Mychel Garzon This workflow corresponds to n8n.io template #15897 — we link there as the canonical source.
This workflow follows the Emailsend → Google Sheets recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "7AinkHj2WNEeixb0",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Social analytics",
"tags": [],
"nodes": [
{
"id": "0222e36c-a40a-4f64-b370-242ee629a445",
"name": "Every Monday 9 AM",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
3984,
2864
],
"parameters": {
"rule": {
"interval": [
{
"field": "weeks",
"triggerAtDay": 1,
"triggerAtHour": 9
}
]
}
},
"typeVersion": 1.1
},
{
"id": "75fdb746-6543-4289-8c4b-c63b8d770f51",
"name": "WES Config",
"type": "n8n-nodes-base.set",
"position": [
4208,
2864
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "w1",
"name": "weight_like",
"type": "number",
"value": 1
},
{
"id": "w2",
"name": "weight_comment",
"type": "number",
"value": 3
},
{
"id": "w3",
"name": "weight_share",
"type": "number",
"value": 5
},
{
"id": "w4",
"name": "weight_save",
"type": "number",
"value": 4
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d3645c22-520c-4200-8b7f-97cb7412772c",
"name": "Get LinkedIn Posts",
"type": "n8n-nodes-base.httpRequest",
"position": [
4528,
2608
],
"settings": {
"alwaysOutputData": true
},
"parameters": {
"url": "=https://api.linkedin.com/v2/ugcPosts?q=authors&authors=List({{$env.LINKEDIN_OWNER_URN}})&count=100",
"options": {},
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "LinkedIn-Version",
"value": "202304"
}
]
},
"nodeCredentialType": "linkedInOAuth2Api"
},
"typeVersion": 4.2,
"continueOnFail": true
},
{
"id": "ecd61a8b-c77f-4cc1-b31a-c5fc92656b91",
"name": "Search Tweets",
"type": "n8n-nodes-base.twitter",
"position": [
4528,
2848
],
"settings": {
"alwaysOutputData": true
},
"parameters": {
"limit": 100,
"operation": "search",
"searchText": "={{$env.TWITTER_SEARCH_QUERY}}",
"additionalFields": {
"startTime": "={{$now.minus({days: 7}).toISO()}}"
}
},
"typeVersion": 2,
"continueOnFail": true
},
{
"id": "d8261e5c-934a-4761-ac33-52cb6df0ee46",
"name": "Get media",
"type": "@mookielianhd/n8n-nodes-instagram.instagram",
"position": [
4528,
3040
],
"settings": {
"alwaysOutputData": true
},
"parameters": {
"limit": 100,
"resource": "igUser",
"operation": "getMedia",
"graphApiVersion": "v24.0"
},
"typeVersion": 1,
"continueOnFail": true
},
{
"id": "2caeebbd-37fa-492f-9fcb-09c871782899",
"name": "Merge LI & TW",
"type": "n8n-nodes-base.merge",
"position": [
4800,
2720
],
"parameters": {},
"typeVersion": 2.1
},
{
"id": "0454934a-3f2b-427c-b889-d2ce0fc4531f",
"name": "Merge All Streams",
"type": "n8n-nodes-base.merge",
"position": [
5024,
2848
],
"parameters": {},
"typeVersion": 2.1
},
{
"id": "44a305b6-b7a4-445d-bf48-5e452942e15f",
"name": "Normalize & Validate Posts",
"type": "n8n-nodes-base.code",
"position": [
5328,
2752
],
"parameters": {
"jsCode": "// Weights from WES Config node\nconst weights = $('WES Config').first().json;\nconst wLike = weights.weight_like ?? 1;\nconst wComment = weights.weight_comment ?? 3;\nconst wShare = weights.weight_share ?? 5;\nconst wSave = weights.weight_save ?? 4;\n\n// Use native JS Date.now() \u2014 safe in all n8n hosting environments\nconst weekAgo = Date.now() - (7 * 24 * 60 * 60 * 1000);\n\nreturn $input.all().reduce((acc, item) => {\n const d = item.json;\n if (!d || Object.keys(d).length === 0) return acc;\n\n // Platform detection\n const isLinkedIn = d.firstPublishedAt !== undefined || d.specificContent !== undefined;\n const isInstagram = d.permalink !== undefined && d.timestamp !== undefined;\n const isTwitter = d.public_metrics !== undefined;\n\n // Direct timestamp extraction \u2014 no intermediate ISO string conversion\n let ts = null;\n if (d.firstPublishedAt) ts = new Date(d.firstPublishedAt).getTime();\n else if (d.publishedDate || d.created_at || d.timestamp) ts = new Date(d.publishedDate || d.created_at || d.timestamp).getTime();\n\n if (!ts || isNaN(ts) || ts < weekAgo) return acc;\n\n const platform = d.platform\n || (isLinkedIn ? 'linkedin'\n : isInstagram ? 'instagram'\n : isTwitter ? 'twitter'\n : 'unknown');\n\n const pm = d.public_metrics || {};\n const likes = pm.like_count || d.like_count || d.likes || d.likeCount || 0;\n const comments = pm.reply_count || d.comments_count || d.comments || d.reply_count || 0;\n const shares = pm.retweet_count || d.shares || d.retweet_count || d.shareCount || 0;\n const saves = pm.bookmark_count || d.saves || d.bookmark_count || 0;\n const impressions = pm.impression_count\n || d.impressions || d.view_count || d.reach\n || d.statistics?.totalShareStatistics?.impressionCount\n || null;\n\n const liText = d.specificContent?.['com.linkedin.ugc.ShareContent']?.shareCommentary?.text || '';\n const rawText = liText || d.text || d.caption || '';\n\n const twitterUrl = isTwitter && d.id\n ? 'https://twitter.com/i/web/status/' + d.id\n : null;\n const url = d.url || d.permalink || twitterUrl || '#';\n\n acc.push({\n json: {\n _platform: platform,\n _date: new Date(ts).toISOString(),\n _likes: likes,\n _comments: comments,\n _shares: shares,\n _saves: saves,\n _impressions: impressions,\n _text: rawText.trim(),\n _url: url,\n _wLike: wLike,\n _wComment: wComment,\n _wShare: wShare,\n _wSave: wSave\n }\n });\n\n return acc;\n}, []);"
},
"typeVersion": 2
},
{
"id": "ab264826-54f3-42c9-9cd9-a95d2f31756e",
"name": "Loop Over Batches",
"type": "n8n-nodes-base.splitInBatches",
"position": [
5680,
2560
],
"parameters": {
"options": {},
"batchSize": 50
},
"typeVersion": 3
},
{
"id": "58ea49ce-4ebc-43c3-a959-42b6a060c906",
"name": "Log Raw Posts to Sheets",
"type": "n8n-nodes-base.googleSheets",
"position": [
5856,
2704
],
"parameters": {
"columns": {
"value": {
"URL": "={{$json._url}}",
"Date": "={{$json._date}}",
"Content": "={{$json._text.slice(0, 100)}}",
"Platform": "={{$json._platform}}",
"Engagement": "={{$json._likes + $json._comments + $json._shares + $json._saves}}",
"Impressions": "={{$json._impressions ?? 'unknown'}}"
},
"mappingMode": "defineBelow"
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "name",
"value": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "id",
"value": "={{$env.ANALYTICS_SPREADSHEET_ID}}"
}
},
"typeVersion": 4.4,
"continueOnFail": true
},
{
"id": "01fbedd7-b27d-457a-937f-b8d9d392bb7d",
"name": "Capture Sheet Errors",
"type": "n8n-nodes-base.code",
"position": [
6192,
2720
],
"parameters": {
"jsCode": "const errors = $input.all()\n .filter(i => i.json.error)\n .map(i => i.json.error?.message || 'Unknown error writing to Sheets');\n\nreturn [{ json: { sheetErrors: errors, sheetErrorCount: errors.length } }];"
},
"typeVersion": 2
},
{
"id": "59b495a0-ba79-4232-b76f-f82d31423f32",
"name": "Calculate Analytics (WES)",
"type": "n8n-nodes-base.code",
"position": [
5664,
3104
],
"parameters": {
"jsCode": "// Use native JS Date.now() \u2014 safe in all n8n hosting environments\nconst items = $input.all();\nlet totalImpressions = 0;\nlet totalWesSum = 0;\nlet postsWithImpressions = 0;\nlet postsWithoutImpressions = 0;\nlet bestPost = null;\nlet worstPost = null;\nconst platformStats = {};\n\nitems.forEach(item => {\n const d = item.json;\n if (!d || Object.keys(d).length === 0) return;\n\n const wLike = d._wLike ?? 1;\n const wComment = d._wComment ?? 3;\n const wShare = d._wShare ?? 5;\n const wSave = d._wSave ?? 4;\n\n const platform = d._platform;\n const likes = d._likes || 0;\n const comments = d._comments || 0;\n const shares = d._shares || 0;\n const saves = d._saves || 0;\n const impressions = d._impressions;\n const wes = (wLike * likes) + (wComment * comments) + (wShare * shares) + (wSave * saves);\n\n if (!platformStats[platform]) {\n platformStats[platform] = { posts: 0, impressions: 0, wesSum: 0, postsWithImpressions: 0 };\n }\n platformStats[platform].posts++;\n\n if (impressions !== null && impressions > 0) {\n const rate = (wes / impressions) * 100;\n platformStats[platform].impressions += impressions;\n platformStats[platform].wesSum += wes;\n platformStats[platform].postsWithImpressions++;\n totalImpressions += impressions;\n totalWesSum += wes;\n postsWithImpressions++;\n\n const postPayload = { platform, text: d._text || 'Media post', rate, url: d._url };\n if (!bestPost || rate > bestPost.rate) bestPost = postPayload;\n if (!worstPost || rate < worstPost.rate) worstPost = postPayload;\n } else {\n postsWithoutImpressions++;\n }\n});\n\nconst platformRankings = Object.entries(platformStats)\n .map(([name, s]) => ({\n name,\n avgEngagementRate: s.postsWithImpressions > 0\n ? ((s.wesSum / s.impressions) * 100).toFixed(2)\n : 'N/A',\n totalPosts: s.posts,\n totalImpressions: s.impressions\n }))\n .sort((a, b) => parseFloat(b.avgEngagementRate) - parseFloat(a.avgEngagementRate));\n\n// Native JS date math \u2014 no Luxon dependency\nconst now = new Date();\nconst weekAgo = new Date(now.getTime() - (7 * 24 * 60 * 60 * 1000));\nconst toISODate = d => d.toISOString().slice(0, 10);\n\nreturn [{\n json: {\n weekStart: toISODate(weekAgo),\n weekEnd: toISODate(now),\n totalPosts: items.length,\n postsWithImpressions,\n postsWithoutImpressions,\n totalImpressions,\n avgEngagementRate: postsWithImpressions > 0\n ? ((totalWesSum / totalImpressions) * 100).toFixed(2)\n : 'N/A',\n platformRankings,\n bestPost,\n worstPost\n }\n}];"
},
"typeVersion": 2
},
{
"id": "e39147ed-6397-42f3-a815-7dba43dc7ef1",
"name": "AI Strategy Insights",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
6000,
3104
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "gpt-4o"
},
"options": {
"temperature": 0.7
},
"messages": {
"values": [
{
"content": "=Based on this week's content performance data, provide 3 actionable recommendations for improving our social media strategy.\n\n**Overall Stats:**\n- Total Posts: {{$json.totalPosts}} ({{$json.postsWithImpressions}} with impression data, {{$json.postsWithoutImpressions}} without)\n- Avg Engagement Rate (WES): {{$json.avgEngagementRate}}%\n\n**Platform Rankings:**\n{{$json.platformRankings.map(p => `${p.name}: ${p.avgEngagementRate}% (${p.totalPosts} posts, ${p.totalImpressions} impressions)`).join('\\n')}}\n\n**Best Performing Post ({{$json.bestPost ? $json.bestPost.rate.toFixed(2) : 'N/A'}}%):**\n{{$json.bestPost ? $json.bestPost.text : 'No posts with impression data this week'}}\n\n**Worst Performing Post ({{$json.worstPost ? $json.worstPost.rate.toFixed(2) : 'N/A'}}%):**\n{{$json.worstPost ? $json.worstPost.text : 'No posts with impression data this week'}}\n\nCRITICAL OUTPUT FORMATTING:\nReturn your complete response in clean, semantic HTML format.\nUse ONLY <h3> for section titles, <p> for body text, <strong> for emphasis, and <ul>/<li> for action items.\nDo NOT wrap the response in markdown code blocks. Begin output directly with the first HTML tag."
}
]
}
},
"typeVersion": 1.1,
"continueOnFail": true
},
{
"id": "05b5400f-9a6b-4dc1-984c-0b66afd31fea",
"name": "Wait for All Branches",
"type": "n8n-nodes-base.merge",
"position": [
6464,
2656
],
"parameters": {
"mode": "combine",
"options": {},
"combinationMode": "mergeByPosition"
},
"typeVersion": 2.1
},
{
"id": "0faf1bf2-e313-436c-b5a1-fb5709b88242",
"name": "Combine Data & AI Output",
"type": "n8n-nodes-base.set",
"position": [
6720,
2656
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1",
"name": "weekStart",
"type": "string",
"value": "={{ $('Calculate Analytics (WES)').first().json.weekStart }}"
},
{
"id": "2",
"name": "weekEnd",
"type": "string",
"value": "={{ $('Calculate Analytics (WES)').first().json.weekEnd }}"
},
{
"id": "3",
"name": "totalPosts",
"type": "number",
"value": "={{ $('Calculate Analytics (WES)').first().json.totalPosts }}"
},
{
"id": "4",
"name": "postsWithImpressions",
"type": "number",
"value": "={{ $('Calculate Analytics (WES)').first().json.postsWithImpressions }}"
},
{
"id": "5",
"name": "postsWithoutImpressions",
"type": "number",
"value": "={{ $('Calculate Analytics (WES)').first().json.postsWithoutImpressions }}"
},
{
"id": "6",
"name": "totalImpressions",
"type": "number",
"value": "={{ $('Calculate Analytics (WES)').first().json.totalImpressions }}"
},
{
"id": "7",
"name": "avgEngagementRate",
"type": "string",
"value": "={{ $('Calculate Analytics (WES)').first().json.avgEngagementRate }}"
},
{
"id": "8",
"name": "sheetErrorCount",
"type": "number",
"value": "={{ $('Capture Sheet Errors').all(0, 0).reduce((total, item) => total + (item.json.sheetErrorCount || 0), 0) }}"
},
{
"id": "9",
"name": "sheetErrors",
"type": "array",
"value": "={{ $('Capture Sheet Errors').all(0, 0).flatMap(item => item.json.sheetErrors || []) }}"
},
{
"id": "10",
"name": "aiInsights",
"type": "string",
"value": "={{ $('AI Strategy Insights').first().json.error ? '<p><em>AI insights unavailable this week: ' + $('AI Strategy Insights').first().json.error.message + '</em></p>' : ($('AI Strategy Insights').first().json.message.content || '').replace(/```html|```/g, '') }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "dcda36dd-3c67-4350-97f5-f4da2ae5d2dd",
"name": "Email Weekly Report",
"type": "n8n-nodes-base.emailSend",
"position": [
6928,
2656
],
"parameters": {
"html": "=<html>\n<body style=\"font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto;\">\n <h1>\ud83d\udcca Weekly Content Performance Report</h1>\n <p><strong>Period:</strong> {{$json.weekStart}} to {{$json.weekEnd}}</p>\n ={{ $json.sheetErrorCount > 0 ? '<div style=\"background:#fff3cd;border:1px solid #ffc107;padding:12px;border-radius:4px;margin-bottom:16px;\"><strong>\u26a0\ufe0f Warning:</strong> ' + $json.sheetErrorCount + ' post(s) failed to log to Google Sheets.<br>Errors: ' + ($json.sheetErrors || []).join(', ') + '</div>' : '' }}\n <h2>Key Metrics</h2>\n <table style=\"border-collapse: collapse; width: 100%;\">\n <tr style=\"background-color: #f0f0f0;\">\n <td style=\"padding: 10px; border: 1px solid #ddd;\"><strong>Total Posts</strong></td>\n <td style=\"padding: 10px; border: 1px solid #ddd;\">{{$json.totalPosts}}</td>\n </tr>\n <tr>\n <td style=\"padding: 10px; border: 1px solid #ddd;\"><strong>Posts with impression data</strong></td>\n <td style=\"padding: 10px; border: 1px solid #ddd;\">{{$json.postsWithImpressions}} / {{$json.totalPosts}}</td>\n </tr>\n <tr style=\"background-color: #f0f0f0;\">\n <td style=\"padding: 10px; border: 1px solid #ddd;\"><strong>Total Impressions</strong></td>\n <td style=\"padding: 10px; border: 1px solid #ddd;\">{{Number($json.totalImpressions).toLocaleString()}}</td>\n </tr>\n <tr>\n <td style=\"padding: 10px; border: 1px solid #ddd;\"><strong>Avg Engagement Rate (WES)</strong></td>\n <td style=\"padding: 10px; border: 1px solid #ddd;\">{{$json.avgEngagementRate}}%</td>\n </tr>\n </table>\n <h2>\ud83d\udca1 AI Strategy Recommendations</h2>\n <div style=\"background: #fff3e0; padding: 15px; border-radius: 5px;\">\n {{$json.aiInsights}}\n </div>\n</body>\n</html>",
"options": {},
"subject": "=Weekly Content Performance Report ({{$json.weekStart}} - {{$json.weekEnd}}){{$json.sheetErrorCount > 0 ? ' \u26a0\ufe0f Sheet errors' : ''}}",
"toEmail": "user@example.com",
"fromEmail": "user@example.com"
},
"typeVersion": 2.1
},
{
"id": "b886cc87-5743-47bf-8568-5e63e044bbc9",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
4416,
2480
],
"parameters": {
"color": 7,
"width": 784,
"height": 784,
"content": "### Multi-Platform Data Fetch\nPulls the last 7 days of posts from LinkedIn, X (Twitter), and Instagram in\nparallel, merges LinkedIn and X first, then combines all three streams into\na single unified feed for downstream processing."
},
"typeVersion": 1
},
{
"id": "d5fce582-efcd-4085-891f-07a209ccb200",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
5232,
2576
],
"parameters": {
"color": 7,
"width": 304,
"height": 480,
"content": "### Normalization & Validation\nDetects the platform of each post, extracts engagement metrics into a unified schema, filters out posts older than 7 days, and drops any empty items before passing clean data to both downstream branches."
},
"typeVersion": 1
},
{
"id": "ff20cbd7-5a33-4ba2-b035-474fdd6ab4e2",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
3840,
2720
],
"parameters": {
"color": 7,
"width": 544,
"height": 368,
"content": "### Trigger & Weight Config\nFires every Monday at 9 AM and sets the engagement weights used across the entire pipeline: likes (1), comments (3), shares (5), saves (4)."
},
"typeVersion": 1
},
{
"id": "34c00412-a56f-42ad-92ca-6973a4fb02ea",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
5568,
2944
],
"parameters": {
"color": 7,
"width": 768,
"height": 384,
"content": "### WES Calculation & AI Insights\nCalculates the Weighted Engagement Score for each post and aggregates platform rankings, best and worst performers, and overall stats. GPT-4o then generates 3 actionable strategy recommendations based on the week's performance data."
},
"typeVersion": 1
},
{
"id": "ffbbd2b6-858a-4c20-8a42-b9756199bd43",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
5568,
2416
],
"parameters": {
"color": 7,
"width": 768,
"height": 496,
"content": "### Raw Post Logging\nIterates over normalized posts in batches of 50 and appends each one to a Google Sheets log with date, platform, content preview, impressions, engagement score, and URL. Sheet write errors are captured without crashing the loop."
},
"typeVersion": 1
},
{
"id": "193530a4-e194-4a4b-b45e-fa418995de6a",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
6384,
2416
],
"parameters": {
"color": 7,
"width": 720,
"height": 496,
"content": "### Report Assembly & Delivery\nWaits for both the Sheets logging and AI insights branches to complete, merges all analytics data and AI output into a single payload, and sends a formatted HTML email report with key metrics, platform rankings, and strategy recommendations."
},
"typeVersion": 1
},
{
"id": "5d09c70d-a04c-4172-b92d-a509a0dca327",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
3024,
2416
],
"parameters": {
"width": 768,
"height": 912,
"content": "## Weekly Social Media Analytics: WES Scoring, AI Insights, and Automated Email Report\n\n### Cross-Platform Content Performance Reporting via LinkedIn, X, and Instagram\n\n### How it works\nThis workflow runs every Monday at 9 AM, pulls the last 7 days of posts from LinkedIn, X (Twitter), and Instagram, calculates a Weighted Engagement Score for each post, generates AI strategy recommendations with GPT-4o, logs raw data to Google Sheets, and delivers a formatted HTML report by email.\n\n### Setup steps\n- [ ] Set the LINKEDIN_OWNER_URN environment variable to your LinkedIn URN\n (format: urn:li:person:XXXXXXXX or urn:li:organization:XXXXXXXX).\n- [ ] Set the TWITTER_SEARCH_QUERY environment variable to the search query\n or handle you want to monitor (e.g. from:yourusername).\n- [ ] Set the ANALYTICS_SPREADSHEET_ID environment variable to your Google\n Sheets document ID where raw posts should be logged.\n- [ ] Connect your LinkedIn OAuth2 credential to the Get LinkedIn Posts node.\n- [ ] Connect your X (Twitter) credential to the Search Tweets node.\n- [ ] Connect your Instagram credential to the Get media node.\n- [ ] Connect your OpenAI credential to the AI Strategy Insights node.\n- [ ] Connect your Google Sheets credential to the Log Raw Posts to Sheets node.\n- [ ] Update the fromEmail and toEmail values in the Email Weekly Report node\n to match your sender and recipient addresses.\n- [ ] Adjust the engagement weights in the WES Config node to reflect your\n team's priorities. Default: likes 1, comments 3, shares 5, saves 4.\n\n### Required credentials\n- **LinkedIn OAuth2** (post fetch)\n- **X (Twitter) OAuth2** (tweet search)\n- **Instagram** (media fetch)\n- **OpenAI API** (strategy recommendations via GPT-4o)\n- **Google Sheets** (raw post logging)\n- **SMTP / email** (weekly report delivery)\n\n### Environment variables required\n`LINKEDIN_OWNER_URN` \u00b7 `TWITTER_SEARCH_QUERY` \u00b7 `ANALYTICS_SPREADSHEET_ID`\n\n### Notes\nWES (Weighted Engagement Score) is calculated as:\n`(likes \u00d7 1) + (comments \u00d7 3) + (shares \u00d7 5) + (saves \u00d7 4) / impressions \u00d7 100`\nPosts without impression data are excluded from the engagement rate calculation\nbut still counted in total post volume. Adjust weights in the WES Config node."
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"binaryMode": "separate",
"executionOrder": "v1"
},
"versionId": "bb8a05fb-fdcc-443e-a8af-88ee2aee37a5",
"connections": {
"Get media": {
"main": [
[
{
"node": "Merge All Streams",
"type": "main",
"index": 1
}
]
]
},
"WES Config": {
"main": [
[
{
"node": "Get LinkedIn Posts",
"type": "main",
"index": 0
},
{
"node": "Search Tweets",
"type": "main",
"index": 0
},
{
"node": "Get media",
"type": "main",
"index": 0
}
]
]
},
"Merge LI & TW": {
"main": [
[
{
"node": "Merge All Streams",
"type": "main",
"index": 0
}
]
]
},
"Search Tweets": {
"main": [
[
{
"node": "Merge LI & TW",
"type": "main",
"index": 1
}
]
]
},
"Every Monday 9 AM": {
"main": [
[
{
"node": "WES Config",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Batches": {
"main": [
[
{
"node": "Wait for All Branches",
"type": "main",
"index": 0
}
],
[
{
"node": "Log Raw Posts to Sheets",
"type": "main",
"index": 0
}
]
]
},
"Merge All Streams": {
"main": [
[
{
"node": "Normalize & Validate Posts",
"type": "main",
"index": 0
}
]
]
},
"Get LinkedIn Posts": {
"main": [
[
{
"node": "Merge LI & TW",
"type": "main",
"index": 0
}
]
]
},
"AI Strategy Insights": {
"main": [
[
{
"node": "Wait for All Branches",
"type": "main",
"index": 1
}
]
]
},
"Capture Sheet Errors": {
"main": [
[
{
"node": "Loop Over Batches",
"type": "main",
"index": 0
}
]
]
},
"Wait for All Branches": {
"main": [
[
{
"node": "Combine Data & AI Output",
"type": "main",
"index": 0
}
]
]
},
"Log Raw Posts to Sheets": {
"main": [
[
{
"node": "Capture Sheet Errors",
"type": "main",
"index": 0
}
]
]
},
"Combine Data & AI Output": {
"main": [
[
{
"node": "Email Weekly Report",
"type": "main",
"index": 0
}
]
]
},
"Calculate Analytics (WES)": {
"main": [
[
{
"node": "AI Strategy Insights",
"type": "main",
"index": 0
}
]
]
},
"Normalize & Validate Posts": {
"main": [
[
{
"node": "Loop Over Batches",
"type": "main",
"index": 0
},
{
"node": "Calculate Analytics (WES)",
"type": "main",
"index": 0
}
]
]
}
}
}
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About this workflow
This workflow runs every Monday, pulls the last 7 days of posts from LinkedIn, X (Twitter), and Instagram, normalizes engagement metrics, logs raw post data to Google Sheets, calculates a weighted engagement rate, generates strategy recommendations with OpenAI (GPT-4o), and…
Source: https://n8n.io/workflows/15897/ — original creator credit. Request a take-down →
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