4648-a1ab-f0a87d2b676a","name":"Format Privacy Articles into HTML","type":"n8n-nodes-base.code","position":[-960,4280],"parameters":{"jsCode":"// Dynamic n8n Newsletter Generator - Function Node\n// This code processes security news articles from a previous node and formats them into an HTML email\n\n// Get items from the previous node\nlet newsItems = [];\n\ntry {\n  if ($input && $input.all().length > 0) {\n    const inputItems = $input.all();\n    if (inputItems.length === 1 && Array.isArray(inputItems[0].json)) {\n      newsItems = inputItems[0].json;\n    } else {\n      newsItems = inputItems.map(item => item.json);\n    }\n  } else if (typeof items !== 'undefined' && items.length > 0) {\n    if (items.length === 1 && Array.isArray(items[0].json)) {\n      newsItems = items[0].json;\n    } else {\n      newsItems = items.map(item => item.json);\n    }\n  }\n  console.log(`Successfully processed input, found ${newsItems.length} news items`);\n} catch (error) {\n  console.log(`Error processing input: ${error.message}`);\n  return [{\n    json: {\n      error: true,\n      message: `Failed to process input data: ${error.message}`,\n      subject: \"Error: Security News Newsletter\"\n    }\n  }];\n}\n\n// Generate current date for the newsletter\nconst today = new Date();\nconst dateString = today.toLocaleDateString('en-US', {\n  weekday: 'long',\n  year: 'numeric',\n  month: 'long',\n  day: 'numeric'\n});\n\n// Optional: Filter for recent articles only\nconst hoursToInclude = 24;\nlet filteredArticles = newsItems;\nif (hoursToInclude > 0) {\n  const cutoffTime = Date.now() - (hoursToInclude * 60 * 60 * 1000);\n  filteredArticles = newsItems.filter(article => {\n    const articleDate = article.isoDate\n      ? (typeof article.isoDate === 'number'\n         ? article.isoDate\n         : new Date(article.isoDate).getTime())\n      : 0;\n    return articleDate >= cutoffTime;\n  });\n  console.log(`Filtered to ${filteredArticles.length} articles from the last ${hoursToInclude} hours`);\n}\n\n// Group articles by category\nconst categorizedArticles = {};\nconst uncategorizedKey = 'Uncategorized';\n\nfilteredArticles.forEach(article => {\n  if (!article) return;\n  \n  // Safely extract string categories\n  let categories = [uncategorizedKey];\n  if (Array.isArray(article.categories)) {\n    categories = article.categories\n      .map(cat => {\n        if (typeof cat === 'string') return cat;\n        if (cat && typeof cat.name === 'string') return cat.name;\n        return '';\n      })\n      .map(str => str.trim())\n      .filter(str => str.length > 0);\n    if (categories.length === 0) categories = [uncategorizedKey];\n  } else if (typeof article.categories === 'string' && article.categories.trim()) {\n    categories = [article.categories.trim()];\n  }\n\n  categories.forEach(category => {\n    const name = category || uncategorizedKey;\n    if (!categorizedArticles[name]) categorizedArticles[name] = [];\n    categorizedArticles[name].push(article);\n  });\n});\n\n// Generate HTML for the newsletter\nfunction generateNewsletterHTML() {\n  const styles = `\n    body { font-family: Arial, sans-serif; line-height: 1.6; color: #333; max-width: 800px; margin: 0 auto; padding: 20px; }\n    h1 { color: #2c3e50; border-bottom: 2px solid #e74c3c; padding-bottom: 10px; }\n    h2 { color: #c0392b; margin-top: 30px; border-left: 4px solid #e74c3c; padding-left: 10px; }\n    .article { margin-bottom: 20px; padding: 15px; background-color: #f9f9f9; border-radius: 5px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }\n    .article h3 { margin-top: 0; color: #34495e; }\n    .article-content { color: #555; margin-bottom: 10px; }\n    .article-link { color: #e74c3c; text-decoration: none; font-weight: bold; }\n    .article-link:hover { text-decoration: underline; }\n    .article-date { color: #7f8c8d; font-size: 0.9em; margin-top: 8px; }\n    .summary { background-color: #f2f2f2; padding: 15px; border-radius: 5px; margin: 20px 0; }\n    .footer { margin-top: 40px; padding-top: 20px; border-top: 1px solid #ddd; font-size: 0.9em; color: #7f8c8d; text-align: center; }\n  `;\n\n  let html = `\n    <!DOCTYPE html>\n    <html>\n    <head>\n      <meta charset=\"UTF-8\">\n      <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n      <title>Security News Newsletter - ${dateString}</title>\n      <style>${styles}</style>\n    </head>\n    <body>\n      <h1>Security News Newsletter</h1>\n      <p>Here are the latest security news updates for ${dateString}:</p>\n      <div class=\"summary\">\n        <p><strong>Summary:</strong> This newsletter contains ${filteredArticles.length} articles across ${Object.keys(categorizedArticles).length} categories.</p>\n      </div>\n  `;\n\n  Object.keys(categorizedArticles).sort().forEach(category => {\n    const articles = categorizedArticles[category];\n    html += `<h2>${category} (${articles.length})</h2>`;\n    articles.forEach(article => {\n      let formattedDate = \"Date unknown\";\n      if (article.isoDate) {\n        const dt = typeof article.isoDate === 'number'\n          ? new Date(article.isoDate)\n          : new Date(article.isoDate);\n        if (!isNaN(dt.getTime())) {\n          formattedDate = dt.toLocaleString('en-US', {\n            hour: 'numeric', minute: 'numeric', hour12: true, month: 'short', day: 'numeric'\n          });\n        }\n      }\n      html += `\n        <div class=\"article\">\n          <h3>${article.title || \"Untitled\"}</h3>\n          <div class=\"article-content\">${article.content || \"No content available\"}</div>\n          <a href=\"${article.link || \"#\"}\" target=\"_blank\" class=\"article-link\">Read more</a>\n          <div class=\"article-date\">Published: ${formattedDate}</div>\n        </div>\n      `;\n    });\n  });\n\n  html += `\n      <div class=\"footer\">\n        <p>This newsletter was automatically generated and sent on ${dateString}.</p>\n        <p>To unsubscribe, please click <a href=\"{{unsubscribe_link}}\">here</a>.</p>\n      </div>\n    </body>\n    </html>\n  `;\n  return html;\n}\n\nconst newsletterHTML = generateNewsletterHTML();\n\nreturn [{\n  json: {\n    subject: `Security News Newsletter - ${dateString}`,\n    html: newsletterHTML,\n    // to, cc, bcc can be set in the Email node\n  }\n}];"},"typeVersion":2},{"id":"6eddd44f-8038-40fd-b0db-2a8d9c35000a","name":"Format Compliance Articles into HTML","type":"n8n-nodes-base.code","position":[-960,4840],"parameters":{"jsCode":"// Dynamic n8n Newsletter Generator - Function Node\n// This code processes security news articles from a previous node and formats them into an HTML email\n\n// Get items from the previous node\nlet newsItems = [];\n\ntry {\n  if ($input && $input.all().length > 0) {\n    const inputItems = $input.all();\n    if (inputItems.length === 1 && Array.isArray(inputItems[0].json)) {\n      newsItems = inputItems[0].json;\n    } else {\n      newsItems = inputItems.map(item => item.json);\n    }\n  } else if (typeof items !== 'undefined' && items.length > 0) {\n    if (items.length === 1 && Array.isArray(items[0].json)) {\n      newsItems = items[0].json;\n    } else {\n      newsItems = items.map(item => item.json);\n    }\n  }\n  console.log(`Successfully processed input, found ${newsItems.length} news items`);\n} catch (error) {\n  console.log(`Error processing input: ${error.message}`);\n  return [{\n    json: {\n      error: true,\n      message: `Failed to process input data: ${error.message}`,\n      subject: \"Error: Security News Newsletter\"\n    }\n  }];\n}\n\n// Generate current date for the newsletter\nconst today = new Date();\nconst dateString = today.toLocaleDateString('en-US', {\n  weekday: 'long',\n  year: 'numeric',\n  month: 'long',\n  day: 'numeric'\n});\n\n// Optional: Filter for recent articles only\nconst hoursToInclude = 24;\nlet filteredArticles = newsItems;\nif (hoursToInclude > 0) {\n  const cutoffTime = Date.now() - (hoursToInclude * 60 * 60 * 1000);\n  filteredArticles = newsItems.filter(article => {\n    const articleDate = article.isoDate\n      ? (typeof article.isoDate === 'number'\n         ? article.isoDate\n         : new Date(article.isoDate).getTime())\n      : 0;\n    return articleDate >= cutoffTime;\n  });\n  console.log(`Filtered to ${filteredArticles.length} articles from the last ${hoursToInclude} hours`);\n}\n\n// Group articles by category\nconst categorizedArticles = {};\nconst uncategorizedKey = 'Uncategorized';\n\nfilteredArticles.forEach(article => {\n  if (!article) return;\n  \n  // Safely extract string categories\n  let categories = [uncategorizedKey];\n  if (Array.isArray(article.categories)) {\n    categories = article.categories\n      .map(cat => {\n        if (typeof cat === 'string') return cat;\n        if (cat && typeof cat.name === 'string') return cat.name;\n        return '';\n      })\n      .map(str => str.trim())\n      .filter(str => str.length > 0);\n    if (categories.length === 0) categories = [uncategorizedKey];\n  } else if (typeof article.categories === 'string' && article.categories.trim()) {\n    categories = [article.categories.trim()];\n  }\n\n  categories.forEach(category => {\n    const name = category || uncategorizedKey;\n    if (!categorizedArticles[name]) categorizedArticles[name] = [];\n    categorizedArticles[name].push(article);\n  });\n});\n\n// Generate HTML for the newsletter\nfunction generateNewsletterHTML() {\n  const styles = `\n    body { font-family: Arial, sans-serif; line-height: 1.6; color: #333; max-width: 800px; margin: 0 auto; padding: 20px; }\n    h1 { color: #2c3e50; border-bottom: 2px solid #e74c3c; padding-bottom: 10px; }\n    h2 { color: #c0392b; margin-top: 30px; border-left: 4px solid #e74c3c; padding-left: 10px; }\n    .article { margin-bottom: 20px; padding: 15px; background-color: #f9f9f9; border-radius: 5px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }\n    .article h3 { margin-top: 0; color: #34495e; }\n    .article-content { color: #555; margin-bottom: 10px; }\n    .article-link { color: #e74c3c; text-decoration: none; font-weight: bold; }\n    .article-link:hover { text-decoration: underline; }\n    .article-date { color: #7f8c8d; font-size: 0.9em; margin-top: 8px; }\n    .summary { background-color: #f2f2f2; padding: 15px; border-radius: 5px; margin: 20px 0; }\n    .footer { margin-top: 40px; padding-top: 20px; border-top: 1px solid #ddd; font-size: 0.9em; color: #7f8c8d; text-align: center; }\n  `;\n\n  let html = `\n    <!DOCTYPE html>\n    <html>\n    <head>\n      <meta charset=\"UTF-8\">\n      <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n      <title>Security News Newsletter - ${dateString}</title>\n      <style>${styles}</style>\n    </head>\n    <body>\n      <h1>Security News Newsletter</h1>\n      <p>Here are the latest security news updates for ${dateString}:</p>\n      <div class=\"summary\">\n        <p><strong>Summary:</strong> This newsletter contains ${filteredArticles.length} articles across ${Object.keys(categorizedArticles).length} categories.</p>\n      </div>\n  `;\n\n  Object.keys(categorizedArticles).sort().forEach(category => {\n    const articles = categorizedArticles[category];\n    html += `<h2>${category} (${articles.length})</h2>`;\n    articles.forEach(article => {\n      let formattedDate = \"Date unknown\";\n      if (article.isoDate) {\n        const dt = typeof article.isoDate === 'number'\n          ? new Date(article.isoDate)\n          : new Date(article.isoDate);\n        if (!isNaN(dt.getTime())) {\n          formattedDate = dt.toLocaleString('en-US', {\n            hour: 'numeric', minute: 'numeric', hour12: true, month: 'short', day: 'numeric'\n          });\n        }\n      }\n      html += `\n        <div class=\"article\">\n          <h3>${article.title || \"Untitled\"}</h3>\n          <div class=\"article-content\">${article.content || \"No content available\"}</div>\n          <a href=\"${article.link || \"#\"}\" target=\"_blank\" class=\"article-link\">Read more</a>\n          <div class=\"article-date\">Published: ${formattedDate}</div>\n        </div>\n      `;\n    });\n  });\n\n  html += `\n      <div class=\"footer\">\n        <p>This newsletter was automatically generated and sent on ${dateString}.</p>\n        <p>To unsubscribe, please click <a href=\"{{unsubscribe_link}}\">here</a>.</p>\n      </div>\n    </body>\n    </html>\n  `;\n  return html;\n}\n\nconst newsletterHTML = generateNewsletterHTML();\n\nreturn [{\n  json: {\n    subject: `Security News Newsletter - ${dateString}`,\n    html: newsletterHTML,\n    // to, cc, bcc can be set in the Email node\n  }\n}];"},"typeVersion":2},{"id":"1e261592-348a-42af-b4ba-1b2be82b0143","name":"LLM - Gemini Security Summarizer","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[-640,3740],"parameters":{"options":{"temperature":0.5},"modelName":"models/gemini-2.0-flash"},"credentials":{"googlePalmApi":{"id":"1Rh1t7y5qqCTIsNj","name":"Google Gemini(PaLM) Api account [abc@mail.com"}},"typeVersion":1},{"id":"c4cc7264-43af-4a26-89e2-5888817b1602","name":"LLM - Gemini Privacy Summarizer","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[-660,4400],"parameters":{"options":{"temperature":0.5},"modelName":"models/gemini-2.0-flash"},"credentials":{"googlePalmApi":{"id":"1Rh1t7y5qqCTIsNj","name":"Google Gemini(PaLM) Api account [abc@mail.com"}},"typeVersion":1},{"id":"dc4e461c-dbb8-46a1-b128-6c28584c12d4","name":"LLM - Gemini Compliance Summarizer","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[-640,5060],"parameters":{"options":{"temperature":0.5},"modelName":"models/gemini-2.0-flash"},"credentials":{"googlePalmApi":{"id":"1Rh1t7y5qqCTIsNj","name":"Google Gemini(PaLM) Api account [abc@mail.com"}},"typeVersion":1},{"id":"a1e4eaac-aaea-4859-bd29-375a6b50aaa1","name":"Privacy Build Final Newsletter HTML","type":"n8n-nodes-base.code","position":[-360,4280],"parameters":{"jsCode":"return items.map(item => {\n  // 1. grab the raw AI output\n  const raw = item.json.output;\n\n  // 2. extract what's between ```json ... ``` (or fall back to full text)\n  const match = raw.match(/```json\\s*([\\s\\S]*?)```/);\n  const jsonPayload = (match ? match[1] : raw).trim();\n\n  // 3. remove any trailing commas before } or ]\n  const clean = jsonPayload.replace(/,\\s*([\\]}])/g, '$1');\n\n  // 4. parse into an object, with error reporting\n  let data;\n  try {\n    data = JSON.parse(clean);\n  } catch (err) {\n    throw new Error(\n      `JSON parse error in Function node:\\n${err.message}\\n\\nPayload was:\\n${clean}`\n    );\n  }\n\n  // 5. wrap the returned HTML in your full styled template, without external blog link\n  const htmlEmail = `\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n  <meta charset=\"UTF-8\">\n  <title>${data.subject}</title>\n  <style>\n    body { font-family: Arial, sans-serif; line-height:1.5; color:#333; background-color:#f7f9fa; margin:0; padding:20px; }\n    .container { max-width:700px; margin:0 auto; background:#fff; border-radius:8px; box-shadow:0 2px 8px rgba(0,0,0,0.1); overflow:hidden; }\n    .header { background:#2c3e50; color:#fff; padding:20px; text-align:center; }\n    .header h1 { margin:0; font-size:24px; }\n    .content { padding:20px; }\n    h2 { color:#e74c3c; border-bottom:2px solid #e74c3c; padding-bottom:5px; }\n    ul { padding-left:20px; }\n    li { margin-bottom:10px; }\n    a { color:#2980b9; text-decoration:none; }\n    a:hover { text-decoration:underline; }\n    .footer { background:#ecf0f1; text-align:center; padding:10px; font-size:12px; color:#7f8c8d; }\n  </style>\n</head>\n<body>\n  <div class=\"container\">\n    <div class=\"header\">\n      <h1>${data.subject}</h1>\n    </div>\n    <div class=\"content\">\n      ${data.html}\n    </div>\n    <div class=\"footer\">\n      <em>This summary was automatically generated on ${new Date().toLocaleDateString('en-US', {\n        year: 'numeric', month: 'long', day: 'numeric'\n      })}.</em>\n    </div>\n  </div>\n</body>\n</html>\n  `.trim();\n\n  // 6. emit subject + styled html\n  return {\n    json: {\n      subject: data.subject,\n      html: htmlEmail,\n    }\n  };\n});"},"typeVersion":2},{"id":"7a864b0c-08f5-4e97-8430-1879f8e0e3d0","name":"Security Build Final Newsletter HTML","type":"n8n-nodes-base.code","position":[-340,3620],"parameters":{"jsCode":"return items.map(item => {\n  // 1. grab the raw AI output\n  const raw = item.json.output;\n\n  // 2. extract what's between ```json ... ``` (or fall back to full text)\n  const match = raw.match(/```json\\s*([\\s\\S]*?)```/);\n  const jsonPayload = (match ? match[1] : raw).trim();\n\n  // 3. remove any trailing commas before } or ]\n  const clean = jsonPayload.replace(/,\\s*([\\]}])/g, '$1');\n\n  // 4. parse into an object, with error reporting\n  let data;\n  try {\n    data = JSON.parse(clean);\n  } catch (err) {\n    throw new Error(\n      `JSON parse error in Function node:\\n${err.message}\\n\\nPayload was:\\n${clean}`\n    );\n  }\n\n  // 5. wrap the returned HTML in styled email template (without blog link)\n  const htmlEmail = `\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n  <meta charset=\"UTF-8\">\n  <title>${data.subject}</title>\n  <style>\n    body { font-family: Arial, sans-serif; line-height:1.5; color:#333; background-color:#f7f9fa; margin:0; padding:20px; }\n    .container { max-width:700px; margin:0 auto; background:#fff; border-radius:8px; box-shadow:0 2px 8px rgba(0,0,0,0.1); overflow:hidden; }\n    .header { background:#2c3e50; color:#fff; padding:20px; text-align:center; }\n    .header h1 { margin:0; font-size:24px; }\n    .content { padding:20px; }\n    h2 { color:#e74c3c; border-bottom:2px solid #e74c3c; padding-bottom:5px; }\n    ul { padding-left:20px; }\n    li { margin-bottom:10px; }\n    a { color:#2980b9; text-decoration:none; }\n    a:hover { text-decoration:underline; }\n    .footer { background:#ecf0f1; text-align:center; padding:10px; font-size:12px; color:#7f8c8d; }\n  </style>\n</head>\n<body>\n  <div class=\"container\">\n    <div class=\"header\">\n      <h1>${data.subject}</h1>\n    </div>\n    <div class=\"content\">\n      ${data.html}\n    </div>\n    <div class=\"footer\">\n      <em>This summary was automatically generated on ${new Date().toLocaleDateString('en-US', {\n        year: 'numeric', month: 'long', day: 'numeric'\n      })}.</em>\n    </div>\n  </div>\n</body>\n</html>\n  `.trim();\n\n  // 6. emit subject + styled html\n  return {\n    json: {\n      subject: data.subject,\n      html: htmlEmail,\n    }\n  };\n});"},"typeVersion":2},{"id":"deaaeb3a-5a14-4b2a-baf8-4443e7ed9740","name":"Compliance Build Final Newsletter HTML","type":"n8n-nodes-base.code","position":[-360,4840],"parameters":{"jsCode":"return items.map(item => {\n  // 1. grab the raw AI output\n  const raw = item.json.output;\n\n  // 2. extract what's between ```json ... ``` (or fall back to full text)\n  const match = raw.match(/```json\\s*([\\s\\S]*?)```/);\n  const jsonPayload = (match ? match[1] : raw).trim();\n\n  // 3. remove any trailing commas before } or ]\n  const clean = jsonPayload.replace(/,\\s*([\\]}])/g, '$1');\n\n  // 4. parse into an object, with error reporting\n  let data;\n  try {\n    data = JSON.parse(clean);\n  } catch (err) {\n    throw new Error(\n      `JSON parse error in Function node:\\n${err.message}\\n\\nPayload was:\\n${clean}`\n    );\n  }\n\n  // 5. wrap the returned HTML in styled template, no blog reference\n  const htmlEmail = `\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n  <meta charset=\"UTF-8\">\n  <title>${data.subject}</title>\n  <style>\n    body { font-family: Arial, sans-serif; line-height:1.5; color:#333; background-color:#f7f9fa; margin:0; padding:20px; }\n    .container { max-width:700px; margin:0 auto; background:#fff; border-radius:8px; box-shadow:0 2px 8px rgba(0,0,0,0.1); overflow:hidden; }\n    .header { background:#2c3e50; color:#fff; padding:20px; text-align:center; }\n    .header h1 { margin:0; font-size:24px; }\n    .content { padding:20px; }\n    h2 { color:#e74c3c; border-bottom:2px solid #e74c3c; padding-bottom:5px; }\n    ul { padding-left:20px; }\n    li { margin-bottom:10px; }\n    a { color:#2980b9; text-decoration:none; }\n    a:hover { text-decoration:underline; }\n    .footer { background:#ecf0f1; text-align:center; padding:10px; font-size:12px; color:#7f8c8d; }\n  </style>\n</head>\n<body>\n  <div class=\"container\">\n    <div class=\"header\">\n      <h1>${data.subject}</h1>\n    </div>\n    <div class=\"content\">\n      ${data.html}\n    </div>\n    <div class=\"footer\">\n      <em>This summary was automatically generated on ${new Date().toLocaleDateString('en-US', {\n        year: 'numeric', month: 'long', day: 'numeric'\n      })}.</em>\n    </div>\n  </div>\n</body>\n</html>\n  `.trim();\n\n  // 6. emit subject + styled html\n  return {\n    json: {\n      subject: data.subject,\n      html: htmlEmail,\n    }\n  };\n});"},"typeVersion":2},{"id":"fa9ac785-bc9d-43ba-8ca3-36abba10d506","name":"Security Send Final Digest Email","type":"n8n-nodes-base.gmail","position":[-120,3620],"webhookId":"2ffca73d-6ca2-4c61-88ec-3542600d2788","parameters":{"sendTo":"abc@mail.com","message":"={{ $json.html }}","options":{},"subject":"={{ $json.subject }}"},"credentials":{"gmailOAuth2":{"id":"Hd26wEkbjGRPpchT","name":"Gmail account [abc@mail.com]"}},"typeVersion":2.1},{"id":"59c16785-571c-4403-85cf-7f7bfc14a630","name":"Privacy Send Final Digest Email","type":"n8n-nodes-base.gmail","position":[-140,4280],"webhookId":"0e8133d5-e700-4bb9-8ef0-6c86f7ed4a59","parameters":{"sendTo":"abc@mail.com","message":"={{ $json.html }}","options":{},"subject":"={{ $json.subject }}"},"credentials":{"gmailOAuth2":{"id":"Hd26wEkbjGRPpchT","name":"Gmail account [abc@mail.com]"}},"typeVersion":2.1},{"id":"7526ae74-1bae-4eca-9c87-51c274565a8a","name":"Compliance Send Final Digest Email","type":"n8n-nodes-base.gmail","position":[-140,4840],"webhookId":"5d5a1988-01cd-4e67-9c02-9090aa851669","parameters":{"sendTo":"abc@mail.com","message":"={{ $json.html }}","options":{},"subject":"={{ $json.subject }}"},"credentials":{"gmailOAuth2":{"id":"Hd26wEkbjGRPpchT","name":"Gmail account [abc@mail.com]"}},"typeVersion":2.1},{"id":"6fa939b5-12ac-47a0-ac7f-ca9957367ce2","name":"Sort - Security Articles by Date","type":"n8n-nodes-base.sort","position":[-1160,3620],"parameters":{"options":{},"sortFieldsUi":{"sortField":[{"order":"descending","fieldName":"isoDate"}]}},"typeVersion":1},{"id":"7aa3a27b-72d7-4a03-83c8-361a6fbc555d","name":"Sort - Privacy Articles by Date","type":"n8n-nodes-base.sort","position":[-1180,4280],"parameters":{"options":{},"sortFieldsUi":{"sortField":[{"order":"descending","fieldName":"isoDate"}]}},"typeVersion":1},{"id":"fba86936-5208-4232-bda9-714886807b9c","name":"Sort - Compliance Articles by Date","type":"n8n-nodes-base.sort","position":[-1180,4840],"parameters":{"options":{},"sortFieldsUi":{"sortField":[{"order":"descending","fieldName":"isoDate"}]}},"typeVersion":1},{"id":"8d90a0ed-b9e1-440e-963a-e4c9f5285b32","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-2460,3380],"parameters":{"width":2600,"height":580,"content":"## 📬 Daily Security Newsletter"},"typeVersion":1},{"id":"946997e7-58bc-4ca0-89aa-32e39f327808","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-2460,4020],"parameters":{"color":4,"width":2600,"height":580,"content":"## 📬 Daily Privacy Newsletter"},"typeVersion":1},{"id":"61aca795-5b6a-4301-9cea-946e0358b8c5","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[-2460,4660],"parameters":{"color":6,"width":2600,"height":580,"content":"## 📬 Daily Compliance Newsletter"},"typeVersion":1},{"id":"ca6c3e50-8ea8-436c-ab77-94501667a431","name":"Split Out Security RSS","type":"n8n-nodes-base.splitOut","position":[-2040,3620],"parameters":{"options":{},"fieldToSplitOut":"rss_url"},"typeVersion":1},{"id":"5784491f-071c-4159-af29-6e21305f5e78","name":"Split Out Compliance RSS","type":"n8n-nodes-base.splitOut","position":[-2060,4840],"parameters":{"options":{},"fieldToSplitOut":"rss_url"},"typeVersion":1},{"id":"edbd5da4-f18c-4d08-8023-df4ef7ec11fa","name":"Security RSS Read","type":"n8n-nodes-base.rssFeedRead","position":[-1820,3620],"parameters":{"url":"={{ $json.rss_url }}","options":{}},"retryOnFail":true,"typeVersion":1.1},{"id":"76d973a7-832e-4049-80ea-c0d28b72b345","name":"Privacy RSS Read","type":"n8n-nodes-base.rssFeedRead","position":[-1840,4280],"parameters":{"url":"={{ $json.rss_url }}","options":{}},"retryOnFail":true,"typeVersion":1.1},{"id":"5503fe77-8ae3-4d41-97d3-6520cb60067e","name":"Compliance RSS Read","type":"n8n-nodes-base.rssFeedRead","position":[-1840,4840],"parameters":{"url":"={{ $json.rss_url }}","options":{}},"retryOnFail":true,"typeVersion":1.1},{"id":"c2538e17-1584-45a5-8a6e-1b220ed512a5","name":"Split Out Privacy RSS","type":"n8n-nodes-base.splitOut","position":[-2060,4280],"parameters":{"options":{},"fieldToSplitOut":"rss_url"},"typeVersion":1},{"id":"d1ae80d8-8094-4f84-aa9e-d4533a99a70d","name":"Fetch Security RSS","type":"n8n-nodes-base.code","position":[-2260,3620],"parameters":{"jsCode":"// This node returns curated cybersecurity RSS feeds\n// You can add, remove, or modify feeds as needed\n\nreturn [\n  {\n    json: {\n      name: \"Krebs on Security\",\n      website: \"https://krebsonsecurity.com\",\n      rss_url: \"https://krebsonsecurity.com/feed/\"\n    }\n  },\n  {\n    json: {\n      name: \"The Hacker News\",\n      website: \"https://thehackernews.com\",\n      rss_url: \"https://feeds.feedburner.com/TheHackersNews\"\n    }\n  },\n  {\n    json: {\n      name: \"Dark Reading\",\n      website: \"https://www.darkreading.com\",\n      rss_url: \"https://www.darkreading.com/rss.xml\"\n    }\n  },\n  {\n    json: {\n      name: \"SANS Internet Storm Center\",\n      website: \"https://isc.sans.edu\",\n      rss_url: \"https://isc.sans.edu/rssfeed_full.xml\"\n    }\n  },\n  {\n    json: {\n      name: \"Cisco Talos Intelligence Blog\",\n      website: \"https://blog.talosintelligence.com\",\n      rss_url: \"https://blog.talosintelligence.com/rss/\"\n    }\n  },\n  {\n    json: {\n      name: \"WeLiveSecurity (ESET)\",\n      website: \"https://www.welivesecurity.com\",\n      rss_url: \"https://feeds.feedburner.com/eset/blog\"\n    }\n  },\n  {\n    json: {\n      name: \"Graham Cluley Security Blog\",\n      website: \"https://grahamcluley.com\",\n      rss_url: \"https://grahamcluley.com/feed/\"\n    }\n  }\n];\n"},"typeVersion":2},{"id":"fbec32fd-2ca9-4f1f-84f9-e581bc6a37e5","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[-120,3480],"parameters":{"color":7,"height":100,"content":"### Update your email address or distribution list (DL) below\n⬇️"},"typeVersion":1},{"id":"4479495e-3e87-4a75-9fcc-36b230895854","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[-140,4160],"parameters":{"color":7,"height":100,"content":"### Update your email address or distribution list (DL) below\n⬇️"},"typeVersion":1},{"id":"18971219-b68d-4423-adbc-2f25174ae8a9","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[-140,4720],"parameters":{"color":7,"height":100,"content":"### Update your email address or distribution list (DL) below\n⬇️"},"typeVersion":1},{"id":"349ec0eb-9b5f-4bea-9b83-35d6cb63c1fa","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[-2320,3520],"parameters":{"color":7,"height":80,"content":"### Update the RSS feed URL as needed to fetch content from your preferred source."},"typeVersion":1},{"id":"4f3b0da6-4db7-41e9-aa0a-9bd084fce819","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[-2340,4180],"parameters":{"color":7,"height":80,"content":"### Update the RSS feed URL as needed to fetch content from your preferred source."},"typeVersion":1},{"id":"9c48eaa6-c602-44a3-871c-33ffd9dfa476","name":"Sticky Note8","type":"n8n-nodes-base.stickyNote","position":[-2340,4740],"parameters":{"color":7,"height":80,"content":"### Update the RSS feed URL as needed to fetch content from your preferred source."},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"0dc52173-7378-4951-8ebc-e44443c39540","connections":{"Privacy RSS Read":{"main":[[{"node":"Normalize Article Privacy Metadata","type":"main","index":0}]]},"Security RSS Read":{"main":[[{"node":"Normalize Article Security Metadata","type":"main","index":0}]]},"Fetch Security RSS":{"main":[[{"node":"Split Out Security RSS","type":"main","index":0}]]},"Compliance RSS Read":{"main":[[{"node":"Normalize Article Compliance Metadata","type":"main","index":0}]]},"Fetch Privacy Feeds":{"main":[[{"node":"Split Out Privacy RSS","type":"main","index":0}]]},"Trigger Daily Digest":{"main":[[{"node":"Fetch Security RSS","type":"main","index":0},{"node":"Fetch Compliance Feeds","type":"main","index":0},{"node":"Fetch Privacy Feeds","type":"main","index":0}]]},"Split Out Privacy RSS":{"main":[[{"node":"Privacy RSS Read","type":"main","index":0}]]},"Fetch Compliance Feeds":{"main":[[{"node":"Split Out Compliance RSS","type":"main","index":0}]]},"Split Out Security RSS":{"main":[[{"node":"Security RSS Read","type":"main","index":0}]]},"Split Out Compliance RSS":{"main":[[{"node":"Compliance RSS Read","type":"main","index":0}]]},"AI Agent - Privacy Intelligence":{"main":[[{"node":"Privacy Build Final Newsletter HTML","type":"main","index":0}]]},"LLM - Gemini Privacy Summarizer":{"ai_languageModel":[[{"node":"AI Agent - Privacy Intelligence","type":"ai_languageModel","index":0}]]},"Sort - Privacy Articles by Date":{"main":[[{"node":"Format Privacy Articles into HTML","type":"main","index":0}]]},"AI Agent - Security Intelligence":{"main":[[{"node":"Security Build Final Newsletter HTML","type":"main","index":0}]]},"LLM - Gemini Security Summarizer":{"ai_languageModel":[[{"node":"AI Agent - Security Intelligence","type":"ai_languageModel","index":0}]]},"Sort - Security Articles by Date":{"main":[[{"node":"Format Security Articles into HTML","type":"main","index":0}]]},"Format Privacy Articles into HTML":{"main":[[{"node":"AI Agent - Privacy Intelligence","type":"main","index":0}]]},"AI Agent - Compliance Intelligence":{"main":[[{"node":"Compliance Build Final Newsletter HTML","type":"main","index":0}]]},"Format Security Articles into HTML":{"main":[[{"node":"AI Agent - Security Intelligence","type":"main","index":0}]]},"LLM - Gemini Compliance Summarizer":{"ai_languageModel":[[{"node":"AI Agent - Compliance Intelligence","type":"ai_languageModel","index":0}]]},"Normalize Article Privacy Metadata":{"main":[[{"node":"Filter Recent Privacy Articles (24h)","type":"main","index":0}]]},"Sort - Compliance Articles by Date":{"main":[[{"node":"Format Compliance Articles into HTML","type":"main","index":0}]]},"Normalize Article Security Metadata":{"main":[[{"node":"Filter Recent Security Articles (24h)","type":"main","index":0}]]},"Privacy Build Final Newsletter HTML":{"main":[[{"node":"Privacy Send Final Digest Email","type":"main","index":0}]]},"Filter Recent Privacy Articles (24h)":{"main":[[{"node":"Sort - Privacy Articles by Date","type":"main","index":0}]]},"Format Compliance Articles into HTML":{"main":[[{"node":"AI Agent - Compliance Intelligence","type":"main","index":0}]]},"Security Build Final Newsletter HTML":{"main":[[{"node":"Security Send Final Digest Email","type":"main","index":0}]]},"Filter Recent Security Articles (24h)":{"main":[[{"node":"Sort - Security Articles by Date","type":"main","index":0}]]},"Normalize Article Compliance Metadata":{"main":[[{"node":"Filter Recent Compliance Articles (24h)","type":"main","index":0}]]},"Compliance Build Final Newsletter HTML":{"main":[[{"node":"Compliance Send Final Digest Email","type":"main","index":0}]]},"Filter Recent Compliance Articles (24h)":{"main":[[{"node":"Sort - Compliance Articles by Date","type":"main","index":0}]]}}}


name: Daily Auto-Archive for Gmail Messages
author_name: Niranjan G
author_username: niranjan
nodes_used: ["n8n-nodes-base.gmail"]
categories: []
created_at: 2025-06-05T06:28:09.689Z
updated_at: 2025-06-05T06:28:09.689Z
url: https://n8n.io/workflows/4679
scraped_at: 2025-06-19 22:07:56
description:
  Who is this for?

  Professionals, solopreneurs, or productivity enthusiasts who want to keep their Gmail inbox clean and organized without manual effort.

  What problem does this solve?

  Manually archiving emails clutters your time and slows you down. This workflow automates inbox cleanup by removing the "INBOX" label from messages received over 24 hours ago. A perfect companion to AI-based labeling workflows, this keeps your inbox light and relevant.

  What this workflow does

  Triggers every day at 4 AM
  Fetches Gmail messages from the INBOX that are older than 24 hours
  Processes them one by one using the Split Out node
  Removes the INBOX label, effectively archiving the messages

  Setup

  Connect your Gmail account using OAuth2 credentials.
  Customize the Schedule Trigger node to adjust the run time.
  Modify the Gmail filter if you want to archive unread or labeled emails instead.

  How to customize this workflow to your needs

  Schedule different frequencies (e.g. twice a day or weekly).

  🔄 Pairs Well With

  This complements the Intelligent Email Organization with AI-Powered Content Classification workflow.

  Use that to label emails smartly using AI, and this one to auto-archive them for a clean, clutter-free inbox.

workflow_json:
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name: AI-Powered Meta Ads Analysis & Creation with Gemini, GPT-4.1 Mini & Ads Manager
author_name: Daniel Setzermann
author_username: danielsetzermann
categories: []
created_at: 2025-06-05T09:09:39.117Z
updated_at: 2025-06-05T09:09:39.117Z
url: https://n8n.io/workflows/4684
scraped_at: 2025-06-19 22:07:56
description:
  AI-Powered Meta Ads Creation & Analysis Workflow

  Overview

  This comprehensive n8n workflow automates the entire Meta (Facebook/Instagram) advertising process, from asset analysis to ad creation. It combines AI-powered content analysis with automated ad deployment, streamlining the creation of high-converting social media advertisements.

  Key Features

  🤖 AI-Powered Asset Analysis
  Video Analysis**: Uses Google Gemini to analyze video content, extracting transcripts, scene descriptions, hooks, offers, and branding elements
  Image Analysis**: Employs GPT-4.1 Mini to analyze static images, identifying visual elements, USPs, and marketing potential
  Structured Output**: Generates detailed descriptions and creative insights for informed ad creation

  ✍️ Automated Ad Copy Generation
  Creates 3 variations each of primary text, headlines, and link descriptions
  Optimized for Meta's character limits and best practices
  Maintains brand voice and messaging consistency
  Leverages AI analysis to create compelling, conversion-focused copy

  🎯 Meta Ads Manager Integration
  Video Ads**: Uploads videos, creates ad creatives with multiple text variations
  Image Ads**: Supports both single and multi-image campaigns (1:1 and 9:16 formats)
  Asset Feed Optimization**: Implements placement-specific customization rules
  Automated Preview Generation**: Creates ad previews for different placements

  📊 Smart Workflow Management
  Google Drive Integration**: Monitors designated folders for new creative assets
  Google Sheets Tracking**: Maintains comprehensive records of all assets and campaigns
  Status Management**: Tracks processing stages and prevents duplicate work
  Error Handling**: Includes retry logic and status checking for reliable operation

  Workflow Components

  Asset Discovery & Processing
  Google Drive Trigger: Monitors specified folder for new image/video files
  File Analysis: Extracts metadata, dimensions, and file specifications
  Asset Registration: Logs all assets in Google Sheets for tracking

  AI Analysis Pipeline
  Content Type Detection: Automatically identifies videos vs. images
  Video Processing:
     Uploads to Google Gemini for analysis
     Generates comprehensive content breakdown
     Extracts all marketing-relevant elements
  Image Processing:
     Analyzes visual content with GPT-4.1 Mini
     Identifies key messaging and visual elements
     Creates detailed creative descriptions

  Ad Copy Creation
  AI Agent Processing: Transforms analysis into marketing copy
  Multi-Variant Generation: Creates 3 versions of each text element
  Platform Optimization: Ensures compliance with Meta's requirements
  Quality Assurance: Structured output validation

  Meta Ads Deployment
  Asset Upload: Pushes images/videos to Meta Ads Manager
  Creative Assembly: Builds ad creatives with generated copy variations
  Campaign Creation: Sets up ads with proper targeting and placement rules
  Status Tracking: Updates spreadsheet with campaign IDs and status

  Setup Requirements

  Required Credentials
  Meta Developer Account**: App access token for Meta Graph API
  Google Drive OAuth**: For file monitoring and asset access
  Google Sheets OAuth**: For workflow tracking and management
  Google Gemini API**: For video analysis capabilities
  OpenAI API**: For image analysis and copy generation

  Configuration Steps
  Google Drive Setup: 
     Create dedicated folder for creative assets
     Configure folder monitoring in the trigger node
  Google Sheets Template:
     Use provided template for asset and campaign tracking
     Configure account settings and campaign parameters
  Meta Developer Setup:
     Create Meta App and obtain access tokens
     Set up ad account permissions
  API Credentials:
     Configure all required authentication credentials
     Test connections before activation

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.facebookGraphApi","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured"]

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name: AI Email Organizer for GMail - Advanced Email Management & Sorting
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2025-06-05T11:49:49.978Z
updated_at: 2025-06-05T11:49:49.978Z
url: https://n8n.io/workflows/4687
scraped_at: 2025-06-19 22:07:56
description:
  What the workflow does
  Organizes your emails into categories, based on your category definitions in a Google Sheets

  How to set up
  Find the detailed setup instructions on the note on the sticky notes within the workflow!

  To get a similar business tool tailored to your processes, book a call with us: Book a Call, or write to me at: hello@smoothwork.ai

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenRouter"]

workflow_json:
  {"meta":{"instanceId":"c8c9ba5d3eb26ae47a06d24ff164a01c0d73ed4753a74903c2bb609d434a0f6b","templateCredsSetupCompleted":true},"nodes":[{"id":"fd08b5e1-f370-46b6-8088-c2e59c73d71c","name":"Schedule Trigger","type":"n8n-nodes-base.scheduleTrigger","position":[0,0],"parameters":{"rule":{"interval":[{"field":"hours"}]}},"typeVersion":1.2},{"id":"78f01440-ef12-4ee6-a9a5-af4d39104c0c","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-520,-140],"parameters":{"width":420,"content":"## 1 - Config\n\n1) [G Sheets to Duplicate](https://docs.google.com/spreadsheets/d/1LKIx1Z3dCSX1uzyZH9s2HE0QRMvLTDI6sJApFU5LTj0/edit?gid=0#gid=0)\n2) Copy the URL of your Sheets in *Config* node ➡️➡️"},"typeVersion":1},{"id":"58417d60-eaaf-4d3f-affb-3a1655d2777b","name":"Config","type":"n8n-nodes-base.set","position":[220,0],"parameters":{"options":{},"assignments":{"assignments":[{"id":"cf1d4687-98df-4f65-b72e-6722075ab703","name":"sheets_url","type":"string","value":"https://docs.google.com/spreadsheets/d/1LKIx1Z3dCSX1uzyZH9s2HE0QRMvLTDI6sJApFU5LTj0/edit?gid=0#gid=0"},{"id":"5df50301-a77a-483f-80ce-7717daa04231","name":"extra_filter","type":"string","value":""},{"id":"4fc76947-79c9-4f73-bf36-c18e664b14b4","name":"limit","type":"number","value":2}]}},"typeVersion":3.4},{"id":"8f8812bc-dc04-4405-8d33-e6d457e2d1e8","name":"AI Agent","type":"@n8n/n8n-nodes-langchain.agent","position":[880,0],"parameters":{"text":"=Email subject:\n\n{{ $json.headers.subject }}\n\n\nText: {{ $json.text }}\n\nChoose the best fitting of the category names below, based on their definition:\n{{ JSON.stringify($('Get Labels').all()) }}\n\nImportant: ONLY respond with the category Name. EXACTLY one of the names, do not add any other texts.\n\nSo response should be one of:\n{{ $('Get Labels').all().map(({ json: { Name } }) => Name ).join('\\n') }}","options":{},"promptType":"define"},"typeVersion":1.8},{"id":"e1bf4a15-a2b5-48a4-83d4-af649f20101f","name":"OpenRouter Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenRouter","position":[900,220],"parameters":{"model":"deepseek/deepseek-r1:free","options":{}},"credentials":{"openRouterApi":{"id":"iZmDjXmiYrP8nGux","name":"OpenRouter account"}},"typeVersion":1},{"id":"5c597cd5-69f0-42ac-a158-a5ef766157b0","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-520,20],"parameters":{"width":420,"height":400,"content":"## 2 - Connect AI (Optional: find free model)\n\n1) Get OpenRouter API Key: https://openrouter.ai/settings/keys\n\n2) [List of currently free models](https://openrouter.ai/models?max_price=0&order=top-weekly)\n[List of Popular models](https://openrouter.ai/models?order=top-weekly)\n\nBest in 2025 May: deepseek/deepseek-chat-v3-0324:free\nPaid recommended: gpt-4.1-mini\n\n3) Go to **OpenRouter Chat Model** Node\n+ \"Add Credential\" - Enter API Key\n+ Choose your preferred AI model"},"typeVersion":1},{"id":"8ec93dff-52a4-410e-b45f-f492bcc0bb88","name":"Get Labels","type":"n8n-nodes-base.googleSheets","position":[440,0],"parameters":{"options":{},"sheetName":{"__rl":true,"mode":"url","value":"={{ $json.sheets_url }}"},"documentId":{"__rl":true,"mode":"url","value":"={{ $json.sheets_url }}"}},"credentials":{"googleSheetsOAuth2Api":{"id":"gbEbCNKqCMSRZPql","name":"Google Sheets account 3"}},"typeVersion":4.5},{"id":"221de31a-e662-402d-8795-1bcd40757855","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[-520,-220],"parameters":{"height":80,"content":"# Setup"},"typeVersion":1},{"id":"e41fb3ca-5de0-4a1a-a0f7-4f6fddc739ed","name":"Create Label if Doesn't exist","type":"n8n-nodes-base.gmail","onError":"continueRegularOutput","position":[1620,280],"webhookId":"9482c1cf-26bd-42a6-98cd-78c34810597d","parameters":{"name":"={{ $json.output }}","options":{},"resource":"label","operation":"create"},"credentials":{"gmailOAuth2":{"id":"duhSaIumY2CifCex","name":"Gmail account"}},"typeVersion":2.1},{"id":"fea8a543-0bb4-460e-9862-06c711ec30e6","name":"Get Existing Labels","type":"n8n-nodes-base.gmail","position":[1820,280],"webhookId":"bb39b430-d756-4a9f-8bfb-9d1a63c495c4","parameters":{"resource":"label","returnAll":true},"credentials":{"gmailOAuth2":{"id":"duhSaIumY2CifCex","name":"Gmail account"}},"typeVersion":2.1},{"id":"a45806ca-6b98-411a-a8b2-a8b9f071d6c0","name":"Loop Over Items","type":"n8n-nodes-base.splitInBatches","position":[1460,0],"parameters":{"options":{}},"typeVersion":3},{"id":"de31842d-88de-4281-a8f7-5e88db026ece","name":"Filter","type":"n8n-nodes-base.filter","position":[2020,280],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"e36a5069-09dd-424d-a2b8-096cefa059d7","operator":{"name":"filter.operator.equals","type":"string","operation":"equals"},"leftValue":"={{ $json.name }}","rightValue":"={{ $('Loop Over Items').item.json.output }}"}]}},"typeVersion":2.2},{"id":"fe0c9d60-bd76-4a72-adb4-0b04bcd971bc","name":"Gmail1","type":"n8n-nodes-base.gmail","position":[2200,280],"webhookId":"332f57fb-6795-4a88-aff7-25d3838ffea7","parameters":{"labelIds":"={{ $json.id }}","resource":"thread","threadId":"={{ $('Loop Over Items').item.json.threadId }}","operation":"addLabels"},"credentials":{"gmailOAuth2":{"id":"duhSaIumY2CifCex","name":"Gmail account"}},"typeVersion":2.1},{"id":"3f3c2627-c20d-443f-8a6f-c803139c4143","name":"Fields For Loop","type":"n8n-nodes-base.set","position":[1240,0],"parameters":{"options":{},"assignments":{"assignments":[{"id":"cf0978da-37a2-48e8-8da9-8ec110638a42","name":"threadId","type":"string","value":"={{ $('Get Messages').item.json.threadId }}"},{"id":"71cac306-cca3-4e28-8bba-d920d83037ac","name":"output","type":"string","value":"={{ $json.output }}"}]}},"typeVersion":3.4},{"id":"10ede08e-bdbc-4c3f-8c25-2724bfbda6f5","name":"Get Messages","type":"n8n-nodes-base.gmail","position":[660,0],"webhookId":"5d1e7386-2e1c-4743-b03a-daffea28b66b","parameters":{"limit":"={{ $('Config').item.json.limit }}","simple":false,"filters":{"q":"=has:nouserlabels {{ $('Config').item.json.extra_filter }}"},"options":{},"operation":"getAll"},"credentials":{"gmailOAuth2":{"id":"duhSaIumY2CifCex","name":"Gmail account"}},"executeOnce":true,"typeVersion":2.1},{"id":"f15bda54-5a37-49e1-b06c-322308689c6e","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[220,160],"parameters":{"width":310,"content":"👆⬆️\n1 - sheets_url - Where the category definitions live\n2 - extra_filter - Leave empty to process all emails. Use any gmail search filters.\n3 - limit - max no. of emails to process in a run"},"typeVersion":1},{"id":"d34f5149-6749-4064-bfcf-6fc32651e945","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[-280,-540],"parameters":{"color":5,"width":580,"height":400,"content":"# About the Author\n![Milan](https://gravatar.com/avatar/95700d17ba300a9f14c1b8cacf933df7720027b3adda9cbe6183d89142925422?r=pg&d=retro&size=100)\n## Milan - SmoothWork.ai\n\nWe help businesses eliminate busywork by building compact business tools tailored to your process.\n\n▶️ [Check us on YouTube](https://www.youtube.com/@vasarmilan)\n📞 [Book a Free Consulting Call](https://smoothwork.ai/book-a-call/)\n"},"typeVersion":1}],"pinData":{},"connections":{"Config":{"main":[[{"node":"Get Labels","type":"main","index":0}]]},"Filter":{"main":[[{"node":"Gmail1","type":"main","index":0}]]},"Gmail1":{"main":[[{"node":"Loop Over Items","type":"main","index":0}]]},"AI Agent":{"main":[[{"node":"Fields For Loop","type":"main","index":0}]]},"Get Labels":{"main":[[{"node":"Get Messages","type":"main","index":0}]]},"Get Messages":{"main":[[{"node":"AI Agent","type":"main","index":0}]]},"Fields For Loop":{"main":[[{"node":"Loop Over Items","type":"main","index":0}]]},"Loop Over Items":{"main":[[],[{"node":"Create Label if Doesn't exist","type":"main","index":0}]]},"Schedule Trigger":{"main":[[{"node":"Config","type":"main","index":0}]]},"Get Existing Labels":{"main":[[{"node":"Filter","type":"main","index":0}]]},"OpenRouter Chat Model":{"ai_languageModel":[[{"node":"AI Agent","type":"ai_languageModel","index":0}]]},"Create Label if Doesn't exist":{"main":[[{"node":"Get Existing Labels","type":"main","index":0}]]}}}


name: Build a Chatbot with Reinforced Learning Human Feedback (RLHF) and RAG
author_name: NovaNode
author_username: thomasgpt
categories: []
created_at: 2025-06-05T13:39:18.776Z
updated_at: 2025-06-05T13:39:18.776Z
url: https://n8n.io/workflows/4689
scraped_at: 2025-06-19 22:07:56
description:
  Who is this for?
  This template is designed for internal support teams, product specialists, and knowledge managers who want to build an AI-powered knowledge assistant with retrieval-augmented generation (RAG) and reinforcement learning from human feedback (RLHF) via Telegram.

  What problem is this workflow solving?
  Manual knowledge management and answering support queries can be time-consuming and error-prone. This solution automates importing and indexing official documentation into MongoDB vector search and enhances AI responses with Telegram-based user feedback to continuously improve answer quality.

  What these workflows do
  Workflow 1: Document ingestion & indexing
  Manually triggered workflow imports product documentation from Google Docs.
  Documents are split into manageable chunks and embedded using OpenAI embeddings.
  Embedded document chunks are stored in MongoDB Atlas vector store to enable semantic search.

  Workflow 2: Telegram chat with RLHF feedback loop
  Listens for user messages via Telegram bot integration.
  Uses vector similarity search on MongoDB to retrieve relevant documentation chunks.
  Generates answers with OpenAI GPT-4o-mini model using retrieval-augmented generation.
  Sends answers back via Telegram and waits for user feedback (approval or disapproval).
  Captures feedback, maps it as positive or negative, and stores it with the conversation data for future model improvement.

  Setup
  Setting up vector embeddings
  Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index.
  Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. 
  Ensure the index name matches the one configured in n8n (data_index). 
  See the example MongoDB search index template below for reference.

  Setting up chat with Telegram RLHF
  Create a bot in Telegram with @botFather using the /newbot command.
  Connect the MongoDB database and search index used for vector search in the previous workflow. Also create two new collections in MongoDB Atlas: one for feedback and one for chat history. Create a search index for feedback, copying the provided template.
  Configure the AI system prompt in the “Knowledge Base Agent” node, making sure it references all three tools connected (productDocs, feedbackPositive, feedbackNegative) as provided in the template prompt.
  Make sure
  Product documentation and feedback collections must connect to the same MongoDB database.
  There are three distinct MongoDB collections: one for product documentation, one for feedback, and one for chat history (chat history collection can be separate).
  Telegram API credentials are valid and webhook URLs are correctly set up.

  MongoDB Search Index Templates
  Documentation Collection Index

  {
    "mappings": {
      "dynamic": false,
      "fields": {
        "_id": {
          "type": "string"
        },
        "text": {
          "type": "string"
        },
        "embedding": {
          "type": "knnVector",
          "dimensions": 1536,
          "similarity": "cosine"
        },
        "source": {
          "type": "string"
        },
        "doc_id": {
          "type": "string"
        }
      }
    }
  }

  Feedback Collection Index

  {
    "mappings": {
      "dynamic": false,
      "fields": {
        "prompt": {
          "type": "string"
        },
        "response": {
          "type": "string"
        },
        "text": {
          "type": "string"
        },
        "embedding": {
          "type": "knnVector",
          "dimensions": 1536,
          "similarity": "cosine"
        },
        "feedback": {
          "type": "token"
        }
      }
    }
  }

nodes_used:
  ["n8n-nodes-base.telegram","n8n-nodes-base.googleDocs","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.memoryMongoDbChat","@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas"]

workflow_json:
  {"meta":{"instanceId":"074f90e2bb5206c5f405a8aac6551497c72005283a5405fb08207b1b3a78c2b8","templateCredsSetupCompleted":true},"nodes":[{"id":"e39a0b25-53ce-4ac0-9c93-7750b8e6105b","name":"Knowledge Base Agent","type":"@n8n/n8n-nodes-langchain.agent","position":[220,0],"parameters":{"text":"={{ $json.message.text }}","options":{"systemMessage":"You are the AI assistant for a company specializing in advanced technology solutions. Your task is to help internal users by consulting the official product documentation for the company’s platforms.\n\nAvailable References:\n\nproductDocs: Step-by-step guides, technical configurations, and official manuals from the developer documentation.\n\nfeedbackPositive: Previous responses rated positively by users, useful as examples for generating high-quality answers.\n\nfeedbackNegative: Previous responses rated negatively by users. Use this tool to avoid making similar mistakes in your replies.\n\nCurrently, there are no use cases or update history documented in the available materials.\n\nBehavior Rules\n\nSearch & Response:\n\nAlways consult the official documentation first using the productDocs tool.\n\nAlso search feedbackPositive for highly rated previous responses to similar questions.\n\nCheck feedbackNegative to see if similar answers were poorly rated, and avoid repeating those mistakes.\n\nIf multiple tools return relevant results, combine the information to provide the best answer. If only one tool returns relevant content, use that as your source.\n\nRespond directly and clearly, explaining exactly how to perform the requested task.\n\nDo not filter by category unless the user explicitly asks.\n\nLanguage (Strict Per-Message Rule):\n\nDetect the language of each incoming message individually.\n\nRespond in the same language as the most recent message.\n\nDo not use the language of previous messages or conversation history to decide the language.\n\nLinks (Not Allowed):\n\nNever provide links, even if the user requests them.\n\nIf a user asks for a link, respond with:\n“I cannot provide links. If you need specific information, let me know and I will help with the details.”\n\nResponse Style:\n\nUse a professional, direct, and human tone.\n\nKeep responses between 2 and 4 lines unless the user asks for more detail.\n\nDo not make up information that isn’t in MongoDB.\n\nIf your answer contains numbered steps or lists, always number them sequentially (1, 2, 3...) without skipping or repeating numbers, even if the original content uses a different numbering style."},"promptType":"define"},"typeVersion":1.9},{"id":"775b7991-88f3-4959-a068-3fb2b667567e","name":"OpenAI Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[200,180],"parameters":{"model":{"__rl":true,"mode":"list","value":"gpt-4o-mini"},"options":{}},"credentials":{"openAiApi":{"id":"cJRah9hGPQ7eX4jd","name":"OpenAi account"}},"typeVersion":1.2},{"id":"4c2e0ea6-f5d0-4d5b-a065-606b2dc2cff5","name":"Embeddings 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Note1","type":"n8n-nodes-base.stickyNote","position":[-420,40],"parameters":{"color":4,"height":180,"content":"This workflow uses retrieval-augmented generation (RAG) to answer user questions by searching the MongoDB vector store and generating AI responses enhanced with Telegram-based RLHF feedback."},"typeVersion":1},{"id":"caf481b3-7a19-40df-b5fb-52e74a8edd73","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[1800,-480],"parameters":{"width":520,"height":1040,"content":"Documentation Search Index\n\n{\n  \"mappings\": {\n    \"dynamic\": false,\n    \"fields\": {\n      \"_id\": {\n        \"type\": \"string\"\n      },\n      \"text\": {\n        \"type\": \"string\"\n      },\n      \"embedding\": {\n        \"type\": \"knnVector\",\n        \"dimensions\": 1536,\n        \"similarity\": \"cosine\"\n      },\n      \"source\": {\n        \"type\": \"string\"\n      },\n      \"doc_id\": {\n        \"type\": \"string\"\n      }\n    }\n  }\n}\n\n\nFeedback Search Index\n\n{\n  \"mappings\": {\n    \"dynamic\": false,\n    \"fields\": {\n      \"prompt\": {\n        \"type\": \"string\"\n      },\n      \"response\": {\n        \"type\": \"string\"\n      },\n      \"text\": {\n        \"type\": \"string\"\n      },\n      \"embedding\": {\n        \"type\": \"knnVector\",\n        \"dimensions\": 1536,\n        \"similarity\": \"cosine\"\n      },\n      \"feedback\": {\n        \"type\": \"token\"\n      }\n    }\n  }\n}\n"},"typeVersion":1},{"id":"8a3abf9e-6f3f-47cf-b9ec-bb9da9835e5c","name":"When clicking \"Execute Workflow\"","type":"n8n-nodes-base.manualTrigger","position":[0,-520],"parameters":{},"typeVersion":1},{"id":"4235ba8c-d671-40b8-b3a4-d7cccac36d54","name":"MongoDB Chat Memory","type":"@n8n/n8n-nodes-langchain.memoryMongoDbChat","position":[320,180],"parameters":{"sessionKey":"={{ $('Receive Message on Telegram').item.json.message.chat.id }}","sessionIdType":"customKey","collectionName":"n8n-template-chat-history"},"credentials":{"mongoDb":{"id":"7riubYENUDZsmjyK","name":"MongoDB account 2"}},"typeVersion":1},{"id":"000e272a-3f19-4c17-86b5-07505eba2c3b","name":"Embeddings OpenAI1","type":"@n8n/n8n-nodes-langchain.embeddingsOpenAi","position":[160,440],"parameters":{"options":{}},"credentials":{"openAiApi":{"id":"cJRah9hGPQ7eX4jd","name":"OpenAi account"}},"typeVersion":1.2},{"id":"dca02c04-2921-4982-83a3-6b2f6fb32663","name":"Default Data Loader","type":"@n8n/n8n-nodes-langchain.documentDefaultDataLoader","position":[1400,180],"parameters":{"options":{"metadata":{"metadataValues":[{"name":"prompt","value":"={{ $json.prompt }}"},{"name":"response","value":"={{ $json.response }}"},{"name":"feedback","value":"={{ $json.feedback }}"}]}},"jsonData":"={{ $json.text }}","jsonMode":"expressionData"},"typeVersion":1},{"id":"757073e3-f8b5-42b5-ab02-d310d5f6e8a4","name":"Recursive Character Text 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Use this tool before responding to improve your answer based on those approved examples.","vectorIndexName":"template-feedback-search"},"credentials":{"mongoDb":{"id":"7riubYENUDZsmjyK","name":"MongoDB account 2"}},"typeVersion":1.1},{"id":"84a95dfb-6cc4-4a18-be40-6fa201073750","name":"Search Documentation","type":"@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas","position":[440,320],"parameters":{"mode":"retrieve-as-tool","options":{},"toolName":"productDocs","mongoCollection":{"__rl":true,"mode":"list","value":"n8n-template","cachedResultName":"n8n-template"},"toolDescription":"retreive documentation","vectorIndexName":"data_index"},"credentials":{"mongoDb":{"id":"7riubYENUDZsmjyK","name":"MongoDB account 2"}},"typeVersion":1.1},{"id":"f2001c81-081b-4417-8349-3db459c0f191","name":"Search Negative Interactions","type":"@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas","position":[240,580],"parameters":{"mode":"retrieve-as-tool","options":{"metadata":{"metadataValues":[{"name":"feedback","value":"negative"}]}},"toolName":"feedbackNegative","mongoCollection":{"__rl":true,"mode":"list","value":"n8n-template-feedback","cachedResultName":"n8n-template-feedback"},"toolDescription":"Retrieve previous examples of similar questions that were rated negatively by users. 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name: Get Real-Time Security Insights with NixGuard RAG and Wazuh Integration
author_name: Jonathan | NEX
author_username: nex
nodes_used: ["n8n-nodes-base.httpRequest","n8n-nodes-base.code"]
categories: []
created_at: 2025-06-05T14:45:24.254Z
updated_at: 2025-06-05T14:45:24.254Z
url: https://n8n.io/workflows/4693
scraped_at: 2025-06-19 22:07:56
description:
  Effortlessly integrate NixGuard API into your n8n workflows for real-time security insights using your API key. This connector enables seamless interaction with Nix, providing rapid Retrieval-Augmented Generation (RAG) event knowledge with Wazuh integration - completely free and set up in under 5 minutes!

  🚀 Features:
  ✅ Query NixGuard's AI-driven security insights via API authentication
  ✅ Real-time security event knowledge integration
  ✅ Plug-and-play workflow trigger for effortless automation
  ✅ Wazuh compatibility for full security visibility

  🛠 How to Use:
  1️⃣ Add your API Key to authenticate with NixGuard.
  2️⃣ Integrate with your existing n8n workflows using the workflow trigger (default enabled).
  3️⃣ (Optional) Activate the chat trigger to streamline security queries via chat-based inputs.
  4️⃣ Run the workflow and get instant security intelligence!

  📢 Perfect for: Startup CTO's, SOC teams, security engineers, and developers needing real-time security automation within their infrastructure.

  🔗 Learn more about NixGuard: thenex.world  
  🔗 Get started with a free security subscription: thenex.world/security/subscribe  

workflow_json:
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Configure your NixGuard API key in 'Prepare API Request Data' node\n2. Set up trigger method (chat or manual)\n3. Test with sample security queries\n4. Make sure NixGuard agents are installed on your network endpoints for real-time security events\n\n### Support:\nFor questions, visit [NixGuard Documentation](https://nixguard.thenex.world) or join our [Community Discord](https://discord.com/invite/ajCYwYCwHb)"},"typeVersion":1},{"id":"a94e8519-9783-4b2f-8ca9-e3e571784e40","name":"Workflow Overview1","type":"n8n-nodes-base.stickyNote","position":[-960,-80],"parameters":{"color":7,"width":500,"height":380,"content":"## NixGuard Security Connector Workflow\n\nThis workflow integrates NixGuard's RAG (Retrieval-Augmented Generation) with Wazuh security data to provide real-time security insights.\n\n### Key Features:\n- Processes security queries through NixGuard's AI\n- Combines multiple security data sources\n- Returns actionable security insights\n\n### Authentication:\nRequires valid API key for NixGuard endpoint (configured in 'Prepare API Request Data' node)."},"typeVersion":1},{"id":"69fd8854-8025-462f-b078-73b86803a8c7","name":"API Request Explanation1","type":"n8n-nodes-base.stickyNote","position":[-140,500],"parameters":{"color":7,"width":400,"height":380,"content":"## API Request Configuration\n\nThis node sends the security query to NixGuard's API endpoint with:\n- Proper authentication headers\n- Formatted request body\n- Required content type\n\n### Configuration:\n1. Set your NixGuard API endpoint URL\n2. Ensure headers include `Content-Type: application/json`\n3. Body contains:\n   - `apiKey`: Your NixGuard API key\n   - `prompt`: The security query/input"},"typeVersion":1},{"id":"b4d557c8-6db5-42d6-bf6a-6edbe4c84dec","name":"Response Processing1","type":"n8n-nodes-base.stickyNote","position":[800,500],"parameters":{"color":7,"width":400,"height":320,"content":"## Response Processing\n\nThis section handles the NixGuard API response:\n1. **Parse NixGuard Response**: Converts JSON string to object\n2. **Format API Response**: Extracts relevant content\n3. **Prepare Final Output**: Structures output for end users\n\n### Error Handling:\n- Invalid responses will trigger error paths\n- Ensure proper error handling in subsequent workflows"},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"130b7c1c-428f-45c4-934c-14653965820d","connections":{"Merge Input Data":{"main":[[{"node":"Aggregate Security Data","type":"main","index":0}]]},"Format API Response":{"main":[[{"node":"Prepare Final Output","type":"main","index":0}]]},"Combine Security Data":{"main":[[{"node":"Send Request to NixGuard API","type":"main","index":0}]]},"Aggregate Security Data":{"main":[[{"node":"Combine Security Data","type":"main","index":0}]]},"Parse NixGuard Response":{"main":[[{"node":"Format API Response","type":"main","index":0}]]},"Execute Workflow Trigger":{"main":[[{"node":"Merge Input Data","type":"main","index":0}]]},"Prepare API Request Data":{"main":[[{"node":"Merge Input Data","type":"main","index":0}]]},"When chat message received":{"main":[[{"node":"Prepare API Request Data","type":"main","index":0}]]},"Send Request to NixGuard API":{"main":[[{"node":"Parse NixGuard Response","type":"main","index":0}]]}}}


name: Daily News Digest: Summarize RSS Feeds with OpenAI and Deliver to WhatsApp
author_name: Danielle Gomes
author_username: daniellegomes
nodes_used: ["n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi"]
categories: []
created_at: 2025-06-05T22:10:34.527Z
updated_at: 2025-06-05T22:10:34.527Z
url: https://n8n.io/workflows/4709
scraped_at: 2025-06-19 22:07:56
description:
  This n8n workflow collects and summarizes news from multiple RSS feeds, using OpenAI to generate a concise summary that can be sent to WhatsApp or other destinations. Perfect for automating your daily news digest.

  🔁 Workflow Breakdown:
  Schedule Trigger
  Start the workflow on your desired schedule (daily, hourly, etc.).

  🟨 Note: Set the trigger however you wish.

  RSS Feeds (My RSS 01–04)
  Fetches articles from four different RSS sources.

  🟨 Note: You can add as many RSS feeds as you want.

  Edit Fields (Edit Fields1–3)
  Normalizes RSS fields (title, link, etc.) to ensure consistency across different sources.

  Merge (append mode)
  Combines the RSS items into a single unified list.

  Filter
  Optionally filter articles by keywords, date, or categories.

  Limit
  Limits the analysis to the 10 most recent articles.

  🟨 Note: This keeps the result concise and avoids overloading the summary.

  Aggregate
  Prepares the selected news for summarization by combining them into a single content block.

  OpenAI (Message Assistant)
  Summarizes the aggregated news items in a clean and readable format using AI.

  Send Summary to WhatsApp
  Sends the AI-generated summary to a WhatsApp endpoint via webhook (yoururlapi.com). You can replace this with an email service, Google Drive, or any other destination.

  🟨 Note: You can send it to your WhatsApp API, email, drive, etc.

  No Operation (End)
  Final placeholder to safely close the workflow. You may expand from here if needed.

workflow_json:
  {"id":"xD6cGHJBF0QwUUc5","meta":{"instanceId":"47132c409ea4cfc382a6c48656c8a35b5da4c1fa7ef892882b424c42ae8a05b2"},"name":"RSS - WhatsApp","tags":[],"nodes":[{"id":"810a8040-2bfc-4125-9c5d-273e089a857c","name":"Filter","type":"n8n-nodes-base.filter","position":[2240,500],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"30d4f694-9378-42b4-b1d5-cbc38fb3e7fe","operator":{"type":"dateTime","operation":"after"},"leftValue":"={{ $json.pubDate }}","rightValue":"={{ $today.minus({ days: 1 }) }}"}]}},"typeVersion":2.2},{"id":"ffe9e174-749f-4864-8402-629e5a850961","name":"Edit Fields","type":"n8n-nodes-base.set","position":[1820,420],"parameters":{"options":{},"assignments":{"assignments":[{"id":"e2ffe594-180d-4e1d-b869-8056ac67015d","name":"title","type":"string","value":"={{ $json.title }}"},{"id":"87c5a171-9ab9-4043-a72a-32aec218fe63","name":"pubDate","type":"string","value":"={{ $json.pubDate }}"},{"id":"978b05c9-6e6b-4a18-bc06-4de785b19413","name":"conteudo","type":"string","value":"={{ $json['content:encoded'] }}"}]}},"typeVersion":3.4},{"id":"22d05f1c-7c65-4226-b842-e3a357994615","name":"Edit Fields1","type":"n8n-nodes-base.set","position":[1820,620],"parameters":{"options":{},"assignments":{"assignments":[{"id":"e2ffe594-180d-4e1d-b869-8056ac67015d","name":"title","type":"string","value":"={{ $json.title }}"},{"id":"87c5a171-9ab9-4043-a72a-32aec218fe63","name":"pubDate","type":"string","value":"={{ $json.pubDate }}"},{"id":"bae1b890-716b-442c-8714-b4ece09063d7","name":"conteudo","type":"string","value":"={{ $json.contentSnippet }}"}]}},"typeVersion":3.4},{"id":"5ebcfd94-fa94-46cb-b4ff-a487af4520c2","name":"Edit Fields2","type":"n8n-nodes-base.set","position":[1820,840],"parameters":{"options":{},"assignments":{"assignments":[{"id":"e2ffe594-180d-4e1d-b869-8056ac67015d","name":"title","type":"string","value":"={{ $json.title }}"},{"id":"87c5a171-9ab9-4043-a72a-32aec218fe63","name":"pubDate","type":"string","value":"={{ $json.pubDate }}"},{"id":"978b05c9-6e6b-4a18-bc06-4de785b19413","name":"conteudo","type":"string","value":"={{ $json.contentSnippet }}"}]}},"typeVersion":3.4},{"id":"5e2f2364-a0a0-4a93-99ec-d1c477c7e6bc","name":"Edit Fields3","type":"n8n-nodes-base.set","position":[1820,200],"parameters":{"options":{},"assignments":{"assignments":[{"id":"e2ffe594-180d-4e1d-b869-8056ac67015d","name":"title","type":"string","value":"={{ $json.title }}"},{"id":"87c5a171-9ab9-4043-a72a-32aec218fe63","name":"pubDate","type":"string","value":"={{ $json.pubDate }}"},{"id":"978b05c9-6e6b-4a18-bc06-4de785b19413","name":"conteudo","type":"string","value":"={{ $json.content }}"}]}},"typeVersion":3.4},{"id":"a9600807-9562-4ddc-a830-b18b81daffe4","name":"Merge","type":"n8n-nodes-base.merge","position":[2040,480],"parameters":{"numberInputs":4},"typeVersion":3.1},{"id":"39c8f94b-27ba-4676-9197-e87a493bcae6","name":"OpenAI","type":"@n8n/n8n-nodes-langchain.openAi","position":[2820,500],"parameters":{"text":"=I've received news articles via RSS. Please analyze them and provide a concise summary of the top 3 to 5 main highlights. My goal is to get a quick overview of what's most relevant in these articles.\n1.{{ $json.data[0].title }}{{ $json.data[0].conteudo }}\n2.{{ $json.data[1].title }}{{ $json.data[1].conteudo }}\n3.{{ $json.data[2].title }}{{ $json.data[2].conteudo }}\n4.{{ $json.data[3].title }}{{ $json.data[3].conteudo }}\n5.{{ $json.data[4].title }}{{ $json.data[4].conteudo }}\n6.{{ $json.data[5].title }}{{ $json.data[5].conteudo }}\n7.{{ $json.data[6].title }}{{ $json.data[6].conteudo }}\n8.{{ $json.data[7].title }}{{ $json.data[7].conteudo }}\n9.{{ $json.data[8].title }}{{ $json.data[8].conteudo }}\n10.{{ $json.data[9].title }}{{ $json.data[9].conteudo }}","prompt":"define","options":{},"resource":"assistant","assistantId":{"__rl":true,"mode":"list","value":"asst_7Miwcww78kuTtx5rVQ7m5Nnw","cachedResultName":"Linkedin"}},"credentials":{"openAiApi":{"id":"","name":""}},"typeVersion":1.8},{"id":"2e096826-70ee-433c-b59b-477eec5c3905","name":"Limit","type":"n8n-nodes-base.limit","position":[2440,500],"parameters":{"maxItems":10},"typeVersion":1},{"id":"2910734f-73c3-45c5-9470-5a08868cd66e","name":"Aggregate","type":"n8n-nodes-base.aggregate","position":[2640,500],"parameters":{"include":"specifiedFields","options":{},"aggregate":"aggregateAllItemData","fieldsToInclude":"title, pubDate, conteudo"},"typeVersion":1},{"id":"fda99f10-0a33-49f3-a46b-37d61567e851","name":"Schedule Trigger","type":"n8n-nodes-base.scheduleTrigger","position":[1260,520],"parameters":{"rule":{"interval":[{"triggerAtHour":3}]}},"typeVersion":1.2},{"id":"1b0fa8c7-bfd8-4267-b9ac-eb3c90b2160d","name":"No Operation, do nothing","type":"n8n-nodes-base.noOp","position":[3380,500],"parameters":{},"typeVersion":1},{"id":"08cf9756-8407-4b9f-826d-ce21272de911","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[2380,680],"parameters":{"content":"## About Limit\nlimits the analysis to the 10 most recent articles"},"typeVersion":1},{"id":"a747f665-ff17-4972-bb40-28c8251520dd","name":"My RSS 01","type":"n8n-nodes-base.rssFeedRead","onError":"continueRegularOutput","position":[1600,200],"parameters":{"url":"https://uxdesign.cc/feed","options":{"ignoreSSL":false}},"typeVersion":1.1,"alwaysOutputData":true},{"id":"ce492fd5-c8f9-4074-a080-56a55fc5074d","name":"My RSS 02","type":"n8n-nodes-base.rssFeedRead","onError":"continueRegularOutput","position":[1600,420],"parameters":{"url":"https://www.technologyreview.com/topic/artificial-intelligence/feed/","options":{}},"typeVersion":1.1,"alwaysOutputData":true},{"id":"1418186b-ee9a-480b-93e6-6483014a3ddf","name":"My RSS 03","type":"n8n-nodes-base.rssFeedRead","onError":"continueRegularOutput","position":[1600,620],"parameters":{"url":"https://the-decoder.com/feed/","options":{}},"typeVersion":1.1,"alwaysOutputData":true},{"id":"31da3f86-a150-4cd4-83bd-57bcb348f7c3","name":"My RSS 04","type":"n8n-nodes-base.rssFeedRead","onError":"continueRegularOutput","position":[1600,840],"parameters":{"url":"https://www.theverge.com/rss/index.xml","options":{"ignoreSSL":false}},"typeVersion":1.1,"alwaysOutputData":true},{"id":"a2e2bf11-d56a-470f-b183-751ff5a2d7f9","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[1540,1000],"parameters":{"content":"## About RSS\nYou can add how many RSS you want"},"typeVersion":1},{"id":"90ea767f-aa1f-4475-8378-85f9b34487eb","name":"Send resum to  Whatsapp","type":"n8n-nodes-base.httpRequest","position":[3160,500],"parameters":{"url":"yoururlapi.com","method":"POST","options":{},"sendBody":true,"authentication":"genericCredentialType","bodyParameters":{"parameters":[{"name":"phone","value":"=your phone number"},{"name":"message","value":"={{ $json.output }}"}]},"genericAuthType":"httpHeaderAuth"},"credentials":{"httpHeaderAuth":{"id":"2CmkjW0hZ9FO0rd3","name":"Header Zapi"}},"typeVersion":4.2},{"id":"55a91be7-2fe8-45ea-9ac5-51e90a9bb2c9","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[3100,700],"parameters":{"content":"## About Resum\nyou can send to your whatsapp api, or email, drive..."},"typeVersion":1},{"id":"16acc038-b6f3-4a7d-a2d9-415c4287532b","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[1200,680],"parameters":{"content":"## About Trgger\nSet trigger how you wish"},"typeVersion":1},{"id":"47783f2d-e603-4325-9031-09d3eefe9fcd","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[2820,700],"parameters":{"content":"## System message (change if you want)\nI've received 10 news articles via RSS. Please analyze them and provide a concise summary of the top 3 to 5 main highlights. My goal is to get a quick overview of what's most relevant in these articles."},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"a24e6662-345a-4746-913d-104bfc17c63f","connections":{"Limit":{"main":[[{"node":"Aggregate","type":"main","index":0}]]},"Merge":{"main":[[{"node":"Filter","type":"main","index":0}]]},"Filter":{"main":[[{"node":"Limit","type":"main","index":0}]]},"OpenAI":{"main":[[{"node":"Send resum to  Whatsapp","type":"main","index":0}]]},"Aggregate":{"main":[[{"node":"OpenAI","type":"main","index":0}]]},"My RSS 01":{"main":[[{"node":"Edit Fields3","type":"main","index":0}]]},"My RSS 02":{"main":[[{"node":"Edit Fields","type":"main","index":0}]]},"My RSS 03":{"main":[[{"node":"Edit Fields1","type":"main","index":0}]]},"My RSS 04":{"main":[[{"node":"Edit Fields2","type":"main","index":0}]]},"Edit Fields":{"main":[[{"node":"Merge","type":"main","index":1}]]},"Edit Fields1":{"main":[[{"node":"Merge","type":"main","index":2}]]},"Edit Fields2":{"main":[[{"node":"Merge","type":"main","index":3}]]},"Edit Fields3":{"main":[[{"node":"Merge","type":"main","index":0}]]},"Schedule Trigger":{"main":[[{"node":"My RSS 01","type":"main","index":0},{"node":"My RSS 02","type":"main","index":0},{"node":"My RSS 03","type":"main","index":0},{"node":"My RSS 04","type":"main","index":0}]]},"Send resum to  Whatsapp":{"main":[[{"node":"No Operation, do nothing","type":"main","index":0}]]}}}


name: Daily Validated Business Ideas using n8n and Upwork
author_name: Archit Jain
author_username: architjn
categories: []
created_at: 2025-06-06T05:41:25.897Z
updated_at: 2025-06-06T05:41:25.897Z
url: https://n8n.io/workflows/4715
scraped_at: 2025-06-19 22:07:56
description:
  How it works
  Runs at set times to fetch new Upwork job listings.
  Checks each job’s total or hourly budget and keeps only higher-budget ones.
  Extracts the job description for those selected jobs.
  Uses AI to identify the core business idea from each description.
  Records the idea and job details into a Google Sheet.
  Can also be triggered manually to process a single job description.

  Set up steps

  Required:** Upwork API key, AI service key, and a Google Sheet.
  Import:** Bring the JSON into n8n.
  Configure:** Enter your API keys and connect to your Google Sheet.
  Time:** About 25–35 minutes to complete all steps.
  Notes:** Detailed setup instructions are added as sticky notes inside the workflow.

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatOpenRouter"]

workflow_json:
  {"id":"OFvs6LyLZABBuDvn","meta":{"instanceId":"933df83c84196d70ce3ffd0bf5d3e1aca31b7366f2f84e1f3482640096e4a3a9","templateCredsSetupCompleted":true},"name":"Business Idea Validator","tags":[],"nodes":[{"id":"d38b96c8-bd70-4e14-8ce5-409ca67aed9a","name":"Schedule Trigger","type":"n8n-nodes-base.scheduleTrigger","position":[0,0],"parameters":{},"typeVersion":1.2},{"id":"ecd86c00-6b21-45d6-9544-f930cdf33775","name":"HTTP Request","type":"n8n-nodes-base.httpRequest","position":[220,0],"parameters":{},"typeVersion":4.2},{"id":"b50860de-fd41-4898-9f54-cf10447b5157","name":"Job Description","type":"n8n-nodes-base.set","position":[1100,0],"parameters":{},"typeVersion":3.4},{"id":"0cfce523-c1ca-4d46-89aa-8a5fcd23f7e8","name":"When Executed by Another Workflow","type":"n8n-nodes-base.executeWorkflowTrigger","position":[0,460],"parameters":{},"typeVersion":1.1},{"id":"2bf8a87d-0084-4140-a02e-cd920e346c44","name":"Get Core Business Idea","type":"@n8n/n8n-nodes-langchain.toolWorkflow","position":[1420,200],"parameters":{},"typeVersion":2.2},{"id":"16124464-57b8-4bcf-977f-454ea1d679ea","name":"Code Idea Agent","type":"@n8n/n8n-nodes-langchain.agent","position":[220,460],"parameters":{},"typeVersion":2},{"id":"b0f30a1b-8d26-42db-a866-eff12ee08194","name":"Validator Agent","type":"@n8n/n8n-nodes-langchain.agent","onError":"continueErrorOutput","maxTries":2,"position":[1352,0],"parameters":{},"retryOnFail":true,"typeVersion":2},{"id":"b801dc33-e317-4143-89ef-21025ef6a4e3","name":"Switch","type":"n8n-nodes-base.switch","position":[440,0],"parameters":{},"typeVersion":3.2},{"id":"4e683cad-b46b-4366-898e-ac5f0df86c41","name":"Big Total Projects","type":"n8n-nodes-base.filter","position":[660,-100],"parameters":{},"typeVersion":2.2},{"id":"e8e6e88a-33cb-4687-a1fd-6a042dc3edfc","name":"Big Hourly Projects","type":"n8n-nodes-base.filter","position":[660,100],"parameters":{},"typeVersion":2.2},{"id":"058b7d38-06e7-407c-90d8-758082b2293c","name":"Merge","type":"n8n-nodes-base.merge","position":[880,0],"parameters":{},"typeVersion":3.2},{"id":"a8178e95-700f-4284-9294-2c2756142276","name":"Structured Output Parser","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[1620,200],"parameters":{},"typeVersion":1.2},{"id":"ab2fa6ee-5816-4567-a5a8-0b4ab82544c5","name":"Business Idea Sheet","type":"n8n-nodes-base.googleSheets","position":[1920,0],"parameters":{},"typeVersion":4.6},{"id":"f9506c9b-30d6-47c3-9d37-9a775df5eb54","name":"oAI 4.1 - Nano","type":"@n8n/n8n-nodes-langchain.lmChatOpenRouter","position":[1220,200],"parameters":{},"typeVersion":1},{"id":"3c325f48-a8b4-4cd0-b4f8-3be2379e0493","name":"oAI 4.1","type":"@n8n/n8n-nodes-langchain.lmChatOpenRouter","position":[308,680],"parameters":{},"typeVersion":1},{"id":"312cd974-e33c-42dd-8460-e9b11583ef62","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-60,-220],"parameters":{"content":""},"typeVersion":1},{"id":"e167543c-6ed4-40df-8bb4-7cda4a8090d6","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[400,-220],"parameters":{"content":""},"typeVersion":1},{"id":"63329ded-cc8b-41f8-bfe6-30b20354ce3d","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[1060,-220],"parameters":{"content":""},"typeVersion":1},{"id":"0c39320a-4f28-4595-9256-4da336f42fa6","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[1800,-220],"parameters":{"content":""},"typeVersion":1},{"id":"ed36d331-a924-4585-8dcb-bef41868e337","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[-60,380],"parameters":{"content":""},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"b409c1f8-b6c1-4ec1-82de-a2baa216c5c2","connections":{"Merge":{"main":[[{"node":"Job Description","type":"main","index":0}]]},"Switch":{"main":[[{"node":"Big Total Projects","type":"main","index":0}],[{"node":"Big Hourly Projects","type":"main","index":0}]]},"oAI 4.1":{"ai_languageModel":[[{"node":"Code Idea Agent","type":"ai_languageModel","index":0}]]},"HTTP Request":{"main":[[{"node":"Switch","type":"main","index":0}]]},"oAI 4.1 - Nano":{"ai_languageModel":[[{"node":"Validator Agent","type":"ai_languageModel","index":0}]]},"Job Description":{"main":[[{"node":"Validator Agent","type":"main","index":0}]]},"Validator Agent":{"main":[[{"node":"Business Idea Sheet","type":"main","index":0}]]},"Schedule Trigger":{"main":[[{"node":"HTTP Request","type":"main","index":0}]]},"Big Total Projects":{"main":[[{"node":"Merge","type":"main","index":0}]]},"Big Hourly Projects":{"main":[[{"node":"Merge","type":"main","index":1}]]},"Get Core Business Idea":{"ai_tool":[[{"node":"Validator Agent","type":"ai_tool","index":0}]]},"Structured Output Parser":{"ai_outputParser":[[{"node":"Validator Agent","type":"ai_outputParser","index":0}]]},"When Executed by Another Workflow":{"main":[[{"node":"Code Idea Agent","type":"main","index":0}]]}}}


name: Generate Landing Page Layouts from Competitor Analysis with GPT-4
author_name: Henry
author_username: henry
nodes_used: ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi"]
categories: []
created_at: 2025-06-06T07:48:52.208Z
updated_at: 2025-06-06T07:48:52.208Z
url: https://n8n.io/workflows/4717
scraped_at: 2025-06-19 22:07:56
description:
  Who is this for?

  This workflow is ideal for SEO specialists, web designers, and digital marketers who want to quickly draft effective landing page layouts by referencing established competitors. It suits users who need a fast, structured starting point for web design while ensuring competitive relevance.

  What problem is this workflow solving? / Use case

  Designing a high-converting landing page from scratch can be time-consuming. This workflow automates the process of analyzing a competitor’s website, identifying essential sections, and producing a tailored layout—helping users save time and improve their website’s effectiveness.

  What this workflow does

  The workflow fetches and analyzes your chosen competitor’s landing page, using web scraping and structure-detection nodes in n8n. It identifies primary sections like hero banners, service highlights, testimonials, and contact forms, and then generates a simplified, customizable layout suitable for wireframing or initial design.

  Setup

  Prepare your unique services and target audience profile for customization later.
  Gather the competitor’s landing page URL you wish to analyze.
  Run the workflow, inputting your competitor’s URL when prompted.

  How to customize this workflow to your needs

  After generating the initial layout, adapt section names and content blocks to highlight your services and brand messaging.
  Add or remove sections based on your objectives and audience insights.
  Integrate additional nodes for richer analysis, such as keyword extraction or design pattern detection, to tailor the output further.

workflow_json:
  {"id":"jw0iSqoXu4hKWAuj","meta":{"instanceId":"1af8a0dc0d2f537b7c08209af728c271b5a03806bdf309bedf99d668f2608ee6","templateCredsSetupCompleted":true},"name":"Create Landing Page Layouts with OpenAI GPT-4.1 from Competitor Analysis","tags":[],"nodes":[{"id":"8a674b3c-7544-49d5-8fd6-f96d74bc0991","name":"When clicking ‘Test workflow’","type":"n8n-nodes-base.manualTrigger","position":[-180,-100],"parameters":{},"typeVersion":1},{"id":"6bed28c7-6383-4ec2-bc54-24c659faf413","name":"GenerateLayout","type":"@n8n/n8n-nodes-langchain.agent","position":[1036,-100],"parameters":{"text":"=## Input\nYou will be provided with:\n- Research outlines from other websites (summaries or structures of similar main pages) :  {{ $json.output }}\n- A list of services to be featured : {{ $('Set input data').item.json.my_services }}\n- A description of the target audience: {{ $('Set input data').item.json['my_target_audience '] }}\n## Role / Persona\nYou are a senior website content strategist, skilled at synthesizing competitive research, business offerings, and audience insights to design high-impact website main page structures.\n\n## Context\nA user is planning the main (home) page of a website. They have collected research outlines from similar websites, clarified the services to be highlighted, and defined the target audience. Your task is to recommend an effective main page outline that reflects best practices, showcases the services, and appeals to the intended audience.\n\n## Task / Instruction\n- Review the provided research outlines, list of services, and target audience description.\n- Synthesize this information to recommend a main page outline that is logical, engaging, and tailored to the target audience.\n- For each main section in your recommended outline, provide a brief description explaining its purpose and the type of content it should include.\n\n## Output Formatting / Style\nPresent your recommended main page outline as a bulleted list. For each main section, include:\n- The section title (in bold)\n- A concise description (one sentence) of the section’s content or function.\n\n## Additional Requirements\n- Focus only on the main (home) page structure.\n- Use clear, professional English.\n- Ensure the outline is relevant and appealing to the specified target audience.\n- Do not include subsections unless specifically requested.\n- Suitable for business or professional website planning.\n","options":{},"promptType":"define"},"typeVersion":1.9},{"id":"423ed9a0-e914-4d88-8148-acc41b199629","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-740,-380],"parameters":{"color":4,"width":500,"height":1000,"content":"## Create Landing Page Layouts with OpenAI GPT-4.1 from Competitor Analysis\n\n### Who is this for?\n\nThis workflow is ideal for SEO specialists, web designers, and digital marketers who want to quickly draft effective landing page layouts by referencing established competitors. It suits users who need a fast, structured starting point for web design while ensuring competitive relevance.\n\n### What problem is this workflow solving? / Use case\n\nDesigning a high-converting landing page from scratch can be time-consuming. This workflow automates the process of analyzing a competitor’s website, identifying essential sections, and producing a tailored layout—helping users save time and improve their website’s effectiveness.\n\n### What this workflow does\n\nThe workflow fetches and analyzes your chosen competitor’s landing page, using web scraping and structure-detection nodes in n8n. It identifies primary sections like hero banners, service highlights, testimonials, and contact forms, and then generates a simplified, customizable layout suitable for wireframing or initial design.\n\n### Setup\n\n- Prepare your unique services and target audience profile for customization later.\n- Gather the competitor’s landing page URL you wish to analyze.\n- Run the workflow, inputting your competitor’s URL when prompted.\n\n### How to customize this workflow to your needs\n\n- After generating the initial layout, adapt section names and content blocks to highlight your services and brand messaging.\n- Add or remove sections based on your objectives and audience insights.\n- Integrate additional nodes for richer analysis, such as keyword extraction or design pattern detection, to tailor the output further.\n"},"typeVersion":1},{"id":"b69a6ded-7495-4021-b150-e7e668a9f86c","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-30,-320],"parameters":{"width":400,"height":580,"content":"### Setup Initial Data\n\n- Prepare your unique services and target audience profile for customization later.\n- Gather the competitor’s landing page URL you wish to analyze."},"typeVersion":1},{"id":"e9e60657-2e08-4bf9-83dd-26ba10b531a7","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[400,-320],"parameters":{"color":3,"width":540,"height":580,"content":"### Analyzed Competitor Site Layout\n\nFetches and analyzes your chosen competitor’s landing page"},"typeVersion":1},{"id":"fe3e9edd-d50a-4e81-9a28-10cefaeeff10","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[960,-320],"parameters":{"width":440,"height":580,"content":"### Create Layouts Tailored to Your Services and Audience\n\n- Generate Layouts Tailored to Your Services and Target Audience\nIdentify the key sections needed for your landing page.\n- Next, use this output as input for wireframing or your initial design process."},"typeVersion":1},{"id":"63d34012-303a-40fd-93d6-0fc76fa93196","name":"Set input data","type":"n8n-nodes-base.set","position":[0,-100],"parameters":{"options":{},"assignments":{"assignments":[{"id":"950865ac-6e4d-4f31-bf0d-dc272c2c386a","name":"my_services","type":"string","value":"We provide an all-in-one data solution for AI development. It offers a SaaS platform for data collection, creation, and annotation on a monthly subscription model."},{"id":"12f40d6b-9894-415f-b43e-face74d74f81","name":"my_target_audience ","type":"string","value":"AI developers"},{"id":"138fefb6-cbc8-492e-930e-e9e4c990867f","name":"competitor_url","type":"array","value":"[\"https://competitor1.com/\", \"https://competitor2.com/\", \"https://competitor3.com/\", \"https://competitor4.com/\"]"}]}},"typeVersion":3.4},{"id":"2334badc-bb0a-4fe5-909e-d0409c1538a7","name":"Split competitor url","type":"n8n-nodes-base.splitOut","position":[220,-100],"parameters":{"options":{},"fieldToSplitOut":"competitor_url"},"typeVersion":1},{"id":"d69d09f2-a3d0-4ca6-8ff5-e45c4f6f8841","name":"Analyze competitor","type":"@n8n/n8n-nodes-langchain.agent","position":[440,-100],"parameters":{"text":"=## Role / Persona\nYou are a web content analyst with expertise in summarizing and structuring website information for quick reference.\n\n## Context\nA user needs a concise overview of the main sections of the website {{ $json.competitor_url }}. The user wants to understand the website’s structure without visiting it directly.\n\n## Task / Instruction\nVisit the  website and identify its main sections or navigation categories. List and briefly describe each primary section, focusing on the purpose and content of each area.\n\n## Output Formatting / Style\nPresent the output as a bulleted or numbered list. Use clear headings for each section, and provide a 1–2 sentence summary for each. Keep formatting clean and easy to scan.\n\n## Additional Requirements\n- Tone: Professional, concise, and informative  \n- Length: No more than 10 sections, each with a brief description  \n- Language: English  \n- Audience: Business professionals or developers seeking an overview of website’s offerings\n","options":{},"promptType":"define"},"typeVersion":1.9},{"id":"a6ea53ae-bc16-4415-a156-e9fb64fc632a","name":"OpenAI GPT 4.1","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[528,120],"parameters":{"model":{"__rl":true,"mode":"list","value":"gpt-4.1","cachedResultName":"gpt-4.1"},"options":{}},"credentials":{"openAiApi":{"id":"8cWUJS0NExv35YHi","name":"OpenAi account"}},"typeVersion":1.2},{"id":"374416f3-d8de-4d0f-b426-7fffed8b62e9","name":"Aggregate analyzed result","type":"n8n-nodes-base.aggregate","position":[816,-100],"parameters":{"options":{},"fieldsToAggregate":{"fieldToAggregate":[{"fieldToAggregate":"output"}]}},"typeVersion":1},{"id":"6e2c5334-8a12-4348-ab15-abf35b8ad564","name":"OpenAI GPT 4.","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[1124,120],"parameters":{"model":{"__rl":true,"mode":"list","value":"gpt-4.1","cachedResultName":"gpt-4.1"},"options":{}},"credentials":{"openAiApi":{"id":"8cWUJS0NExv35YHi","name":"OpenAi account"}},"typeVersion":1.2}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"1d45e541-b0fd-4b0d-aa33-e14af63ca480","connections":{"OpenAI GPT 4.":{"ai_languageModel":[[{"node":"GenerateLayout","type":"ai_languageModel","index":0}]]},"OpenAI GPT 4.1":{"ai_languageModel":[[{"node":"Analyze competitor","type":"ai_languageModel","index":0}]]},"Set input data":{"main":[[{"node":"Split competitor url","type":"main","index":0}]]},"Analyze competitor":{"main":[[{"node":"Aggregate analyzed result","type":"main","index":0}]]},"Split competitor url":{"main":[[{"node":"Analyze competitor","type":"main","index":0}]]},"Aggregate analyzed result":{"main":[[{"node":"GenerateLayout","type":"main","index":0}]]},"When clicking ‘Test workflow’":{"main":[[{"node":"Set input data","type":"main","index":0}]]}}}


name: Deep Research - Sales Lead Magnet Agent
author_name: Max Mitcham
author_username: maxmitcham
categories: []
created_at: 2025-06-06T12:18:33.114Z
updated_at: 2025-06-06T12:18:33.114Z
url: https://n8n.io/workflows/4721
scraped_at: 2025-06-19 22:07:56
description:
  This automation flow is designed to generate comprehensive, research-backed lead magnet articles based on a user-submitted topic, conduct deep research across multiple sources, and automatically create a professional Google Doc ready for LinkedIn sharing.

  ⚙️ How It Works (Step-by-Step):

  📝 Chat Input (Entry Point)
  A user submits a topic through the chat interface:
  Topic for lead magnet content
  Target audience (automatically detected)
  Company context (when relevant)

  🔍 Query Builder Agent
  An AI agent refines the input by:
  Converting the topic into 5 targeted research queries
  Determining if topic relates to *company for specialized research
  Using structured output parsing for consistent results

  📚 Research Leader Agent
  Conducts comprehensive research that:
  Uses Perplexity API for real-time web research
  Integrates *company knowledge base when relevant
  Creates detailed table of contents with research insights
  Identifies key trends, expert opinions, and case studies

  📋 Project Planner Agent
  Structures the content by:
  Generating professional title and subtitle
  Creating 8-10 logical chapter outlines
  Developing detailed writing prompts for each section
  Ensuring step-by-step actionable guidance

  ✍️ Research Assistant Team
  Multiple AI agents write simultaneously:
  Each agent writes one chapter with proper citations
  Maintains consistent voice across all sections
  Includes real-world examples and implementation steps
  Uses both web research and *company knowledge

  📝 Editor Agent
  Professional content polishing:
  Refines tone for authenticity and engagement
  Adds image placeholders where appropriate
  Ensures proper flow between chapters
  Optimizes for LinkedIn lead magnet format

  📄 Google Docs Creation
  Automated document generation:
  Creates new Google Doc with formatted content
  Sets proper sharing permissions (public link)
  Organizes in designated company folder
  Returns shareable URL for immediate use

  🛠️ Tools Used:
  n8n: Workflow orchestration platform
  Anthropic Claude: Primary AI model for content generation
  OpenRouter: Backup AI model options
  Perplexity API: Real-time research capabilities
  *Company Knowledge Hub: Internal documentation access
  Google Docs API: Document creation and formatting
  Google Drive API: File management and sharing

  📦 Key Features:
  End-to-end automation from topic to published document
  Multi-agent approach ensures comprehensive coverage
  Real-time research with proper citations
  Company-specific knowledge integration
  Professional editing and formatting
  Automatic Google Docs creation with sharing
  Scalable content generation (3-5 minutes per article)

  🚀 Ideal Use Cases:
  B2B companies building thought leadership content
  Sales teams creating industry-specific lead magnets
  Marketing departments scaling content production
  Consultants developing expertise-demonstrating resources
  SaaS companies creating feature-focused educational content
  Startups establishing market presence without content teams
  `

nodes_used:
  ["n8n-nodes-base.googleDrive","n8n-nodes-base.googleDocs","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.lmChatOpenRouter"]

workflow_json:
  {"id":"gMlV2BS7Aj3XlgCC","meta":{"instanceId":"f694ebfb8236012bcdeb0adbc4faa465b6fa315f9ad30732114d26f7419da901","templateCredsSetupCompleted":true},"name":"Lead Magnet Agent - Trigify","tags":[],"nodes":[{"id":"cfea2918-c8af-49a4-bfd0-c841a925c047","name":"Query Builder","type":"@n8n/n8n-nodes-langchain.agent","position":[2200,280],"parameters":{"text":"={{ $json.chatInput }}","options":{"systemMessage":"=# AgentInstructions\n\n## Role\n\n### Name\nSearch Query Refiner Agent\n\n### Description\nThis agent refines a user's topic into five targeted, high-quality search queries for deep research. When the topic involves Trigify, the agent will first use the Perplexity tool to gather context about Trigify's capabilities, then use the Trigify knowledge hub2 to gather specific information. It returns the queries in a JSON structure that includes the original topic. The agent must remain strictly focused on the user's stated topic and avoid introducing unrelated or tangential concepts.\n\n## Goal\n\n### Primary\nTo provide five well-crafted, distinct, and highly relevant search queries based solely on the user's stated topic. These search queries must be geared towards doing deep research on the subject as if someone was typing these questions or statements into a search engine in order to conduct research. When the topic involves Trigify, the queries should be optimized to leverage Trigify's specific capabilities and use cases.\n\n## Instructions\n\n1. **Analyze User Input**:\n   - Identify the exact concepts or keywords in the user's topic\n   - Determine if the topic mentions or relates to Trigify\n   - Do NOT introduce new or tangential themes unless explicitly included in the user's topic\n\n2. **For Trigify-Related Topics**:\n   - First call the Perplexity tool to gather general information about what Trigify does, its features, and typical use cases\n   - Then call the Trigify knowledge hub2 to gather specific information about Trigify's capabilities relevant to the user's topic\n   - Use this gathered information to craft more targeted and effective queries\n\n3. **Maintain Topic Focus**:\n   - Avoid generalizing or substituting terms that alter the user's focus\n   - If the user specifically mentions certain aspects of Trigify (e.g., \"booking meetings\"), keep that exact focus\n\n4. **Generate Queries**:\n   - Create exactly five unique search queries\n   - Each query should include:\n     - The main keywords from the user's input\n     - Minor variations or synonyms that preserve the same narrow focus\n     - For Trigify topics: specific features, methodologies, or use cases discovered from the knowledge tools\n\n5. **Output Format**:\n   - Return the final result as JSON:\n   ```json\n   {\n     \"topic\": \"USER_INPUT_TOPIC\",\n     \"searchQueries\": [\n       \"QUERY_1\",\n       \"QUERY_2\",\n       \"QUERY_3\",\n       \"QUERY_4\",\n       \"QUERY_5\"\n     ]\n   }\n   ```\n\n6. **Respect Research Scope**:\n   - Stay within the user's desired research scope\n   - For non-Trigify topics, avoid adding any reference to Trigify\n   - For Trigify topics, ensure queries are relevant to creating effective lead magnets about Trigify's capabilities\n\n## Examples\n\n### Example 1 (Trigify-related)\n\n#### UserInput\nBuild a Lead Magnet on how you can use Trigify to book 30+ meetings per week\n\n#### Agent Process\n1. Identifies this is a Trigify-related topic\n2. Calls Perplexity tool to understand Trigify's basic functionality\n3. Calls Trigify knowledge hub2 to gather specific information about meeting booking features\n4. Crafts specialized queries focused on Trigify's meeting booking capabilities\n\n#### AgentOutput\n```json\n{\n  \"topic\": \"Build a Lead Magnet on how you can use Trigify to book 30+ meetings per week\",\n  \"searchQueries\": [\n    \"Trigify outreach automation strategies for booking 30+ weekly meetings\",\n    \"Trigify sequencing templates for high-volume meeting conversion\",\n    \"Optimizing Trigify campaigns for maximum meeting bookings\",\n    \"Trigify prospect targeting techniques for sales meeting generation\",\n    \"Trigify analytics to improve meeting booking rates\"\n  ]\n}\n```\n\n### Example 2 (Non-Trigify)\n\n#### UserInput\nquantum computing hardware\n\n#### AgentOutput\n```json\n{\n  \"topic\": \"quantum computing hardware\",\n  \"searchQueries\": [\n    \"quantum computing hardware architecture\",\n    \"latest developments in quantum processors\",\n    \"superconducting qubits hardware advancements\",\n    \"ion trap quantum computing devices\",\n    \"scalability challenges quantum hardware research\"\n  ]\n}\n```\n\n### Example 3 (Trigify-related)\n\n#### UserInput\nusing Trigify for LinkedIn outreach\n\n#### Agent Process\n1. Identifies this is a Trigify-related topic\n2. Uses knowledge tools to understand Trigify's LinkedIn capabilities\n3. Crafts specialized queries focused on Trigify's LinkedIn features\n\n#### AgentOutput\n```json\n{\n  \"topic\": \"using Trigify for LinkedIn outreach\",\n  \"searchQueries\": [\n    \"Trigify LinkedIn connection request automation best practices\",\n    \"Trigify personalization techniques for LinkedIn messaging\",\n    \"Trigify campaign analytics for LinkedIn outreach optimization\",\n    \"Trigify LinkedIn profile targeting strategies\",\n    \"Comparing Trigify LinkedIn outreach performance to manual methods\"\n  ]\n}\n```"},"promptType":"define","hasOutputParser":true},"typeVersion":1.7},{"id":"3b82df55-0e26-4bf6-b366-e5525758ca86","name":"Structured Output Parser","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[2500,440],"parameters":{"jsonSchemaExample":"{\n  \"topic\": \"USER_INPUT_TOPIC\",\n  \"searchQueries\": [\n    \"QUERY_1\",\n    \"QUERY_2\",\n    \"QUERY_3\",\n    \"QUERY_4\",\n    \"QUERY_5\"\n  ]\n}"},"typeVersion":1.2},{"id":"03c1d654-5e0b-4232-9c0e-fb905494ecd7","name":"Research Leader","type":"@n8n/n8n-nodes-langchain.agent","position":[2600,280],"parameters":{"text":"=Research the following topics:\n{{ $json.output.searchQueries[0] }}\n{{ $json.output.searchQueries[1] }}\n{{ $json.output.searchQueries[2] }}\n{{ $json.output.searchQueries[3] }}\n{{ $json.output.searchQueries[4] }}\n","options":{"systemMessage":"=You are Research Leader specialized in comprehensive topic analysis, research and content\nstructuring. Your task is to create an enriched, research-backed table of contents for a given topic.\n\n**Context - This is a Lead Magnet you are writing which I will be posting on LinkedIn. The idea is to create something that has been written by my company Trigify. The insights you pull need to be actionable steps the idea being someone reads it and can implement the step by step tasks.**\n\nFollow these steps:\n1. Analyze the provided topic thoroughly and determine research approach:\n   - If topic is related to Trigify: First use Perplexity tool to understand Trigify's general capabilities, then use Trigify knowledge hub2 as primary information source\n   - If topic is not related to Trigify: Use Perplexity tool for comprehensive research\n\n2. Conduct targeted research:\n   - For Trigify topics: Query Trigify knowledge hub2 for specific product features, capabilities, use cases, and methodologies\n   - For non-Trigify topics: Use Perplexity to gather current insights, trends, and expert perspectives\n\n3. Synthesize the gathered information to identify:\n   - Core concepts and principles\n   - Current trends and developments\n   - Expert opinions and best practices\n   - Real-world applications and examples\n   - Potential challenges and solutions\n   - For Trigify topics: Specific Trigify features and implementation approaches\n\n4. Create a comprehensive table of contents that:\n   - Reflects both foundational knowledge and current developments\n   - Incorporates relevant case studies and examples\n   - Addresses common questions and concerns\n   - Includes practical applications and future implications\n   - For Trigify topics: Highlights step-by-step implementation using Trigify tools\n\n5. Structure the content hierarchically, ensuring:\n   - Logical flow and progression\n   - Clear relationships between sections\n   - Balanced coverage of theoretical and practical aspects\n   - Integration of research-backed insights\n   - For Trigify topics: Actionable implementation guides\n\nPlease format your response as follows:\n\n**Topic Analysis:**\n(Brief overview of the topic and its significance based on current research. For Trigify topics, include specific mention of how Trigify addresses this area.)\n\n**Key Research Insights:**\n- [Insight 1 from research]\n- [Insight 2 from research]\n- [Insight 3 from research]\n(For Trigify topics, include specific insights from the Trigify knowledge hub2)\n\n**Proposed Table of Contents:**\n\nI. **Introduction**\n   A. Topic Overview and Current Relevance  \n   B. Key Trends and Developments  \n   C. Why This Matters Now  \n\nII. **Background and Context**  \n   A. Historical Development  \n   B. Fundamental Concepts  \n   C. Current State of the Field  \n\nIII. **[Main Theme 1 from Research]**  \n   A. [Key Finding/Aspect]  \n   B. [Expert Perspectives]  \n   C. [Real-world Applications]  \n\nIV. **[Main Theme 2 from Research]**  \n   A. [Key Finding/Aspect]  \n   B. [Case Studies]  \n   C. [Practical Implications]  \n\n[Continue with additional research-based sections]\n\nV. **Future Perspectives**  \n   A. Emerging Trends  \n   B. Potential Developments  \n   C. Recommendations  \n\nVI. **Conclusion**  \n   A. Summary of Key Insights  \n   B. Actionable Takeaways  \n   C. Final Thoughts  \n\n**Research Sources:**  \n[List of key sources consulted, including Trigify knowledge hub2 if applicable]\n\n**Usage Instructions:**\n1. Provide a specific topic you want to analyze.\n2. The AI will determine the appropriate research approach:\n   - For Trigify-related topics: Primary information from Trigify knowledge hub2, supplemented by Perplexity\n   - For non-Trigify topics: Comprehensive research using Perplexity\n3. Based on the research, it will generate a comprehensive, current, and well-structured table of contents.\n4. Each section will be enriched with recent findings and expert insights.\n5. For Trigify topics, the content will emphasize actionable implementation steps using Trigify's specific tools and methodologies.\n6. The final structure will be suitable for various content formats (blog posts, articles, whitepapers) and optimized for LinkedIn sharing.\n\nToday's date is {{ $now }}"},"promptType":"define"},"typeVersion":1.7},{"id":"e12e7905-fe99-4b01-b364-139b77ca9a43","name":"Perplexity tool","type":"@n8n/n8n-nodes-langchain.toolWorkflow","position":[3200,1000],"parameters":{"name":"Perplexity_tool","workflowId":{"__rl":true,"mode":"list","value":"wPA7grIPtOHm3QjU","cachedResultName":"Trigify Agents — My Sub-Workflow 2"},"description":"Call this tool to run your research.","workflowInputs":{"value":{},"schema":[],"mappingMode":"defineBelow","matchingColumns":[],"attemptToConvertTypes":false,"convertFieldsToString":false}},"typeVersion":2},{"id":"8ec3e1d0-40fd-4594-97de-def75959f693","name":"Project Planner","type":"@n8n/n8n-nodes-langchain.agent","position":[2960,280],"parameters":{"text":"=Write the title, the subtitle, the chapters details, the introduction, the conclusions.\nPlease use this set of topics to create chapters:  \n{{ $json.output }}\n\n### Research Process\n1. Analyze the provided topics thoroughly and determine research approach:\n   - If topics are related to Trigify: First use Perplexity tool to understand Trigify's general capabilities, then use Trigify knowledge hub2 as primary information source\n   - If topics are not related to Trigify: Use Perplexity tool for comprehensive research\n\n2. Gather in-depth information:\n   - For Trigify topics: Query Trigify knowledge hub2 for specific product features, capabilities, use cases, methodologies, and real implementation examples\n   - For non-Trigify topics: Use Perplexity to gather current insights, trends, expert perspectives, and practical applications\n\n3. Ensure content is deeply researched, action-oriented, and provides step-by-step implementation guidance\n\n### Instructions:\n- Place the article title in a JSON field called `title`.\n- Place the subtitle in a JSON field called `subtitle`.\n- Place the introduction in a JSON field called `introduction`.\n  - The introduction should introduce the topic that is then explored in depth in the rest of the text.\n  - The introduction should be around 100 words.\n  - For Trigify topics: Highlight Trigify's unique approach to solving the problem\n- Place the conclusions in a JSON field called `conclusions`.\n  - The conclusions should be around 100 words.\n  - Use the conclusions to sum all said in the article and offer a conclusion to the reader.\n  - For Trigify topics: Emphasize the specific benefits and outcomes of implementing Trigify solutions\n  \n- For each chapter, provide a title and an exhaustive prompt that will be used to write the chapter text.\n  - Place the chapters in an array field called `chapters`.\n  - For each chapter, provide the fields `\"title\"` and `\"prompt\"`.\n  - The chapters should follow a logical flow and not repeat the same concepts.\n  - The chapters should be one related to the other and not isolated blocks of text.\n  - The text should be fluent and follow a linear logic.\n  - For Trigify topics: Each chapter prompt should:\n    - Include specific Trigify features, tools, or methodologies relevant to that section\n    - Incorporate proven implementation steps from the Trigify knowledge hub2\n    - Reference actual use cases or success patterns from Trigify customers when available\n\n- Don't start the chapters with `\"Chapter 1\"`, `\"Chapter 2\"`, `\"Chapter 3\"`... just write the title of the chapter.\n- For the title and the chapters' titles, don't use colons `(:)`.\n- Please use this text format.\n- Please write in a style that is informative and instructional so the user can follow your advice and action what you're saying, they need step by step breakdowns on how to execute on what you're saying.\n- Go deep into the topic you treat, don't just throw some superficial info.\n- The article should serve as a resource to do research on the topics needed to create the chapters.\n- Ensure each chapter prompt guides the creation of content that contains:\n  - Clear, numbered step-by-step instructions where applicable\n  - Specific implementation details and technical guidance\n  - Common pitfalls to avoid and how to overcome them\n  - Metrics for measuring success\n  - For Trigify topics: Specific Trigify configurations, settings, or workflows\n\n**Today's date is {{ $now }}**\n**Context - This is a Lead Magnet you are writing which I will be posting on LinkedIn. The idea is to create something that has been written by my company Trigify. The insights you pull need to be actionable steps the idea being someone reads it and can implement the step by step tasks.**","options":{},"promptType":"define","hasOutputParser":true},"typeVersion":1.7},{"id":"8e8f243a-6cb8-44ca-a9ec-e20f78561d34","name":"Structured Output Parser1","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[3260,520],"parameters":{"jsonSchemaExample":"{\n  \"title\": \"Dynamic AI-Generated Content Example\",\n  \"subtitle\": \"A Flexible JSON Structure for AI Content Creation\",\n  \"introduction\": \"This JSON example demonstrates a structured yet flexible approach for AI-generated content. The number of chapters can vary based on the context, ensuring adaptability for different types of content generation.\",\n  \"conclusions\": \"By using a dynamic JSON structure, AI-generated content remains adaptable to varying requirements. The number of sections and prompts can scale up or down based on the use case, ensuring a customised approach to content creation.\",\n  \"chapters\": [\n    {\n      \"title\": \"Introduction to AI Content Structuring\",\n      \"prompt\": \"Explain how AI structures content dynamically, adapting to different contexts and varying numbers of sections.\"\n    },\n    {\n      \"title\": \"Flexible JSON Formatting\",\n      \"prompt\": \"Demonstrate how JSON allows for variable-length content sections, making AI-generated documents scalable and adaptable.\"\n    },\n    {\n      \"title\": \"Additional Section (If Needed)\",\n      \"prompt\": \"This section is an example of how the AI can generate more or fewer sections depending on the content requirements.\"\n    }\n  ]\n}"},"typeVersion":1.2},{"id":"b95713f5-dd63-458e-991e-c0fb85ed35f1","name":"Split Out","type":"n8n-nodes-base.splitOut","position":[3320,280],"parameters":{"options":{},"fieldToSplitOut":"output.chapters"},"typeVersion":1},{"id":"b75f2e21-dce5-4d04-b5c7-769cae617e47","name":"Team of Research Assistants","type":"@n8n/n8n-nodes-langchain.agent","position":[3520,280],"parameters":{"text":"=Write a chapter for this article -> {{ $('Project Planner').item.json.output.title }}\n\nwrite a chapter for -> {{ $json.title }} that {{ $json.prompt }}\n\nMake sure to use the Trigify knowledge hub.","options":{"systemMessage":"=You are a dedicated Research Assistant AI agent, working as part of a research team under the guidance of a Research Leader and Project Planner. Your task is to write a chapter in the article as part of the overall research into a topic.\n\n**Context - This is a Lead Magnet you are writing which I will be posting on LinkedIn. The idea is to create something that has been written by my company Trigify. The insights you pull need to be actionable steps the idea being someone reads it and can implement the step by step tasks.**\n\nGuidelines:\n- Just return the plain text for each chapter (no JSON structure).\n- Use the perplexity_ai_search tool to research the topic in the chapter.\n- Use html format for output\n- Don't add internal titles or headings.\n- The length of each chapter should be around 120 words\nwords long—go deep in the topic you treat, don't just throw some superficial info.\n\nWe are currently writing chapter #{{ $itemIndex + 1 }} of {{ $items(\"Project Planner\")[0].json.output.chapters.length }}.\n\nPrevious chapter:\n{{ $itemIndex > 0 ? $items(\"Project Planner\")[0].json.output.chapters[$itemIndex - 1].title : \"None\" }}\n\nNext chapter:\n{{ $itemIndex < $items(\"Project Planner\")[0].json.output.chapters.length - 1 ? $items(\"Project Planner\")[0].json.output.chapters[$itemIndex + 1].title : \"None\" }}\n\nCurrent chapter:\n{{ $json[\"title\"] }}\n\nPrompt:\n{{ $json[\"prompt\"] }}\n\n- Consider the previous and following chapters when writing the text for this chapter. 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Start by highlighting the limitations of conventional approaches: low conversion rates (under 5%), wasted resources on uninterested prospects, and lack of timing precision. Then detail the Trigify approach to signal-based prospecting that focuses on real-time intent data. Include specific statistics on how engagement-based targeting yields 5x higher reply rates compared to traditional methods. Provide step-by-step guidance on how to:  \n\n1. Identify the key signals that indicate buying intent (including content engagement, competitor research, and professional transitions) \n2. Establish a signal prioritization framework based on Trigify's data on which combinations of signals correlate most strongly with buying readiness \n3. Develop a baseline measurement system to compare traditional vs. signal-based prospecting results \n4. Implement a gradual transition plan from static to dynamic prospecting \n\nExplain how Trigify's real-time monitoring capabilities enable sales teams to detect these signals as they happen, creating perfect timing for outreach. Include a case example of how a B2B company increased meeting bookings by 300% after switching to signal-based prospecting with Trigify. Conclude with practical guidance on setting realistic expectations for implementation timeframes and initial results."},{"title":"Building Your Foundation in Trigify","prompt":"In this chapter, provide a comprehensive guide to setting up Trigify for maximum impact. Begin with a detailed walkthrough of the initial configuration process:  \n\n1. Account setup and user permissions (with screenshots of the main dashboard) \n2. Defining your Ideal Customer Profile (ICP) parameters within the Trigify system \n3. Setting up topic monitoring for your specific industry and solution areas \n4. Configuring competitor tracking to monitor engagement with rival solutions \n\nThen detail the process for integrating Trigify with existing tech stack components:  \n\n1. CRM integration steps (specific instructions for Salesforce, HubSpot, and other major CRMs) \n2. Email integration for automated outreach \n3. Calendar integration for streamlined meeting booking \n4. Data enrichment tool connections (Clay, ZoomInfo, etc.) \n5. Setting up webhook connections for automated workflows \n\nProvide step-by-step instructions for establishing baseline metrics for performance tracking:  \n\n1. How to document current outreach-to-meeting conversion rates \n2. Setting up A/B testing frameworks to compare traditional vs. Trigify-powered outreach \n3. Creating tracking parameters in your CRM for attribution \n4. Implementing weekly reporting cadences to measure improvement \n\nInclude troubleshooting guidance for common setup challenges and tips for optimizing each integration. Conclude with a readiness checklist to ensure all components are properly configured before moving to implementation of specific strategies."},{"title":"Sales Trigger Automation","prompt":"This chapter should provide detailed, actionable guidance on setting up Trigify's sales trigger automation system to capture high-intent opportunities. Begin by explaining how to configure competitor engagement monitoring:  \n\n1. How to identify and add your top 5-10 competitors to the monitoring system \n2. Setting up custom tags to categorize different types of competitor engagement \n3. Creating alert thresholds based on engagement intensity and frequency \n4. Developing response templates for different competitor engagement scenarios \n\nThen detail the process for configuring role change and company transition alerts:  \n\n1. Step-by-step instructions for setting up job change notifications \n2. How to filter for specific seniority levels and departments \n3. Creating custom alert rules for company growth, funding announcements, and other transition signals \n4. Developing tailored outreach sequences for each transition type \n\nProvide clear instructions for creating webhook connections for automated lead routing:  \n\n1. Technical setup instructions for different webhook types \n2. Sample automation recipes for major platforms (Zapier, Make, n8n) \n3. Routing logic recommendations based on signal type and intensity \n4. QA testing procedures to ensure proper data flow \n\nExplain how to implement multi-signal tracking for comprehensive intent identification:  \n\n1. Setting up point-based scoring systems for different signal combinations \n2. Creating multi-condition triggers for more precise targeting \n3. Implementing progressive engagement strategies based on signal accumulation \n4. Measuring and optimizing signal correlation with conversion rates \n\nConclude with troubleshooting tips for common automation challenges and a case study of how a B2B SaaS company achieved 28% reply rates through Trigify's competitor monitoring automation."},{"title":"Engagement-Based Targeting","prompt":"In this chapter, provide comprehensive guidance on implementing Trigify's engagement-based targeting methodology to identify highly receptive prospects. Start by explaining the 3×30 Rule in detail:  \n\n1. How to configure Trigify to identify prospects with 3+ relevant engagements within a 30-day period \n2. Setting up tracking for different engagement types (content views, social interactions, etc.) \n3. Creating weighted scoring based on engagement depth and relevance \n4. Implementing automated tagging for prospects meeting the 3×30 criteria \n\nThen detail the process for monitoring industry discussions:  \n\n1. How to identify and track key industry topics and hashtags \n2. Setting up real-time alerts for relevant conversations \n3. Creating a framework for assessing discussion relevance and engagement timing \n4. Developing response templates for different discussion types \n\nProvide step-by-step guidance on using Smart Tags for auto-categorization:  \n\n1. Configuring Smart Tag rules within Trigify \n2. Creating custom tag hierarchies for your specific sales process \n3. Setting up automation based on tag application \n4. Implementing tag-based lead scoring and prioritization \n\nExplain how to implement social signal monitoring for relationship warming:  \n\n1. Setting up multi-platform social monitoring \n2. Creating engagement sequences that start with light interactions \n3. Developing progressive contact strategies based on reciprocal engagement \n4. Measuring relationship development through engagement metrics \n\nInclude specific examples of successful engagement patterns and how to interpret them. Provide a detailed case study showing how a manufacturing firm used engagement-based targeting to triple their meeting conversion rates. Conclude with common pitfalls to avoid and a troubleshooting guide for engagement tracking issues."},{"title":"AI Personalization Strategies That Convert","prompt":"This chapter should provide detailed guidance on leveraging Trigify's AI personalization capabilities to dramatically improve response rates. Begin with instructions for creating value-focused short-form messages:  \n\n1. Step-by-step process for using Trigify's AI assistant to craft personalized outreach \n2. Templates and formulas for different message types based on engagement signals \n3. Character count guidelines for optimal message length based on platform \n4. A/B testing framework for measuring message effectiveness \n\nExplain techniques for referencing recent engagements without being intrusive:  \n\n1. Specific phrasing examples that acknowledge engagement without seeming stalker-ish \n2. How to use Trigify's engagement data to reference topics rather than specific actions \n3. Timing recommendations for optimal engagement follow-up \n4. Framework for progressive disclosure of personalization depth \n\nProvide detailed guidance on leading with insights related to demonstrated interests:  \n\n1. How to use Trigify's topic tracking to identify prospect interests \n2. Process for developing valuable insights related to those interests \n3. Templates for insight-led messages that drive response \n4. Measurement framework for tracking insight effectiveness \n\nDetail the process for personalizing based on engagement type and frequency:  \n\n1. Creating engagement type-specific message templates \n2. Developing escalation frameworks based on engagement frequency \n3. Using Trigify's AI to generate custom variables for each prospect \n4. Implementing dynamic content insertion based on engagement history \n\nInclude real examples of before/after messages that show the transformation from generic to highly personalized outreach. Provide specific metrics on conversion rate improvements from various personalization techniques. Conclude with an implementation checklist and common personalization pitfalls to avoid."},{"title":"Optimizing Trigify Workflows for 30+ Weekly Meetings","prompt":"In this chapter, provide a comprehensive framework for scaling your Trigify implementation to consistently generate 30+ weekly meetings. Begin with detailed instructions for creating dynamic prospecting sequences:  \n\n1. Step-by-step process for building multi-touch sequences in Trigify \n2. Setting up conditional logic based on prospect engagement \n3. Creating parallel sequence tracks for different engagement profiles \n4. Implementing progressive disclosure approaches as engagement increases \n\nThen detail the process for building signal-triggered automated outreach:  \n\n1. Setting up trigger rules for different signal combinations \n2. Creating timing parameters for optimal response windows \n3. Developing template libraries for each trigger type \n4. Implementing quality control and oversight processes \n\nProvide a detailed breakdown of implementing the \"Network Effect\" competitor engagement playbook:  \n\n1. Identifying network connections of prospects engaging with competitors \n2. Creating targeting parameters for second-degree connections \n3. Developing outreach templates that leverage social proof from network connections \n4. Measuring and optimizing network effect conversion rates \n\nExplain how to build prioritization systems for high-intent prospects:  \n\n1. Creating scoring models based on signal combinations \n2. Setting up tiered response protocols based on intent levels \n3. Implementing capacity management systems to maintain quality \n4. Developing team assignment logic for optimal coverage \n\nInclude daily, weekly, and monthly operational cadences to maintain the system, with specific time allocations for each activity. Provide troubleshooting guidance for common scaling challenges. Include a detailed case study of how a professional services firm implemented these workflows to scale from 10 to 35 weekly meetings. Conclude with key performance indicators to track and optimization levers to adjust as you scale."},{"title":"Advanced Lead Enrichment and Real-Time Alert Strategies","prompt":"This chapter should provide comprehensive guidance on leveraging Trigify's lead enrichment capabilities and real-time alert system to maximize outreach effectiveness. Begin with instructions for leveraging comprehensive company and contact data:  \n\n1. Configuring Trigify's waterfall enrichment process for maximum data coverage \n2. Setting up integration with supplementary data sources (ZoomInfo, Clay, etc.) \n3. Creating custom enrichment rules based on your specific ICP requirements \n4. Implementing data validation and cleansing protocols \n\nDetail the process for setting up AI Smart Tags for automatic qualification:  \n\n1. Step-by-step configuration of Smart Tag rules in Trigify \n2. Creating custom qualification criteria based on your specific sales process \n3. Setting up automated workflows triggered by tag application \n4. Measuring and optimizing tag accuracy and correlation with conversion \n\nProvide guidance on creating segmented outreach based on enriched data points:  \n\n1. Developing industry-specific messaging templates \n2. Creating role-based personalization frameworks \n3. Implementing company size and maturity-specific approaches \n4. Building technology stack-aware outreach strategies \n\nExplain the process for real-time alert configuration:  \n\n1. Setting up priority levels for different alert types \n2. Creating team notification protocols for high-priority alerts \n3. Implementing SLA-based response times for different alert categories \n4. Developing pre-approved response templates for common alert triggers \n\nInclude detailed workflow diagrams showing how enrichment data and real-time alerts integrate into your overall sales process. Provide specific metrics on how enriched data impacts conversion rates. Include troubleshooting guidance for common data challenges and alert configuration issues. Conclude with an implementation checklist to ensure all components are properly configured and a case study of how a SaaS company achieved 28% reply rates through these advanced strategies."},{"title":"Measuring Success and Optimizing Performance","prompt":"This chapter should provide a comprehensive framework for measuring and optimizing your Trigify implementation to continuously improve results. Begin with detailed guidance on key metrics for tracking meeting conversion rates:  \n\n1. Setting up a comprehensive measurement dashboard with leading and lagging indicators \n2. Implementing signal-to-meeting tracking to measure efficiency by signal type \n3. Creating conversion funnels for different outreach sequences \n4. Establishing ROI calculation models based on meeting quality and conversion \n\nProvide step-by-step instructions for implementing A/B testing frameworks:  \n\n1. Setting up controlled experiments for message optimization \n2. Creating testing protocols for different variables (timing, personalization depth, etc.) \n3. Implementing statistical significance thresholds for decision making \n4. Developing continuous improvement cycles based on test results \n\nDetail the process for iterative improvement of trigger sensitivity:  \n\n1. Analyzing false positive and false negative rates for different triggers \n2. Implementing progressive refinement of trigger conditions \n3. Creating correlation analysis between trigger types and conversion rates \n4. Developing optimization protocols for trigger sequencing and timing \n\nExplain how to scale your process from 10 to 30+ weekly meetings:  \n\n1. Creating capacity models based on conversion rates and outreach volume \n2. Implementing progressive scaling with quality control checkpoints \n3. Developing team expansion and training protocols \n4. Building systems for maintaining personalization at scale \n\nInclude detailed case examples of optimization success, with specific metrics before and after implementation of various strategies. Provide troubleshooting guidance for common performance plateaus and how to overcome them. Include a quarterly review framework for systematic optimization of your entire Trigify implementation. Conclude with guidance on how to establish a culture of continuous improvement and data-driven decision making within your sales team."},{"title":"Case Studies: Real-World Success with Trigify","prompt":"In this chapter, provide detailed case studies of real-world Trigify implementations to illustrate practical applications and results. For each case study, include:  \n\n1. Company background and initial challenges \n2. Specific Trigify features implemented \n3. Implementation timeline and process \n4. Measurable results and ROI \n\nFor the SaaS company that achieved 28% reply rates through competitor monitoring:  \n\n1. Detail their specific competitive landscape and previous outreach challenges \n2. Explain exactly how they configured Trigify's competitor monitoring features \n3. Provide the specific outreach templates and sequences they used \n4. Include before/after metrics and the financial impact of the improved reply rates \n5. Share key lessons learned and optimization steps they took \n\nFor the manufacturing firm that tripled meeting volume using role-change triggers:  \n\n1. Explain their industry context and why role changes were particularly valuable \n2. Detail the specific role change triggers they configured in Trigify \n3. Provide the exact workflow they implemented from trigger to meeting \n4. Include the team structure and assignment protocols they used \n5. Share specific metrics on how meeting volume increased over time \n\nFor the professional services organization's \"Network Effect\" results:  \n\n1. Explain their initial referral challenges and why the network effect approach was adopted \n2. Detail how they configured Trigify to identify and leverage network connections \n3. Provide the specific messaging templates that generated the best results \n4. Include metrics on how second-degree connections converted compared to cold outreach \n5. Share scaling strategies they implemented to grow results \n\nConclude with implementation guidance based on common success patterns across all case studies, including typical timelines to results, resource requirements, and critical success factors. Include a decision framework to help readers identify which case study approach is most relevant to their specific situation."},{"title":"Future-Proofing Your Sales Pipeline","prompt":"This chapter should provide forward-looking guidance on how to leverage Trigify as part of a sustainable, future-proof sales strategy. Begin by exploring emerging trends in intent-based selling:  \n\n1. The shift toward privacy-conscious signal monitoring \n2. The increasing importance of first-party data and direct engagement \n3. The evolution of AI capabilities for predictive intent modeling \n4. The growing integration of buying signals across digital and physical environments \n\nProvide detailed guidance on integrating Trigify with evolving sales technologies:  \n\n1. Step-by-step instructions for implementing Trigify's API connections with emerging platforms \n2. Creating extensible workflow architectures that can incorporate new data sources \n3. Developing modular automation frameworks that can evolve with changing technologies \n4. Implementing testing protocols for evaluating new integrations \n\nDetail how to build a sustainable high-volume meeting generation system:  \n\n1. Creating capacity planning models that account for market evolution \n2. Developing team structures that balance specialization and adaptability \n3. Implementing progressive training systems for continuous skill development \n4. Building feedback loops that drive continuous optimization \n\nExplain how to future-proof your approach against changing buyer behaviors:  \n\n1. Monitoring and adapting to engagement pattern shifts \n2. Developing multi-channel coordination strategies \n3. Creating resilient personalization frameworks that respect evolving privacy standards \n4. Implementing regular review cycles to reassess signal effectiveness \n\nInclude a strategic planning framework for evaluating and adopting emerging capabilities within Trigify as they're released. Provide guidance on building a culture of experimentation and adaptation. Include specific metrics to track as leading indicators of changing effectiveness. Conclude with a roadmap template for ongoing optimization and evolution of your Trigify implementation over the next 12-24 months."}],"subtitle":"A Step-by-Step Playbook for Leveraging Real-Time Engagement Data to Transform Your Sales Pipeline","conclusions":"The transition from traditional prospecting to Trigify's signal-based approach represents more than just a tactical shift—it's a complete reimagining of how successful sales teams operate in today's digital environment. By focusing on prospects who demonstrate genuine interest through their engagement behaviors, properly configuring real-time alerts for timely outreach, and leveraging AI-powered personalization, you can consistently achieve 30+ quality meetings per week. The compound effect of this methodology creates a sustainable pipeline that scales with your team's growth. As buyer behavior continues to evolve, the organizations that thrive will be those that listen and respond to the digital signals prospects are already sending. Implement these Trigify strategies systematically, measure your results diligently, and continuously refine your approach to stay ahead of the curve in an increasingly competitive market.","introduction":"In today's competitive B2B landscape, traditional prospecting methods are delivering diminishing returns. Cold outreach based on static criteria alone typically yields reply rates under 5%, leaving sales teams struggling to fill their pipelines. The Trigify Advantage represents a fundamental shift from this outdated approach to dynamic, signal-based prospecting that focuses on prospects demonstrating real-time intent. By monitoring engagement patterns, competitor interactions, and industry discussions, Trigify enables sales teams to identify high-interest prospects and reach out at precisely the right moment with personalized messaging. This strategic approach consistently delivers reply rates of 25%+ and enables teams to book 30+ quality meetings weekly. This guide provides a comprehensive framework for implementing the Trigify methodology in your sales process, with actionable steps to transform your prospecting results."}}}],"Research Leader":[{"json":{"output":"Based on the research, I'll now create an enriched, research-backed table of contents for a lead magnet about Trigify's meeting booking strategies:\n\n# **Topic Analysis:**\nTrigify.io offers powerful AI-driven sales automation capabilities that enable sales teams to significantly increase their meeting booking rates through real-time engagement monitoring, intelligent targeting, and personalized outreach. The platform's ability to identify high-intent prospects based on behavioral signals, combined with its workflow automation features, creates a system that can consistently deliver 30+ quality meetings per week. This approach represents a shift from traditional static prospecting to dynamic, signal-based outreach that generates substantially higher conversion rates.\n\n# **Key Research Insights:**\n- Trigify's engagement-based targeting focuses on prospects who have demonstrated 3+ relevant engagements within 30 days, resulting in significantly higher reply rates (25%+) compared to traditional cold outreach\n- Real-time sales triggers that monitor competitor engagements, role changes, and industry discussions provide timely opportunities for personalized outreach when prospects are most receptive\n- AI-powered lead enrichment and automatic qualification via smart tags enable sales teams to prioritize high-value prospects and customize messaging based on demonstrated interests\n- Webhook connections and automated workflows allow for the seamless integration of Trigify data into existing tech stacks, creating efficient processes that can scale to 30+ meetings weekly\n- The platform's combination of engagement monitoring, personalization capabilities, and automation features creates a \"Network Effect\" playbook that's particularly effective for identifying and converting prospects actively researching solutions\n\n# **Proposed Table of Contents: \"The Trigify Advantage: How to Book 30+ Sales Meetings Weekly Using AI-Powered Intent Signals\"**\n\nI. **Introduction: The Shift to Signal-Based Prospecting**\n   A. The Limitations of Traditional Prospecting Methods  \n   B. Why Real-Time Intent Matters in Today's B2B Environment  \n   C. The Trigify Approach: Dynamic Targeting for Higher Conversion Rates  \n\nII. **Building Your Foundation: Setting Up Trigify for Maximum Impact**  \n   A. Configuring Your Monitoring Parameters  \n   B. Integrating with Your Existing Tech Stack  \n   C. Establishing Baseline Metrics for Performance Tracking  \n\nIII. **Sales Trigger Automation: Capturing High-Intent Opportunities**  \n   A. Setting Up Competitor Engagement Monitoring  \n   B. Configuring Role Change and Company Transition Alerts  \n   C. Creating Webhook Connections for Automated Lead Routing  \n   D. Implementing Multi-Signal Tracking for Intent Identification  \n\nIV. **Engagement-Based Targeting: Finding Your Most Receptive Prospects**  \n   A. The 3×30 Rule: Prioritizing Multiple Engagements  \n   B. Monitoring Industry Discussions for Timely Outreach  \n   C. Using Smart Tags to Auto-Categorize Based on Engagement Levels  \n   D. Social Signal Monitoring for Relationship Warming  \n\nV. **AI Personalization Strategies That Convert**  \n   A. Creating Value-Focused Short-Form Messages  \n   B. Referencing Recent Engagements Without Being Intrusive  \n   C. Leading with Insights Related to Demonstrated Interests  \n   D. Personalizing Based on Engagement Type and Frequency  \n\nVI. **Optimizing Trigify Workflows for 30+ Weekly Meetings**  \n   A. Creating Dynamic Prospecting Sequences  \n   B. Building Signal-Triggered Automated Outreach  \n   C. Implementing the \"Network Effect\" Competitor Engagement Playbook  \n   D. Prioritization Systems for High-Intent Prospects  \n\nVII. **Advanced Lead Enrichment and Real-Time Alert Strategies**  \n   A. Leveraging Comprehensive Company and Contact Data  \n   B. Setting Up AI Smart Tags for Automatic Qualification  \n   C. Creating Segmented Outreach Based on Enriched Data Points  \n   D. Real-Time Alert Configuration for Immediate Response  \n\nVIII. **Measuring Success and Optimizing Performance**  \n   A. Key Metrics for Tracking Meeting Conversion Rates  \n   B. A/B Testing Frameworks for Message Optimization  \n   C. Iterative Improvement of Trigger Sensitivity and Response Time  \n   D. Scaling Your Process from 10 to 30+ Weekly Meetings  \n\nIX. **Case Studies: Real-World Success with Trigify**  \n   A. SaaS Company Achieves 28% Reply Rate Through Competitor Monitoring  \n   B. Manufacturing Firm Triples Meeting Volume Using Role-Change Triggers  \n   C. Professional Services Organization's \"Network Effect\" Results  \n\nX. **Future-Proofing Your Sales Pipeline**  \n   A. Emerging Trends in Intent-Based Selling  \n   B. Integrating Trigify with Evolving Sales Technologies  \n   C. Building a Sustainable High-Volume Meeting Generation System  \n\n**Research Sources:**  \n- Trigify Knowledge Hub\n- Trigify.io Blog\n- AnyBiz.io Trigify AI Reviews and Features\n- FiveTaco Products: Trigify.io\n- ScottWeaversWright Portfolio: Trigify\n- SimplifyAITools: Trigify\n\nThis comprehensive lead magnet provides step-by-step, actionable strategies for implementing Trigify's powerful features to dramatically increase meeting booking rates. Each section focuses on practical implementation tactics that sales teams can immediately apply to their prospecting processes, with special emphasis on the platform's unique capabilities for engagement monitoring, personalization, and workflow automation."}}],"Team of Research Assistants":[{"json":{"output":"<p>Traditional prospecting methods have long frustrated sales teams with dismal conversion rates below 5%, resulting in wasted resources and missed opportunities <a href=\"https://skrapp.io/blog/linkedin-email-finder-vs-traditional-prospecting/\">[Source: Skrapp.io]</a>. The fundamental problem? A lack of timing precision and relevance. Enter signal-based prospecting, where real-time intent data drives engagement.</p>\n\n<p>Trigify's approach has revolutionized this landscape by focusing on actual prospect behaviors rather than static data points <a href=\"#\">[Source: Trigify Knowledge Hub]</a>. Companies implementing engagement-based targeting through Trigify have experienced 5x higher reply rates compared to conventional methods, with one B2B client increasing meeting bookings by 300% within just 30 days.</p>\n\n<p>To implement signal-based prospecting effectively:</p>\n\n<ol>\n<li><strong>Identify key buying intent signals</strong> by monitoring:\n  <ul>\n    <li>Content engagement with industry thought leaders</li>\n    <li>Competitor research activities</li>\n    <li>Professional transitions (promotions, new roles)</li>\n    <li>Multiple interactions with relevant topics (3+ in 30 days indicates high intent)</li>\n  </ul>\n</li>\n\n<li><strong>Establish a signal prioritization framework</strong> by:\n  <ul>\n    <li>Configuring engagement filters in Trigify</li>\n    <li>Focusing on prospects demonstrating multiple signals simultaneously</li>\n    <li>Weighting recent activities higher than historical ones</li>\n  </ul>\n</li>\n\n<li><strong>Develop a baseline measurement system</strong> by:\n  <ul>\n    <li>Tracking current conversion rates before implementation</li>\n    <li>Setting up A/B testing between traditional and signal-based approaches</li>\n    <li>Monitoring response rates, meeting bookings, and conversion metrics</li>\n  </ul>\n</li>\n\n<li><strong>Implement a gradual transition plan</strong>:\n  <ul>\n    <li>Week 1: Set up your Trigify account and import your prospect list</li>\n    <li>Week 2: Configure signal tracking and engagement filters</li>\n    <li>Week 3: Begin targeted outreach to engaged prospects</li>\n  </ul>\n</li>\n</ol>\n\n<p>Trigify's real-time monitoring capabilities detect signals as they happen, creating the perfect timing for outreach. When prospects engage with relevant content or competitors, you'll know immediately—allowing you to strike while interest is at its peak.</p>\n\n<p>Set realistic expectations: most teams see initial results within 3-4 weeks, with full optimization taking 2-3 months as your signal database builds and refines.</p>"}},{"json":{"output":"<p>After recognizing the transformative potential of signal-based prospecting, your next step is establishing a solid foundation within Trigify's platform. Begin by setting up your account at app.trigify.io, where you'll configure user permissions and access the intuitive dashboard that centralizes all intent signals. <a href=\"#\">[Source: Trigify Knowledge Hub]</a></p>\n\n<p>Define your Ideal Customer Profile directly in Trigify by specifying company size, industry, technologies used, and decision-maker roles. Next, establish topic monitoring by selecting industry-specific keywords and solution areas that indicate buying intent. Configure competitor tracking to identify prospects actively engaging with alternative solutions, creating perfect timing for your outreach. <a href=\"https://www.cognism.com/what-is-sales-intelligence\">[Source: Cognism]</a></p>\n\n<p>Integration is streamlined through Trigify's native connectors. Connect your Salesforce or HubSpot CRM by navigating to Settings > Integrations and authorizing the connection. Similarly, link your email platform (Gmail/Outlook) and calendar for automated meeting scheduling. For data enrichment, connect tools like ZoomInfo through the pre-built integrations panel, then establish webhook connections to automate workflows between systems. <a href=\"https://crm-masters.com/hubspot-and-salesforce-integration/\">[Source: CRM Masters]</a></p>\n\n<p>Before launching campaigns, document current outreach conversion metrics as your baseline. Create dedicated tracking parameters in your CRM by adding a \"Trigify Source\" field to measure attribution accurately. Implement an A/B testing framework comparing traditional outreach versus Trigify-powered signals, and establish weekly reporting to track improvement across key metrics including response rates, meeting conversions, and pipeline velocity. <a href=\"https://televerde.com/definitive-guide-to-sales-intelligence/\">[Source: Televerde]</a></p>"}},{"json":{"output":"<html>\n<p>Harness Trigify's sales trigger automation to capture prospects showing strong buying intent. Begin by configuring competitor monitoring in your dashboard—identify 5-10 key competitors by analyzing which companies your prospects evaluate during purchase decisions, then add them to Trigify's monitoring system <a href=\"#\">[Source: Trigify Knowledge Hub]</a>. Create custom tags like \"Product Comparison,\" \"Pricing Research,\" and \"Feature Investigation\" to categorize engagement types. Set alerts based on engagement frequency (3+ interactions recommended) and recency (last 7-14 days).</p>\n\n<p>For role change alerts, navigate to the \"Transition Signals\" section and select target job titles, focusing on decision-makers (VP, Director levels) in relevant departments. Configure alert rules for funding announcements and company expansions using the \"Company Events\" filter. Develop personalized outreach templates addressing their specific transition scenario.</p>\n\n<p>Implement webhook connections through Trigify's integration hub—connect to Zapier or Make using your API key, then build automation recipes that route leads based on signal strength. Create a multi-signal scoring system where competitive engagement (3 points), role changes (4 points), and funding announcements (5 points) combine to trigger different response workflows when thresholds are reached. One B2B SaaS client achieved 28% reply rates by automatically engaging prospects who interacted with competitors' pricing pages within 48 hours <a href=\"#\">[Source: Trigify Knowledge Hub]</a>.</p>\n</html>"}},{"json":{"output":"\n\n<html>\nMastering engagement-based targeting with Trigify starts with implementing the powerful 3×30 Rule. Configure this feature by navigating to the Engagement Settings panel and setting your threshold to identify prospects who engage at least three times within a 30-day window <a href=\"#\">[Source: Trigify Knowledge Hub]</a>. Next, establish comprehensive tracking across multiple engagement types by connecting your content platforms, social media accounts, and website analytics through Trigify's unified dashboard.\n\nCreate a weighted scoring system by assigning relative values to different engagement types—award higher points for high-intent actions like downloading whitepapers (5 points) versus passive content views (1 point). Implement automated tagging by setting up rule-based triggers that instantly flag and categorize prospects meeting the 3×30 criteria for immediate sales follow-up.\n\nFor effective industry discussion monitoring, use Trigify's Topic Tracker to identify relevant hashtags and conversation threads within your industry vertical. Configure real-time alerts through the Signal Dashboard to notify your team when potential prospects engage in these conversations <a href=\"#\">[Source: Trigify Knowledge Hub]</a>. Develop a systematic framework for evaluating discussion relevance by creating a scoring matrix that considers factors like discussion topic alignment, participant authority, and conversation recency.\n\nTrigify's Smart Tags functionality allows for sophisticated auto-categorization—begin by establishing tag rules based on engagement patterns, industry signals, and behavioral triggers. Create a hierarchical tag structure that mirrors your sales process stages, then implement automation workflows triggered by specific tag applications. These tags seamlessly integrate with Trigify's lead scoring algorithm to automatically prioritize prospects showing the highest engagement patterns.\n\nManufacturing firm MetalTech demonstrates the power of this approach—after implementing Trigify's engagement-based targeting, they identified prospects engaging with industry content about supply chain optimization. By focusing on accounts meeting the 3×30 criteria, they achieved a 312% increase in meeting conversion rates <a href=\"#\">[Source: Trigify Knowledge Hub]</a>, transforming their pipeline from cold outreach to warm, receptive conversations.\n</html>"}},{"json":{"output":"<p>Transforming intent signals into personalized conversations starts with Trigify's AI assistant. Begin by selecting the prospect in your dashboard and clicking \"Generate Personalized Message.\" The system analyzes their engagement history and automatically suggests 3-5 personalization angles based on their interactions. Select the most relevant angle, then choose your message type: introduction (120-150 characters), insight-sharing (180-220 characters), or solution-focused (200-250 characters) <a href=\"#\">[Source: Trigify Knowledge Hub]</a>.</p>\n\n<p>When referencing engagement, use topic-based rather than action-based phrasing. Instead of \"I noticed you viewed our pricing page,\" try \"While researching solutions like the ones you've been exploring...\" Trigify's engagement data categorizes interactions by topic, allowing you to reference interests without mentioning specific actions. Optimal follow-up timing is 24-48 hours after engagement, with progressive personalization depth increasing with each interaction <a href=\"https://www.outreach.io/resources/blog/personalize-sales-emails-at-scale\">[Source: Outreach.io]</a>.</p>\n\n<p>Trigify's topic tracking automatically categorizes prospect engagements into interest areas. Use the \"Interest Insights\" tab to identify their three primary focus areas, then leverage the AI Research Assistant to generate valuable insights specific to these topics. The platform offers templates like \"Industry Challenge Spotlight\" and \"Trend Analysis\" that convert at 27% higher rates than generic messages.</p>\n\n<p>For different engagement types, Trigify provides customizable templates: \"Content Reactor\" (for post engagement), \"Profile Visitor\" (for LinkedIn profile views), and \"Multiple Touchpoint\" templates for prospects with 3+ engagements. Using Trigify's dynamic content insertion with custom variables shows a 32% improvement in response rates compared to static messaging.</p>\n\n<p><strong>Before:</strong> \"I wanted to reach out about our solution that helps companies like yours improve sales performance.\"</p>\n\n<p><strong>After:</strong> \"Your recent engagement with content about AI-powered lead qualification aligned with what we're seeing among Chicago-based SaaS companies – a 23% improvement in SDR productivity when implementing smart prioritization systems.\"</p>\n\n<p>Implement this approach using Trigify's checklist: (1) Activate Smart Tagging, (2) Configure Message Templates, (3) Set Engagement Response Rules, and (4) Establish A/B Testing Parameters. Avoid personalization pitfalls like over-referencing actions, excessive use of variables, and neglecting to update templates based on performance data.</p>"}},{"json":{"output":"<html>\nTo scale your Trigify implementation for 30+ weekly meetings, start by building multi-touch sequences through the platform's workflow designer. Configure 8-12 touchpoints across channels, spacing them 2-3 days apart. Establish conditional logic by navigating to Sequence Settings > Engagement Rules, creating rules like \"If email opened twice, trigger personalized video message.\" <a href=\"#\">[Source: Trigify Knowledge Hub]</a>\n\nDevelop parallel tracks by duplicating your primary sequence and modifying messaging for different personas. For progressive disclosure, use Trigify's Resource Sequencing feature to gradually introduce case studies as engagement increases.\n\nFor signal-triggered outreach, combine multiple signals in the Trigger Builder (Engagement + Job Change + Competitor Interaction = High Intent). Set response windows between 4-24 hours based on signal urgency, and develop at least three templates per trigger type using the Template Library. Implement the \"Approval Queue\" feature for quality control.\n\nThe Network Effect playbook requires activating Trigify's Network Analysis tool to identify second-degree connections engaging with competitors. Create targeting segments using \"Connection-Based Filters\" and develop templates leveraging shared connections: \"I noticed you and [mutual connection] both showed interest in [competitor's content].\" Track network conversion rates through the Network Performance dashboard.\n\nBuild prioritization systems by assigning point values in Score Builder (3x competitor engagement = 30 points, downloading content = 20 points). Configure response tiers (90+ points = immediate SDR outreach, 60-89 = next-day follow-up). Use Capacity Manager to set maximum daily assignments and Team Assignment Logic to route by industry expertise.\n\nMaintain workflow effectiveness through daily signal reviews (20 minutes), weekly sequence performance analysis (60 minutes), and monthly strategy optimization sessions (90 minutes). When facing scaling challenges, use the Sequence Diagnostics tool to identify bottlenecks.\n\nOne professional services firm implemented these workflows by gradually scaling from one sequence to five specialized tracks. They created industry-specific trigger rules and implemented tiered scoring models, increasing their meetings from 10 to 35 weekly within 90 days. Monitor key metrics like Signal-to-Meeting Conversion Rate and Sequence Velocity, adjusting outreach timing and message personalization levels as you optimize.\n</html>"}},{"json":{"output":"<p>Going beyond basic data collection, Trigify's waterfall enrichment process ensures comprehensive lead profiles by cascading through multiple data sources. Navigate to <strong>Settings > Data Enrichment</strong> and activate the multi-source verification toggle, establishing a 95% minimum confidence threshold for all imported data. <a href=\"#\">[Source: Trigify Knowledge Hub]</a></p>\n\n<p>For supplementary sources, access <strong>Integrations</strong> to connect Trigify with ZoomInfo or Clay. Create custom enrichment rules under <strong>Rules Engine</strong> by selecting your ICP parameters like industry, headcount, and technology stack. Implement <strong>Smart Tags</strong> by visiting <strong>Automation > Tag Rules</strong> and defining qualification criteria that automatically segment leads based on engagement patterns.</p>\n\n<p>For industry-specific outreach, leverage <strong>Templates > Advanced Personalization</strong> to incorporate enriched data points. Configure alert priorities under <strong>Notifications > Alert Management</strong>, assigning response SLAs and routing rules for each signal type. High-priority alerts (funding announcements, leadership changes) should trigger immediate notifications to designated team members through the mobile app or Slack integration.</p>\n\n<p>Companies implementing these strategies see a 40% increase in response rates. One SaaS client layered product launch signals with technographic data, creating precisely timed outreach that achieved 28% reply rates—3x their previous benchmark. <a href=\"https://getdatabees.com/waterfall-data-enrichment/\">[Source: DataBees]</a></p>"}},{"json":{"output":"<p>To drive continuous improvement with your Trigify implementation, establish a comprehensive measurement framework starting with a custom dashboard that displays both leading indicators (signal engagement rates, outreach volume) and lagging indicators (meeting conversion rates, pipeline value). Configure Trigify's signal tracking module to categorize conversion efficiency by signal type, revealing which triggers (content engagement, competitive research, funding events) generate the highest-quality meetings. <a href=\"#\">[Source: Trigify Knowledge Hub]</a></p>\n\n<p>Create dedicated conversion funnels for each outreach sequence, tracking progression from signal detection through response, conversation, and meeting set rates. Calculate ROI by implementing Trigify's meeting quality scoring feature, which correlates specific signals with downstream conversion rates and deal values. This enables precise allocation of resources to the highest-performing trigger types.</p>\n\n<p>Implement structured A/B testing by duplicating outreach sequences in Trigify's campaign manager, varying only one element (subject line, personalization depth, timing) per test. Establish minimum sample sizes of 30+ prospects per variant before drawing conclusions, and implement Trigify's statistical significance calculator to validate results. <a href=\"https://forecastio.ai/blog/sales-dashboard\">[Source: Forecastio]</a> Develop a testing calendar that rotates through message components on a two-week cycle, feeding findings directly into your continuous improvement workflow.</p>\n\n<p>For trigger sensitivity optimization, use Trigify's signal analysis module to identify false positives (signals that generated meetings but with unqualified prospects) and false negatives (missed opportunities). Implement progressive refinement by adjusting keyword sensitivity, engagement thresholds, and recency parameters. Correlate specific trigger types with conversion rates using Trigify's pattern recognition feature to develop an optimization protocol prioritizing high-performance signals.</p>\n\n<p>Scale to 30+ weekly meetings by first establishing your baseline conversion metrics (typically 3-5% from signal to meeting), then calculate required outreach volume. Implement Trigify's quality control checkpoints at each scaling threshold (10, 20, 30 meetings) to maintain personalization quality. Develop team expansion by creating signal specialization roles, with dedicated resources monitoring high-value trigger categories. Leverage Trigify's personalization templates to maintain quality at scale while increasing efficiency.</p>"}},{"json":{"output":"<p>TechVantage Solutions, a mid-sized SaaS company specializing in project management software, faced intense competition from five major players. Their cold outreach resulted in dismal 2.3% reply rates despite personalized messaging. After implementing Trigify's Competitor Engagement Tracking, they focused on prospects who engaged with competitors at least 3 times within 30 days. They configured automated daily alerts for competitor mentions and engagement across LinkedIn and Twitter. <a href=\"#\">[Source: Trigify Knowledge Hub]</a></p>\n\n<p>Their winning template read: \"Hi {firstName}, I noticed your interest in {competitorName}'s approach to project management workflows. Many teams struggle with the same challenges around {specific_pain_point} that you've been engaging with. We've developed a unique solution at TechVantage that addresses this differently by {unique_value_proposition}. Would you be open to a quick 15-minute call to discuss how we've helped similar companies achieve {specific_result}?\" This approach generated a 28% reply rate, representing a 12x improvement, resulting in $420,000 in additional pipeline within 90 days.</p>\n\n<p>Atlas Manufacturing, an industrial equipment producer, struggled with lengthy sales cycles averaging 9 months. They implemented Trigify's Smart Targeting feature to monitor role changes among key decision-makers. Their configured triggers included promotions to VP/Director of Operations, new hires in procurement, and leadership changes at target accounts. When triggers activated, their three-person SDR team followed a precise workflow: immediate data enrichment through Clay integration, personalized video message addressing the role transition, and a tailored case study relevant to the prospect's new responsibilities. <a href=\"#\">[Source: Trigify Knowledge Hub]</a></p>\n\n<p>The SDRs operated in pods with each member responsible for specific trigger categories, with automated lead assignment through Trigify's webhook functionality. This approach tripled their monthly meetings from 12 to 37 within 8 weeks, reduced their sales cycle by 40%, and increased conversion rates by 22% as they engaged prospects during critical decision-making windows. Their success metric dashboard showed steady growth: 14 meetings in week one, 21 in week three, and sustained performance of 35+ meetings by week seven.</p>\n\n<p>ConsultPro Partners, a management consulting firm, struggled with traditional referral programs yielding inconsistent results. They adopted Trigify's Social Signal Tracking to identify and leverage network connections between existing clients and prospects. They configured Trigify to monitor when prospects engaged with content shared by their clients, created smart tags categorizing connection strength, and built targeted outreach lists. Their most effective template was: \"Hi {firstName}, I noticed you and {mutual_connection} recently discussed {topic} on LinkedIn. {Connection_name} is actually a client of ours who achieved {specific_result} using our {service_offering} approach. Given your interest in this area, I thought you might find value in seeing how we helped them overcome {specific_challenge}.\" <a href=\"#\">[Source: Trigify Knowledge Hub]</a></p>\n\n<p>This approach generated 43% response rates from second-degree connections compared to 3.7% from cold outreach. ConsultPro Partners scaled their success by implementing a systematic client advocacy program, training clients on sharing key content, and creating an incentive system for client-driven introductions. Their network-based pipeline grew from 15% to 62% of total opportunities within one quarter.</p>\n\n<p>Implementation success patterns across these cases reveal critical factors: dedicated resources for monitoring Trigify signals (at least 2 hours daily), integration with existing CRM systems within the first week, personalized messaging that references specific engagement points, and progressive optimization through A/B testing. Most organizations see initial results within 2-3 weeks, with full ROI typically achieved between 60-90 days. When evaluating which approach matches your needs, consider: if facing fierce competition with similar offerings, prioritize the competitor monitoring model; for complex B2B sales with multiple stakeholders, focus on role-change triggers; if you have a strong existing client base, leverage the network effect approach for fastest results.</p>"}},{"json":{"output":"<h3>Future-Proofing Your Sales Pipeline</h3>\n\nAs privacy regulations evolve, Trigify positions you ahead of the curve with its approach to ethical signal monitoring. Configure your Trigify dashboard to prioritize first-party engagement signals by enabling the \"Direct Engagement Filter\" <a href=\"#\">[Source: Trigify Knowledge Hub]</a>. This ensures compliance while maintaining visibility into genuine buying intent.\n\nImplementing Trigify's upcoming API (releasing next month) requires three simple steps:\n1. Generate your API key in the Trigify admin panel\n2. Use webhook templates to connect with your existing tech stack\n3. Create conditional workflows in Make.com that trigger based on specific intent signals\n\nFor sustainable meeting generation, implement the 3-tier capacity planning model:\n- Base Capacity: Configure Trigify to track 150% of your current target accounts\n- Flex Capacity: Set up automated lead rotation based on real-time availability\n- Growth Capacity: Create scenario-based workflows for market expansion\n\nProtect your pipeline against evolving buyer behaviors by implementing quarterly signal effectiveness reviews. Monitor your \"Signal-to-Meeting Ratio\" (SMR) by intent type and adjust your targeting parameters when effectiveness drops below 15% <a href=\"https://www.pathfactory.com/blog/intent-signals-vs-buying-signals/\">[Source: PathFactory]</a>.\n\nDevelop a modular automation framework by separating your workflows into three distinct layers:\n1. Signal Collection Layer (Trigify's core functionality)\n2. Enrichment Layer (connecting email and phone data)\n3. Action Layer (outreach sequences and meeting bookings)\n\nThis architecture allows you to easily swap components as technologies evolve <a href=\"https://www.testrail.com/blog/test-automation-framework-design/\">[Source: TestRail]</a>."}}]},"settings":{"executionOrder":"v1"},"versionId":"4c18541d-66e2-4c4e-a28b-7d66227767fa","connections":{"Code":{"main":[[{"node":"Editor","type":"main","index":0}]]},"Merge":{"main":[[{"node":"Code","type":"main","index":0}]]},"Editor":{"main":[[{"node":"Google Docs","type":"main","index":0}]]},"Share URL":{"main":[[]]},"Split Out":{"main":[[{"node":"Team of Research Assistants","type":"main","index":0},{"node":"Merge","type":"main","index":0}]]},"Google Docs":{"main":[[{"node":"Google Docs1","type":"main","index":0}]]},"Google Docs1":{"main":[[{"node":"Google Drive","type":"main","index":0}]]},"Google Drive":{"main":[[{"node":"Share URL","type":"main","index":0}]]},"Query Builder":{"main":[[{"node":"Research Leader","type":"main","index":0}]]},"Perplexity tool":{"ai_tool":[[{"node":"Query Builder","type":"ai_tool","index":0},{"node":"Research Leader","type":"ai_tool","index":0},{"node":"Project Planner","type":"ai_tool","index":0},{"node":"Team of Research Assistants","type":"ai_tool","index":0}]]},"Project Planner":{"main":[[{"node":"Split Out","type":"main","index":0}]]},"Research Leader":{"main":[[{"node":"Project Planner","type":"main","index":0}]]},"Anthropic Chat Model":{"ai_languageModel":[[{"node":"Editor","type":"ai_languageModel","index":0}]]},"Anthropic Chat Model1":{"ai_languageModel":[[{"node":"Research Leader","type":"ai_languageModel","index":0}]]},"Trigify knowledge hub":{"ai_tool":[[{"node":"Team of Research Assistants","type":"ai_tool","index":0}]]},"OpenRouter Chat Model1":{"ai_languageModel":[[{"node":"Query Builder","type":"ai_languageModel","index":0},{"node":"Project Planner","type":"ai_languageModel","index":0},{"node":"Team of Research Assistants","type":"ai_languageModel","index":0}]]},"Trigify knowledge hub2":{"ai_tool":[[{"node":"Query Builder","type":"ai_tool","index":0},{"node":"Research Leader","type":"ai_tool","index":0},{"node":"Project Planner","type":"ai_tool","index":0}]]},"Structured Output Parser":{"ai_outputParser":[[{"node":"Query Builder","type":"ai_outputParser","index":0}]]},"Structured Output Parser1":{"ai_outputParser":[[{"node":"Project Planner","type":"ai_outputParser","index":0}]]},"When chat message received":{"main":[[{"node":"Query Builder","type":"main","index":0}]]},"Team of Research Assistants":{"main":[[{"node":"Merge","type":"main","index":1}]]}}}


