Generate and score LinkedIn posts from bullet points with Gemini and Google Sheets
n8n-workflow-20533.n8n.io
· n8n.io
Quick overview This workflow receives bullet points via a webhook, uses Google Gemini to generate and score three LinkedIn post variations, logs the results to Google Sheets, and returns an HTML page with all versions plus an automatically selected recommended post. How it works 1. Receives a POST request to a webhook with bulletpoints, tone, posttype, and an optional authorname. 2. Normalizes incoming fields and sets configuration values such as the target Google Sheets document ID and sheet name. 3. Uses Google Gemini to generate exactly three distinct LinkedIn post variations from the provided bullet points and options. 4. Uses Google Gemini again to score each variation on hook strength, readability, and engagement potential. 5. Totals the three scores per variation, selects the highest-scoring post as the recommendation, and prepares the variations and score breakdown for output. 6. Appends all three variations, their totals, the input metadata, and the recommended index to a Goog
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Conformance score: 60/100
Catalogue listing, not an A2A agent: a workflow template listed by n8n.io. Scored on the 2 criteria that apply (9 of 15 points, scaled to 100); the A2A card criteria do not apply.
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1 snapshot Every change to the catalogue entry| Captured | Hash | |
|---|---|---|
| 2026-10-10 07:40:25 current | ac4c8b292ebf… |
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| Agent card | https://n8n.io/workflows/20533 |
| Provider | https://n8n.io |
| Docs | https://n8n.io/workflows/20533 |
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"fileBuffer": "data:image/svg+xml;base64,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"
},
"displayName": "Webhook",
"typeVersion": 2,
"nodeCategories": [
{
"id": 5,
"name": "Development"
},
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 535,
"icon": "file:webhook.svg",
"name": "n8n-nodes-base.respondToWebhook",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.respondtowebhook/"
}
]
},
"categories": [
"Core Nodes",
"Utility"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Helpers"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Respond to Webhook"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,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"
},
"displayName": "Respond to Webhook",
"typeVersion": 2,
"nodeCategories": [
{
"id": 7,
"name": "Utility"
},
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 565,
"icon": "fa:sticky-note",
"name": "n8n-nodes-base.stickyNote",
"codex": {
"data": {
"alias": [
"Comments",
"Notes",
"Sticky"
],
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Helpers"
]
}
}
},
"group": "[\"input\"]",
"defaults": {
"name": "Sticky Note",
"color": "#FFD233"
},
"iconData": {
"icon": "sticky-note",
"type": "icon"
},
"displayName": "Sticky Note",
"typeVersion": 1,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 834,
"icon": "file:code.svg",
"name": "n8n-nodes-base.code",
"codex": {
"data": {
"alias": [
"cpde",
"Javascript",
"JS",
"Python",
"Script",
"Custom Code",
"Function"
],
"details": "The Code node allows you to execute JavaScript in your workflow.",
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/"
}
]
},
"categories": [
"Development",
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Helpers",
"Data Transformation"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Code"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iNTEyIiBoZWlnaHQ9IjUxMiIgdmlld0JveD0iMCAwIDUxMiA1MTIiIGZpbGw9Im5vbmUiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+CjxnIGNsaXAtcGF0aD0idXJsKCNjbGlwMF8xMTcxXzQ0MSkiPgo8cGF0aCBkPSJNMTcwLjI4MyA0OEgxOTYuNUMyMDMuMTI3IDQ4IDIwOC41IDQyLjYyNzQgMjA4LjUgMzZWMTJDMjA4LjUgNS4zNzI1OCAyMDMuMTI3IDAgMTk2LjUgMEgxNzAuMjgzQzEyNi4xIDAgOTAuMjgzIDM1LjgxNzIgOTAuMjgzIDgwVjE3NkM5MC4yODMgMjA2LjkyOCA2NS4yMTA5IDIzMiAzNC4yODMgMjMySDIzQzE2LjM3MjYgMjMyIDExIDIzNy4zNzIgMTEgMjQ0VjI2OEMxMSAyNzQuNjI3IDE2LjM3MjQgMjgwIDIyLjk5OTYgMjgwTDM0LjI4MyAyODBDNjUuMjEwOSAyODAgOTAuMjgzIDMwNS4wNzIgOTAuMjgzIDMzNlY0NDBDOTAuMjgzIDQ3OS43NjQgMTIyLjUxOCA1MTIgMTYyLjI4MyA1MTJIMTk2LjVDMjAzLjEyNyA1MTIgMjA4LjUgNTA2LjYyNyAyMDguNSA1MDBWNDc2QzIwOC41IDQ2OS4zNzMgMjAzLjEyNyA0NjQgMTk2LjUgNDY0SDE2Mi4yODNDMTQ5LjAyOCA0NjQgMTM4LjI4MyA0NTMuMjU1IDEzOC4yODMgNDQwVjMzNkMxMzguMjgzIDMwOS4wMjIgMTI4LjAxMSAyODQuNDQzIDExMS4xNjQgMjY1Ljk2MUMxMDYuMTA5IDI2MC40MTYgMTA2LjEwOSAyNTEuNTg0IDExMS4xNjQgMjQ2LjAzOUMxMjguMDExIDIyNy41NTcgMTM4LjI4MyAyMDIuOTc4IDEzOC4yODMgMTc2VjgwQzEzOC4yODMgNjIuMzI2OSAxNTIuNjEgNDggMTcwLjI4MyA0OFoiIGZpbGw9IiNGRjk5MjIiLz4KPHBhdGggZD0iTTMwNSAzNkMzMDUgNDIuNjI3NCAzMTAuMzczIDQ4IDMxNyA0OEgzNDIuOTc5QzM2MC42NTIgNDggMzc0Ljk3OCA2Mi4zMjY5IDM3NC45NzggODBWMTc2QzM3NC45NzggMjAyLjk3OCAzODUuMjUxIDIyNy41NTcgNDAyLjA5OCAyNDYuMDM5QzQwNy4xNTMgMjUxLjU4NCA0MDcuMTUzIDI2MC40MTYgNDAyLjA5OCAyNjUuOTYxQzM4NS4yNTEgMjg0LjQ0MyAzNzQuOTc4IDMwOS4wMjIgMzc0Ljk3OCAzMzZWNDMyQzM3NC45NzggNDQ5LjY3MyAzNjAuNjUyIDQ2NCAzNDIuOTc5IDQ2NEgzMTdDMzEwLjM3MyA0NjQgMzA1IDQ2OS4zNzMgMzA1IDQ3NlY1MDBDMzA1IDUwNi42MjcgMzEwLjM3MyA1MTIgMzE3IDUxMkgzNDIuOTc5QzM4Ny4xNjEgNTEyIDQyMi45NzggNDc2LjE4MyA0MjIuOTc4IDQzMlYzMzZDNDIyLjk3OCAzMDUuMDcyIDQ0OC4wNTEgMjgwIDQ3OC45NzkgMjgwSDQ5MEM0OTYuNjI3IDI4MCA1MDIgMjc0LjYyOCA1MDIgMjY4VjI0NEM1MDIgMjM3LjM3MyA0OTYuNjI4IDIzMiA0OTAgMjMyTDQ3OC45NzkgMjMyQzQ0OC4wNTEgMjMyIDQyMi45NzggMjA2LjkyOCA0MjIuOTc4IDE3NlY4MEM0MjIuOTc4IDM1LjgxNzIgMzg3LjE2MSAwIDM0Mi45NzkgMEgzMTdDMzEwLjM3MyAwIDMwNSA1LjM3MjU4IDMwNSAxMlYzNloiIGZpbGw9IiNGRjk5MjIiLz4KPC9nPgo8ZGVmcz4KPGNsaXBQYXRoIGlkPSJjbGlwMF8xMTcxXzQ0MSI+CjxyZWN0IHdpZHRoPSI1MTIiIGhlaWdodD0iNTEyIiBmaWxsPSJ3aGl0ZSIvPgo8L2NsaXBQYXRoPgo8L2RlZnM+Cjwvc3ZnPgo="
},
"displayName": "Code",
"typeVersion": 2,
"nodeCategories": [
{
"id": 5,
"name": "Development"
},
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 1123,
"icon": "fa:link",
"name": "@n8n/n8n-nodes-langchain.chainLlm",
"codex": {
"data": {
"alias": [
"LangChain"
],
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Chains",
"Root Nodes"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Basic LLM Chain",
"color": "#909298"
},
"iconData": {
"icon": "link",
"type": "icon"
},
"displayName": "Basic LLM Chain",
"typeVersion": 2,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1179,
"icon": "fa:code",
"name": "@n8n/n8n-nodes-langchain.outputParserStructured",
"codex": {
"data": {
"alias": [
"json",
"zod"
],
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.outputparserstructured/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Output Parsers"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Structured Output Parser"
},
"iconData": {
"icon": "code",
"type": "icon"
},
"displayName": "Structured Output Parser",
"typeVersion": 1,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1262,
"icon": "file:google.svg",
"name": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatgooglegemini/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Language Models",
"Root Nodes"
],
"Language Models": [
"Chat Models (Recommended)"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Google Gemini Chat Model"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,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"
},
"displayName": "Google Gemini Chat Model",
"typeVersion": 1,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
}
]
},
"detail": {
"description": "Quick overview This workflow receives bullet points via a webhook, uses Google Gemini to generate and score three LinkedIn post variations, logs the results to Google Sheets, and returns an HTML page with all versions plus an automatically selected recommended post. How it works 1. Receives a POST request to a webhook with bulletpoints, tone, posttype, and an optional authorname. 2. Normalizes incoming fields and sets configuration values such as the target Google Sheets document ID and sheet name. 3. Uses Google Gemini to generate exactly three distinct LinkedIn post variations from the provided bullet points and options. 4. Uses Google Gemini again to score each variation on hook strength, readability, and engagement potential. 5. Totals the three scores per variation, selects the highest-scoring post as the recommendation, and prepares the variations and score breakdown for output. 6. Appends all three variations, their totals, the input metadata, and the recommended index to a Google Sheets worksheet. 7. Returns a formatted HTML response showing the recommended post and all three variations with their scores. Setup 1. Add a Google Gemini (PaLM) API credential and attach it to both Gemini steps (generation and scoring). 2. Add a Google Sheets OAuth2 credential. 3. Create a Google Sheet with a worksheet named \u201cPosts\u201d (or update the sheet name) and add headers: generatedat, bulletpoints, tone, posttype, variation1, score1, variation2, score2, variation3, score3, recommendedindex. 4. Replace REPLACEWITHGOOGLESHEETID in the configuration step with your Google Sheet document ID. 5. Activate the workflow, copy the webhook URL, and send JSON requests to the /linkedin-generator endpoint from your form or client."
}
}