Rahul Joshi This workflow corresponds to n8n.io template #7277 — we link there as the canonical source.
This workflow follows the Chainllm → Gmail recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "4Mr57mBTiQiphFDX",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Influencer daily suggestion",
"tags": [
{
"id": "HE1ilnFaryfUZjCN",
"name": "LinkedIn Automation",
"createdAt": "2025-08-07T12:32:17.084Z",
"updatedAt": "2025-08-07T12:32:17.084Z"
}
],
"nodes": [
{
"id": "47228c55-bd7e-459f-93ea-5c4185c4863f",
"name": "Process and Identify Top Topics",
"type": "n8n-nodes-base.code",
"position": [
592,
-128
],
"parameters": {
"jsCode": "// Advanced Professional LinkedIn Trending Topics Report Generator\n// Enhanced with Outlook compatibility for consistent email rendering\n\nconst items = $input.all();\nconst currentDate = new Date().toLocaleDateString('en-US', {\n weekday: 'long',\n year: 'numeric',\n month: 'long',\n day: 'numeric'\n});\n\nconst currentTime = new Date().toLocaleTimeString('en-US', {\n hour: '2-digit',\n minute: '2-digit',\n timeZoneName: 'short'\n});\n\n// Extract trending topics from input with enhanced error handling\nlet trendingTopics = [];\ntry {\n if (items[0]?.json?.text) {\n const jsonMatch = items[0].json.text.match(/```json(.+?)```/s);\n if (jsonMatch && jsonMatch[1]) {\n trendingTopics = JSON.parse(jsonMatch[1].trim());\n }\n } else if (items[0]?.json && Array.isArray(items[0].json)) {\n trendingTopics = items[0].json;\n } else if (Array.isArray(items)) {\n trendingTopics = items.map(item => item.json);\n }\n} catch (error) {\n console.error('Error parsing trending topics:', error);\n trendingTopics = [];\n}\n\n// Generate hashtags function\nconst generateHashtags = (title, description) => {\n const text = (title + ' ' + description).toLowerCase();\n const stopWords = ['the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by'];\n \n const industryHashtags = {\n 'ai|artificial intelligence|machine learning|ml': ['#AI', '#MachineLearning', '#ArtificialIntelligence', '#TechInnovation'],\n 'remote work|wfh|work from home|hybrid': ['#RemoteWork', '#WFH', '#HybridWork', '#DigitalNomad', '#FlexibleWork'],\n 'leadership|management|executive': ['#Leadership', '#Management', '#ExecutiveInsights', '#LeadershipDevelopment'],\n 'data|analytics|big data|data science': ['#Data', '#DataScience', '#Analytics', '#BigData', '#DataDriven'],\n 'marketing|digital marketing|brand': ['#Marketing', '#DigitalMarketing', '#Branding', '#ContentMarketing'],\n 'startup|entrepreneur|innovation': ['#Startup', '#Entrepreneur', '#Innovation', '#Entrepreneurship'],\n 'mental health|wellness|wellbeing': ['#MentalHealth', '#Wellness', '#WorkLifeBalance', '#EmployeeWellbeing'],\n 'diversity|inclusion|dei': ['#Diversity', '#Inclusion', '#DEI', '#WorkplaceCulture'],\n 'finance|fintech|investment': ['#Finance', '#FinTech', '#Investment', '#FinancialTech'],\n 'sales|revenue|growth': ['#Sales', '#SalesStrategy', '#RevenueGrowth', '#BusinessDevelopment'],\n 'customer|client|service': ['#CustomerExperience', '#CustomerSuccess', '#ClientService'],\n 'technology|tech|digital transformation': ['#Technology', '#Tech', '#DigitalTransformation', '#TechTrends'],\n 'team|collaboration|workplace': ['#Teamwork', '#Collaboration', '#WorkplaceCulture', '#TeamBuilding'],\n 'skill|training|learning|development': ['#SkillDevelopment', '#Learning', '#ProfessionalDevelopment', '#Training'],\n 'productivity|efficiency|automation': ['#Productivity', '#Efficiency', '#Automation', '#WorkSmart']\n };\n \n let hashtags = new Set(['#LinkedIn', '#Professional']);\n \n Object.entries(industryHashtags).forEach(([pattern, tags]) => {\n const regex = new RegExp(pattern, 'i');\n if (regex.test(text)) {\n tags.forEach(tag => hashtags.add(tag));\n }\n });\n \n const titleWords = title.split(/[\\s\\-_]+/)\n .filter(word => word.length > 3 && !stopWords.includes(word.toLowerCase()))\n .slice(0, 3);\n \n titleWords.forEach(word => {\n const cleanWord = word.replace(/[^\\w]/g, '');\n if (cleanWord.length > 2) {\n hashtags.add('#' + cleanWord.charAt(0).toUpperCase() + cleanWord.slice(1).toLowerCase());\n }\n });\n \n return Array.from(hashtags).slice(0, 10).join(' ');\n};\n\n// Calculate engagement potential score\nconst calculateEngagementScore = (topic) => {\n const text = (topic.title + ' ' + topic.description).toLowerCase();\n let score = 50; // Base score\n \n const highEngagementWords = ['ai', 'future', 'trend', 'innovation', 'success', 'growth', 'leadership', 'strategy'];\n highEngagementWords.forEach(word => {\n if (text.includes(word)) score += 10;\n });\n \n if (topic.description.length > 100 && topic.description.length < 300) score += 15;\n \n if (topic.title.split(' ').length >= 3 && topic.title.split(' ').length <= 8) score += 10;\n \n return Math.min(100, score);\n};\n\n// Generate the Outlook-compatible HTML report\nconst htmlReport = `\n<!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 <title>LinkedIn Trending Topics Report</title>\n <!--[if mso]>\n <noscript>\n <xml>\n <o:OfficeDocumentSettings>\n <o:AllowPNG/>\n <o:PixelsPerInch>96</o:PixelsPerInch>\n </o:OfficeDocumentSettings>\n </xml>\n </noscript>\n <![endif]-->\n <style type=\"text/css\">\n /* Reset and base styles */\n body, table, td, p, a, li, blockquote {\n -webkit-text-size-adjust: 100%;\n -ms-text-size-adjust: 100%;\n }\n \n table, td {\n mso-table-lspace: 0pt;\n mso-table-rspace: 0pt;\n border-collapse: collapse !important;\n }\n \n body {\n margin: 0 !important;\n padding: 0 !important;\n background-color: #f5f7fa;\n font-family: Arial, sans-serif;\n font-size: 14px;\n line-height: 1.4;\n color: #333333;\n }\n \n /* Outlook-specific fixes */\n .outlook-group-fix {\n width: 100% !important;\n }\n \n /* Main container */\n .email-container {\n max-width: 650px;\n margin: 0 auto;\n background-color: #ffffff;\n }\n \n /* Header styles */\n .header {\n background-color: #0077b5;\n padding: 30px 20px;\n text-align: center;\n }\n \n .header h1 {\n margin: 0 0 10px 0;\n color: #ffffff;\n font-size: 28px;\n font-weight: bold;\n line-height: 1.2;\n }\n \n .header-subtitle {\n margin: 0 0 10px 0;\n color: #ffffff;\n font-size: 16px;\n opacity: 0.9;\n }\n \n .header-time {\n margin: 0;\n color: #ffffff;\n font-size: 14px;\n opacity: 0.8;\n }\n \n /* Stats section */\n .stats-section {\n padding: 20px;\n background-color: #ffffff;\n }\n \n .stats-table {\n width: 100%;\n border-collapse: collapse;\n }\n \n .stat-cell {\n width: 25%;\n padding: 15px;\n text-align: center;\n background-color: #f8f9fa;\n border: 1px solid #e9ecef;\n vertical-align: top;\n }\n \n .stat-number {\n display: block;\n font-size: 24px;\n font-weight: bold;\n color: #0077b5;\n margin-bottom: 5px;\n line-height: 1.1;\n }\n \n .stat-label {\n color: #666666;\n font-size: 12px;\n font-weight: normal;\n margin: 0;\n }\n \n /* Content section */\n .content-section {\n padding: 20px;\n background-color: #ffffff;\n }\n \n /* Main data table */\n .data-table {\n width: 100%;\n border-collapse: collapse;\n margin: 0;\n }\n \n .data-table th {\n background-color: #0077b5;\n color: #ffffff;\n padding: 12px 8px;\n text-align: left;\n font-weight: bold;\n font-size: 13px;\n border: 1px solid #005b8c;\n }\n \n .data-table td {\n padding: 12px 8px;\n border: 1px solid #e9ecef;\n vertical-align: top;\n font-size: 13px;\n line-height: 1.4;\n }\n \n .data-table tr:nth-child(even) td {\n background-color: #f8f9fa;\n }\n \n .topic-rank {\n display: inline-block;\n background-color: #e3f2fd;\n color: #0077b5;\n padding: 3px 8px;\n font-size: 11px;\n font-weight: bold;\n border-radius: 3px;\n margin-right: 5px;\n }\n \n .topic-title {\n font-weight: bold;\n color: #005b8c;\n margin: 0 0 5px 0;\n }\n \n .engagement-score {\n display: inline-block;\n background-color: #e6f7ee;\n color: #28a745;\n padding: 3px 8px;\n font-size: 11px;\n font-weight: bold;\n border-radius: 3px;\n margin-top: 8px;\n }\n \n .hashtags {\n font-size: 12px;\n color: #666666;\n line-height: 1.3;\n word-break: break-word;\n }\n \n /* Footer */\n .footer {\n background-color: #f8f9fa;\n padding: 20px;\n text-align: center;\n color: #666666;\n font-size: 13px;\n }\n \n /* Mobile responsiveness */\n @media only screen and (max-width: 600px) {\n .email-container {\n width: 100% !important;\n max-width: 100% !important;\n }\n \n .header h1 {\n font-size: 24px !important;\n }\n \n .stat-cell {\n width: 50% !important;\n display: block !important;\n }\n \n .data-table th,\n .data-table td {\n padding: 8px 5px !important;\n font-size: 12px !important;\n }\n }\n </style>\n</head>\n<body>\n <!-- Main container table -->\n <table role=\"presentation\" cellspacing=\"0\" cellpadding=\"0\" border=\"0\" width=\"100%\">\n <tr>\n <td align=\"center\" style=\"padding: 20px 0; background-color: #f5f7fa;\">\n \n <!-- Email container -->\n <table role=\"presentation\" class=\"email-container\" cellspacing=\"0\" cellpadding=\"0\" border=\"0\" width=\"650\" style=\"max-width: 650px;\">\n \n <!-- Header -->\n <tr>\n <td class=\"header\">\n <h1>\ud83d\udcca LinkedIn Trends Report</h1>\n <p class=\"header-subtitle\">Professional insights in structured format</p>\n <p class=\"header-time\">${currentDate} \u2022 Generated at ${currentTime}</p>\n </td>\n </tr>\n \n <!-- Stats Section -->\n <tr>\n <td class=\"stats-section\">\n <table role=\"presentation\" class=\"stats-table\" cellspacing=\"0\" cellpadding=\"0\" border=\"0\" width=\"100%\">\n <tr>\n <td class=\"stat-cell\">\n <span class=\"stat-number\">${trendingTopics.length}</span>\n <p class=\"stat-label\">Trending Topics</p>\n </td>\n <td class=\"stat-cell\">\n <span class=\"stat-number\">${Math.round(trendingTopics.reduce((acc, topic) => acc + calculateEngagementScore(topic), 0) / trendingTopics.length)}%</span>\n <p class=\"stat-label\">Avg Engagement</p>\n </td>\n <td class=\"stat-cell\">\n <span class=\"stat-number\">${trendingTopics.length * 10}+</span>\n <p class=\"stat-label\">Hashtags</p>\n </td>\n <td class=\"stat-cell\">\n <span class=\"stat-number\">100%</span>\n <p class=\"stat-label\">Ready to Use</p>\n </td>\n </tr>\n </table>\n </td>\n </tr>\n \n <!-- Content Section -->\n <tr>\n <td class=\"content-section\">\n <table role=\"presentation\" class=\"data-table\" cellspacing=\"0\" cellpadding=\"0\" border=\"0\" width=\"100%\">\n <thead>\n <tr>\n <th width=\"8%\">Rank</th>\n <th width=\"25%\">Topic</th>\n <th width=\"35%\">Description</th>\n <th width=\"32%\">Hashtags</th>\n </tr>\n </thead>\n <tbody>\n ${trendingTopics.map((topic, index) => {\n const hashtags = generateHashtags(topic.title, topic.description);\n const engagementScore = calculateEngagementScore(topic);\n \n return `\n <tr>\n <td align=\"center\">\n <span class=\"topic-rank\">#${index + 1}</span>\n </td>\n <td>\n <p class=\"topic-title\">${topic.title}</p>\n </td>\n <td>\n ${topic.description}\n <div class=\"engagement-score\">${engagementScore}% Engagement</div>\n </td>\n <td>\n <div class=\"hashtags\">${hashtags}</div>\n </td>\n </tr>\n `;\n }).join('')}\n </tbody>\n </table>\n </td>\n </tr>\n \n <!-- Footer -->\n <tr>\n <td class=\"footer\">\n <p>\ud83e\udd16 Automated LinkedIn Intelligence System \u2022 Generated on ${currentDate}</p>\n </td>\n </tr>\n \n </table>\n \n </td>\n </tr>\n </table>\n</body>\n</html>\n`;\n\n// Enhanced email subject with personalization\nconst emailSubject = `\ud83d\udcca ${trendingTopics.length} LinkedIn Trends - Structured Report ${currentDate}`;\n\n// Enhanced summary with engagement metrics\nconst topicsSummary = trendingTopics.map((topic, index) => \n `${index + 1}. ${topic.title} (${calculateEngagementScore(topic)}% engagement)`\n).join('\\n');\n\n// Return comprehensive data package\nreturn [{\n json: {\n email_html: htmlReport,\n email_subject: emailSubject,\n topics_summary: topicsSummary,\n topics_count: trendingTopics.length,\n generation_date: new Date().toISOString(),\n average_engagement_score: Math.round(trendingTopics.reduce((acc, topic) => acc + calculateEngagementScore(topic), 0) / trendingTopics.length),\n total_hashtags: trendingTopics.length * 10,\n trending_topics: trendingTopics.map((topic, index) => ({\n ...topic,\n engagement_score: calculateEngagementScore(topic),\n rank: index + 1,\n hashtags: generateHashtags(topic.title, topic.description),\n content_suggestion: topic.description.length > 200 ? topic.description.substring(0, 200) + '...' : topic.description\n }))\n }\n}];"
},
"typeVersion": 2
},
{
"id": "adab755d-3887-4773-9fdb-d772506fef71",
"name": "Basic LLM Chain",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
208,
-240
],
"parameters": {
"text": "=Fetch the latest trending topics from LinkedIn along with a short, relevant description for each topic. Return the results as an array of objects, where each object contains a `title` and `description`. Ensure the topics are current, professional, and suitable for a business or corporate audience.\n",
"batching": {},
"messages": {
"messageValues": [
{
"message": "You are an AI assistant integrated into an automation workflow designed to extract trending professional topics from LinkedIn. Your task is to return a list of 3\u20135 currently trending LinkedIn topics, each with a brief but informative description (1\u20132 sentences). These results will be used in a later step to generate a formatted newsletter email for professionals. Ensure the language is clear, concise, and professional."
}
]
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "3f0985b6-926d-4cdf-ade6-653cec0c5b50",
"name": "Azure OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatAzureOpenAi",
"position": [
336,
-16
],
"parameters": {
"model": "gpt-4o-mini",
"options": {}
},
"credentials": {
"azureOpenAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "0d3852aa-ace4-49bb-80ff-b30734ed63af",
"name": "When clicking \u2018Execute workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-64,
-80
],
"parameters": {},
"typeVersion": 1
},
{
"id": "c3f5df3f-80a2-4c4b-be02-a513c636e98d",
"name": "Send Daily Report Email",
"type": "n8n-nodes-base.gmail",
"position": [
864,
-128
],
"parameters": {
"toList": [
"user@example.com"
],
"message": "Linkedin Report",
"subject": "=LinkedIn Report",
"resource": "message",
"htmlMessage": "={{ $json.email_html }}",
"includeHtml": true,
"additionalFields": {}
},
"credentials": {
"gmailOAuth2": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "ed694b5e-7459-44bb-ac07-cf932155470d",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
592,
-400
],
"parameters": {
"height": 256,
"content": "Transforms AI-generated topics into professional HTML email reports.\nFeatures: Dynamic hashtag generation, engagement scoring, visual styling.\nCreates: Ready-to-post content snippets, strategic hashtags, professional layouts.\nOutput: Complete HTML email with embedded analytics and user-friendly design."
},
"typeVersion": 1
},
{
"id": "a09844ee-68e7-4432-b687-86fce5fe7531",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
64,
-512
],
"parameters": {
"color": 6,
"height": 240,
"content": "Processes current market data to identify trending LinkedIn topics.\n\nAnalyzes engagement patterns, content themes, and professional discussions.\nOutputs: Structured JSON with topic titles, descriptions, and relevance scores.\n\nModel: Optimized for business/professional content analysis."
},
"typeVersion": 1
},
{
"id": "1329a8eb-038a-4734-9199-67901e8c97b4",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
944,
-368
],
"parameters": {
"color": 3,
"height": 224,
"content": "Sends professionally formatted trending topics report via email.\nIncludes: Interactive content, copy-paste ready posts, engagement metrics.\nRecipients: Stakeholders, content teams, marketing professionals.\nTiming: Configurable for optimal delivery windows (morning preferred)."
},
"typeVersion": 1
},
{
"id": "895e14da-77f0-453e-92ae-3d28bd75aba4",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-256,
-288
],
"parameters": {
"color": 3,
"height": 208,
"content": "\nManually starts the LinkedIn trending topics analysis workflow.\n \nUse this to test the workflow or run ad-hoc reports.\n\nPerfect for initial setup and troubleshooting."
},
"typeVersion": 1
},
{
"id": "100757f0-63ec-4d48-abac-686169a9316e",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
144,
128
],
"parameters": {
"color": 2,
"height": 240,
"content": "Powers the LLM Chain with enterprise-grade AI processing.\nConfigured for: Professional content analysis, trend identification.\nHandles: Natural language processing, content categorization, sentiment analysis.\nEnsures consistent, high-quality topic extraction and insights."
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "d01ed6ff-a30e-4156-add5-025a40d51e8b",
"connections": {
"Basic LLM Chain": {
"main": [
[
{
"node": "Process and Identify Top Topics",
"type": "main",
"index": 0
}
]
]
},
"Azure OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Basic LLM Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Process and Identify Top Topics": {
"main": [
[
{
"node": "Send Daily Report Email",
"type": "main",
"index": 0
}
]
]
},
"When clicking \u2018Execute workflow\u2019": {
"main": [
[
{
"node": "Basic LLM Chain",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
azureOpenAiApigmailOAuth2
Pro
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About this workflow
Description
Source: https://n8n.io/workflows/7277/ — original creator credit. Request a take-down →
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