Verwenden Sie AI und Apify, um LinkedIn-Post-Interaktionen in qualifizierte Leads umzuwandeln
Experte
Dies ist ein Lead Generation, AI Summarization-Bereich Automatisierungsworkflow mit 26 Nodes. Hauptsächlich werden If, Set, Code, Wait, Airtable und andere Nodes verwendet. Wandeln Sie LinkedIn-Post-Interaktionen mithilfe von KI und Apify in qualifizierte Leads um
Voraussetzungen
- •Airtable API Key
- •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
Verwendete Nodes (26)
Kategorie
Workflow-Vorschau
Visualisierung der Node-Verbindungen, mit Zoom und Pan
Workflow exportieren
Kopieren Sie die folgende JSON-Konfiguration und importieren Sie sie in n8n
{
"meta": {
"instanceId": "8d70623c0c9f4448eda9626cd8185192c28447e191325b0c0d94d3f40d23be3a"
},
"nodes": [
{
"id": "89ac2fe8-a5df-4150-b921-318e641b429c",
"name": "If3",
"type": "n8n-nodes-base.if",
"position": [
1740,
440
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "loose"
},
"combinator": "and",
"conditions": [
{
"id": "fe7ae37d-2d50-4308-b340-b3cfed20a2f0",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $json && Object.keys($json).length > 0 }}",
"rightValue": ""
}
]
},
"looseTypeValidation": true
},
"typeVersion": 2.2
},
{
"id": "2cc1a9bb-8b33-4af5-83ac-5af7906f778b",
"name": "Felder bearbeiten1",
"type": "n8n-nodes-base.set",
"position": [
1960,
440
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "a8672809-182b-4e13-96e4-7a2550c8f796",
"name": "linkedin_scraper_out",
"type": "string",
"value": "={{ $('Aggregate').item.json.fullName[0] }}\n{{ $('Aggregate').item.json.jobTitle[0] }}\n{{ $('Aggregate').item.json.companyName[0] }}\n{{ $('Aggregate').item.json.companyIndustry[0] }}\n{{ $('Aggregate').item.json.companyWebsite[0] }}\n{{ $('Aggregate').item.json.topSkillsByEndorsements[0] }}\n{{ $('Aggregate').item.json.about[0] }}\n{{ $('Aggregate').item.json.title[0] }}\n{{ $('Aggregate').item.json.description[0] }}\n{{ $('Aggregate').item.json.text[0] }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "198608d6-f63c-41d1-b612-161c208a8514",
"name": "Structured Output Parser1",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
2340,
660
],
"parameters": {
"jsonSchemaExample": "{\n \"icp\": \"true/false\",\n \"reasoning\": \"Brief explanation of classification decision based on the data analysis\"\n}"
},
"typeVersion": 1.2
},
{
"id": "f48f1abd-b702-451f-8e75-4aabe1e3515f",
"name": "Bei Klick auf 'Workflow testen'",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-1660,
120
],
"parameters": {},
"typeVersion": 1
},
{
"id": "1124ab40-bbe3-4202-a16f-61228dbdb728",
"name": "Aggregieren",
"type": "n8n-nodes-base.aggregate",
"position": [
1000,
440
],
"parameters": {
"options": {},
"fieldsToAggregate": {
"fieldToAggregate": [
{
"fieldToAggregate": "fullName"
},
{
"fieldToAggregate": "jobTitle"
},
{
"fieldToAggregate": "companyName"
},
{
"fieldToAggregate": "companyIndustry"
},
{
"fieldToAggregate": "companyWebsite"
},
{
"fieldToAggregate": "topSkillsByEndorsements"
},
{
"fieldToAggregate": "about"
},
{
"fieldToAggregate": "experiences[0].title"
},
{
"fieldToAggregate": "experiences[0].subComponents[0].description"
},
{
"fieldToAggregate": "experiences[1].title"
},
{
"fieldToAggregate": "experiences[1].subComponents[0].description[0].text"
}
]
}
},
"typeVersion": 1
},
{
"id": "25405123-3526-43b6-811e-afafb3796a39",
"name": "If",
"type": "n8n-nodes-base.if",
"position": [
-160,
140
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "b7097e6a-a156-41ad-b506-ebcb78a1538c",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $json && Object.keys($json).length > 0 }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "a22810dc-dee3-4e34-8f64-85e4026785fa",
"name": "No Operation, do nothing",
"type": "n8n-nodes-base.noOp",
"position": [
0,
0
],
"parameters": {},
"typeVersion": 1
},
{
"id": "cae1c603-11e7-4c83-bac1-008690c56a09",
"name": "Duplikate prüfen",
"type": "n8n-nodes-base.airtable",
"position": [
-380,
140
],
"parameters": {
"base": {
"__rl": true,
"mode": "id",
"value": ""
},
"table": {
"__rl": true,
"mode": "id",
"value": ""
},
"options": {},
"operation": "search",
"authentication": "airtableOAuth2Api",
"filterByFormula": "=({Name} = \"{{ $json.profile_name }}\")"
},
"typeVersion": 2.1,
"alwaysOutputData": true
},
{
"id": "fd30b54c-a01a-4db5-8556-7ca3605b47d4",
"name": "Daten bereinigen",
"type": "n8n-nodes-base.set",
"position": [
-640,
140
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "28682a76-7670-4af5-a3c8-b85d485dcad8",
"name": "profile_urn",
"type": "string",
"value": "={{ $json.reactor.urn }}"
},
{
"id": "7bc40d85-d383-4b27-a89b-dc8607adc40a",
"name": "profile_name",
"type": "string",
"value": "={{ $json.reactor.name }}"
},
{
"id": "ce4d0feb-c803-4c6e-a448-b3f6921593c4",
"name": "profile_job_title",
"type": "string",
"value": "={{ $json.reactor.headline }}"
},
{
"id": "735110e2-a403-4cf3-bac2-c2fa598b7c67",
"name": "profile_linkedin_url",
"type": "string",
"value": "={{ $json.reactor.profile_url }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "de1ccf48-08d0-4058-964c-c9e940546e42",
"name": "Über Elemente iterieren",
"type": "n8n-nodes-base.splitInBatches",
"position": [
-920,
120
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "c7676bae-f6c4-4746-a88f-ed877bb09d83",
"name": "Warte-Ratenlimit",
"type": "n8n-nodes-base.wait",
"position": [
-1140,
120
],
"webhookId": "1a90f5d4-c201-4866-9b46-fb99e1a0a798",
"parameters": {},
"typeVersion": 1.1
},
{
"id": "ea9be84c-663a-424b-9f82-49a1c0faad63",
"name": "Neuen Datensatz erstellen",
"type": "n8n-nodes-base.airtable",
"position": [
240,
140
],
"parameters": {
"base": {
"__rl": true,
"mode": "id",
"value": ""
},
"table": {
"__rl": true,
"mode": "id",
"value": ""
},
"columns": {
"value": {
"URN": "={{ $('Clean Data').item.json.profile_urn }}",
"Name": "={{ $('Clean Data').item.json.profile_name }}",
"Title": "={{ $('Clean Data').item.json.profile_job_title }}",
"Profile URL": "={{ $('Clean Data').item.json.profile_linkedin_url }}"
},
"schema": [
{
"id": "URN",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "URN",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Name",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Email Address",
"type": "string",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "Email Address",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Title",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Title",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Profile URL",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Profile URL",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "MSP?",
"type": "boolean",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "MSP?",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Reason",
"type": "string",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "Reason",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "create",
"authentication": "airtableOAuth2Api"
},
"typeVersion": 2.1
},
{
"id": "3766e2ce-7f5f-430a-ac6d-d31c1ef595db",
"name": "Zufällige Verzögerung",
"type": "n8n-nodes-base.code",
"position": [
460,
140
],
"parameters": {
"jsCode": "// Random Time Generator - Returns single random time object\n\n// Function to generate random seconds with decimal places\nfunction generateRandomSeconds(min = 0.1, max = 60, decimalPlaces = 1) {\n const randomValue = Math.random() * (max - min) + min;\n return Number(randomValue.toFixed(decimalPlaces));\n}\n\n// Generate single random time\nconst seconds = generateRandomSeconds(0.1, 60, 1);\n\n// Return array with single object (n8n requirement)\nreturn [{\n seconds: seconds,\n formatted: `${seconds} seconds`,\n milliseconds: seconds * 1000\n}];"
},
"typeVersion": 2
},
{
"id": "03fb46da-5027-4996-87d1-511b20860f34",
"name": "Zufällige Verzögerung warten",
"type": "n8n-nodes-base.wait",
"position": [
680,
140
],
"webhookId": "73d87027-2b93-4798-a496-05090671f858",
"parameters": {
"amount": "={{ $('Random Delay').item.json.seconds }}"
},
"typeVersion": 1.1
},
{
"id": "107aee62-4ced-47a6-8063-ac0f0ecc2e49",
"name": "Zufällige Verzögerung generieren",
"type": "n8n-nodes-base.code",
"position": [
1220,
440
],
"parameters": {
"jsCode": "// Random Time Generator - Returns single random time object\n\n// Function to generate random seconds with decimal places\nfunction generateRandomSeconds(min = 0.1, max = 60, decimalPlaces = 1) {\n const randomValue = Math.random() * (max - min) + min;\n return Number(randomValue.toFixed(decimalPlaces));\n}\n\n// Generate single random time\nconst seconds = generateRandomSeconds(0.1, 60, 1);\n\n// Return array with single object (n8n requirement)\nreturn [{\n seconds: seconds,\n formatted: `${seconds} seconds`,\n milliseconds: seconds * 1000\n}];\n"
},
"typeVersion": 2
},
{
"id": "139536cf-6039-48e7-a41f-21d98171bb33",
"name": "Zufällige Verzögerung Warte-Knoten",
"type": "n8n-nodes-base.wait",
"position": [
1440,
440
],
"webhookId": "e77b3d5b-d56f-4cba-8d79-60781a0f327e",
"parameters": {
"amount": "={{ $('Random Delay Generator').item.json.seconds }}"
},
"typeVersion": 1.1
},
{
"id": "490c6df0-e206-4673-ad2a-fbb2c50bb5ae",
"name": "Datensatz aktualisieren",
"type": "n8n-nodes-base.airtable",
"position": [
2560,
440
],
"parameters": {
"base": {
"__rl": true,
"mode": "id",
"value": ""
},
"table": {
"__rl": true,
"mode": "id",
"value": ""
},
"columns": {
"value": {
"id": "={{ $('Create New Record').item.json.id }}",
"MSP?": "={{ $('AI ICP Classification').item.json.output.msp }}",
"Reason": "={{ $('AI ICP Classification').item.json.output.reasoning }}",
"Email Address": "={{$('Enrich LinkedIn Profile').first().json.email}}"
},
"schema": [
{
"id": "id",
"type": "string",
"display": true,
"removed": false,
"readOnly": true,
"required": false,
"displayName": "id",
"defaultMatch": true
},
{
"id": "URN",
"type": "string",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "URN",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Name",
"type": "string",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Email Address",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Email Address",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Title",
"type": "string",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "Title",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Profile URL",
"type": "string",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "Profile URL",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "MSP?",
"type": "boolean",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "MSP?",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Reason",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Reason",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"id"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "update",
"authentication": "airtableOAuth2Api"
},
"typeVersion": 2.1
},
{
"id": "5d693aa7-79b0-4a19-b7b6-888710743617",
"name": "KI-ICP-Klassifizierung",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
2180,
440
],
"parameters": {
"text": "=You are an AI classifier that determines if a person works for xxxx based on LinkedIn scraper data.\nTask: Analyze the provided LinkedIn data and classify the person as icp or non-icp.\n\nData input: {{ $('Edit Fields1').item.json.linkedin_scraper_out }}\n\nJob Indicators:\n\nJob titles containing: \n\nInstructions:\n\nIf scraper returns blank/no data → = false\nAnalyze job title, company name, company description, and industry\nLook for XXX-related keywords and services\nProvide classification with reasoning\n\nExamples:\nInput: No data returned from scraper\njson{\n \"icp\": false,\n \"reasoning\": \"No data returned from scraping\"\n}\nInput: Job Title: \"Senior Network Engineer at TechCorp MSP\"\njson{\n \"icp\": true,\n \"reasoning\": \"Job title indicates and company name explicitly mentions keywords\"\n}\nInput: Job Title: \"Marketing Manager at Nike\"\njson{\n \"icp\": false,\n \"reasoning\": \"Marketing role at retail company with no indication of managed keywords services\"\n} ",
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.6
},
{
"id": "623feb13-cc50-4d71-964c-e076af7f7987",
"name": "Post-Reaktionen scrapen",
"type": "n8n-nodes-base.httpRequest",
"position": [
-1380,
120
],
"parameters": {
"url": "https://api.apify.com/v2/acts/apimaestro~linkedin-post-reactions/run-sync-get-dataset-items?token= ",
"method": "POST",
"options": {},
"jsonBody": "{\n \"post_url\": \"YOUR_POST_URN\",\n \"page_number\": 1\n}",
"sendBody": true,
"specifyBody": "json"
},
"typeVersion": 4.2
},
{
"id": "63f90fba-ffaa-434e-bdd0-b5368f35f0f2",
"name": "LinkedIn-Profil anreichern",
"type": "n8n-nodes-base.httpRequest",
"position": [
780,
440
],
"parameters": {
"url": "https://api.apify.com/v2/acts/dev_fusion~linkedin-profile-scraper/run-sync-get-dataset-items?token=",
"method": "POST",
"options": {},
"jsonBody": "={\n \"profileUrls\": [\n \"{{ $('Clean Data').item.json.profile_linkedin_url }}\"\n ]\n}\n",
"sendBody": true,
"specifyBody": "json"
},
"typeVersion": 4.2,
"alwaysOutputData": true
},
{
"id": "a6c1e1e0-d708-42da-b234-a4cec31700e2",
"name": "Haftnotiz1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2420,
-80
],
"parameters": {
"width": 640,
"height": 1040,
"content": "## 🎯 LinkedIn ICP Lead Qualification Automation\n\n### Automatically identify and qualify ideal customer prospects from LinkedIn post reactions using AI-powered analysis\n\nPerfect for sales teams, marketing professionals, and business development teams who want to turn LinkedIn engagement into qualified leads without manual research.\n\n### How it works\n* Scrapes LinkedIn post reactions to identify engaged users\n* Removes duplicates by checking existing records in Airtable\n* Enriches profiles with comprehensive LinkedIn data scraping\n* Uses AI to classify prospects as ICP matches with reasoning\n* Stores qualified leads with full contact and company information\n* Implements smart rate limiting to respect API constraints\n\n### How to use\n* Set up Apify API credentials for LinkedIn scraping actors\n* Configure Airtable base with prospect tracking fields\n* Add your LinkedIn post URL to start scraping reactions\n* Customize ICP criteria in the AI classification prompt\n* Run workflow to automatically qualify and store leads\n\n### Requirements\n* Apify account with API access\n* Airtable account with OAuth2 authentication\n* OpenAI or compatible AI model for classification\n* LinkedIn post URL with reactions to analyze\n\n### Good to know\n* **LinkedIn Safety**: Use only cookie-free Apify actors to prevent account detection\n* **Daily Limits**: Process maximum 1 page of reactions per day (50-100 profiles)\n* Apify actors cost ~$0.01-0.05 per profile scraped\n* Includes random delays to prevent rate limiting and account suspension\n* AI classification requires clear ICP criteria definition\n* Excessive scraping will trigger LinkedIn's anti-scraping measures and risk account suspension\n\nHappy Prospecting!"
},
"typeVersion": 1
},
{
"id": "6323c901-a4f8-4a41-b548-fe55e4576fff",
"name": "Haftnotiz",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1660,
-240
],
"parameters": {
"color": 7,
"width": 400,
"height": 300,
"content": "## 1. Extract Post Reactions\n[Read more about the HTTP Request node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest/)\n\nScrapes LinkedIn post reactions using Apify's post reaction scraper to identify users who engaged with your content. This provides the initial list of prospects who have already shown interest in your topic."
},
"typeVersion": 1
},
{
"id": "005853d9-c286-47bc-ba22-4b8dd01865be",
"name": "Haftnotiz2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-660,
-220
],
"parameters": {
"color": 7,
"width": 400,
"height": 300,
"content": "## 2. Clean & Deduplicate Prospects\n[Read more about the Set node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.set/)\n\nExtracts key profile information and checks against existing Airtable records to prevent duplicate processing. This saves API costs and ensures clean data management."
},
"typeVersion": 1
},
{
"id": "691b8dee-1378-4f02-82dd-0108b6bbbaad",
"name": "Haftnotiz3",
"type": "n8n-nodes-base.stickyNote",
"position": [
620,
680
],
"parameters": {
"color": 7,
"width": 400,
"height": 300,
"content": "## 3. Enrich LinkedIn Profiles\n[Read more about the HTTP Request node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest/)\n\nUses Apify's LinkedIn profile scraper to gather comprehensive professional information including job titles, company details, skills, and experience data for accurate ICP assessment."
},
"typeVersion": 1
},
{
"id": "50fe618e-7334-4fe0-aaf7-459d705f1e99",
"name": "Haftnotiz4",
"type": "n8n-nodes-base.stickyNote",
"position": [
2100,
60
],
"parameters": {
"color": 7,
"width": 400,
"height": 300,
"content": "## 4. AI-Powered ICP Classification\n[Read more about the LLM Chain node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/)\n\nAnalyzes enriched profile data using AI to determine if prospects match your Ideal Customer Profile. Provides reasoning for each classification decision to help refine your targeting criteria."
},
"typeVersion": 1
},
{
"id": "7fdca041-4a8b-4e98-b8dc-fef55ea10ee8",
"name": "Haftnotiz6",
"type": "n8n-nodes-base.stickyNote",
"position": [
940,
-360
],
"parameters": {
"color": 3,
"width": 460,
"height": 420,
"content": "### ⚠️ LinkedIn Safety & Rate Limiting Warning!\nThis workflow uses paid Apify actors and requires careful usage to avoid LinkedIn account suspension.\n\n1. **CRITICAL**: Use ONLY cookie-free Apify actors to avoid LinkedIn detection\n2. **Daily Limits**: Scrape maximum 1 page of reactions per day (typically 50-100 profiles)\n3. Process full profile enrichment in small batches to stay under LinkedIn's radar\n4. Set up Apify API credentials in both HTTP Request nodes\n5. Never run multiple instances simultaneously - LinkedIn tracks usage patterns\n6. Budget approximately $0.01-0.05 per prospect analyzed for Apify costs\n7. Monitor Apify usage dashboard and LinkedIn account health regularly\n8. Modify AI prompt to suit your need"
},
"typeVersion": 1
}
],
"pinData": {},
"connections": {
"25405123-3526-43b6-811e-afafb3796a39": {
"main": [
[
{
"node": "a22810dc-dee3-4e34-8f64-85e4026785fa",
"type": "main",
"index": 0
}
],
[
{
"node": "ea9be84c-663a-424b-9f82-49a1c0faad63",
"type": "main",
"index": 0
}
]
]
},
"89ac2fe8-a5df-4150-b921-318e641b429c": {
"main": [
[
{
"node": "2cc1a9bb-8b33-4af5-83ac-5af7906f778b",
"type": "main",
"index": 0
}
],
[
{
"node": "2cc1a9bb-8b33-4af5-83ac-5af7906f778b",
"type": "main",
"index": 0
}
]
]
},
"1124ab40-bbe3-4202-a16f-61228dbdb728": {
"main": [
[
{
"node": "107aee62-4ced-47a6-8063-ac0f0ecc2e49",
"type": "main",
"index": 0
}
]
]
},
"fd30b54c-a01a-4db5-8556-7ca3605b47d4": {
"main": [
[
{
"node": "cae1c603-11e7-4c83-bac1-008690c56a09",
"type": "main",
"index": 0
}
]
]
},
"2cc1a9bb-8b33-4af5-83ac-5af7906f778b": {
"main": [
[
{
"node": "5d693aa7-79b0-4a19-b7b6-888710743617",
"type": "main",
"index": 0
}
]
]
},
"3766e2ce-7f5f-430a-ac6d-d31c1ef595db": {
"main": [
[
{
"node": "03fb46da-5027-4996-87d1-511b20860f34",
"type": "main",
"index": 0
}
]
]
},
"490c6df0-e206-4673-ad2a-fbb2c50bb5ae": {
"main": [
[
{
"node": "de1ccf48-08d0-4058-964c-c9e940546e42",
"type": "main",
"index": 0
}
]
]
},
"de1ccf48-08d0-4058-964c-c9e940546e42": {
"main": [
[],
[
{
"node": "fd30b54c-a01a-4db5-8556-7ca3605b47d4",
"type": "main",
"index": 0
}
]
]
},
"c7676bae-f6c4-4746-a88f-ed877bb09d83": {
"main": [
[
{
"node": "de1ccf48-08d0-4058-964c-c9e940546e42",
"type": "main",
"index": 0
}
]
]
},
"cae1c603-11e7-4c83-bac1-008690c56a09": {
"main": [
[
{
"node": "25405123-3526-43b6-811e-afafb3796a39",
"type": "main",
"index": 0
}
]
]
},
"ea9be84c-663a-424b-9f82-49a1c0faad63": {
"main": [
[
{
"node": "3766e2ce-7f5f-430a-ac6d-d31c1ef595db",
"type": "main",
"index": 0
}
]
]
},
"03fb46da-5027-4996-87d1-511b20860f34": {
"main": [
[
{
"node": "63f90fba-ffaa-434e-bdd0-b5368f35f0f2",
"type": "main",
"index": 0
}
]
]
},
"5d693aa7-79b0-4a19-b7b6-888710743617": {
"main": [
[
{
"node": "490c6df0-e206-4673-ad2a-fbb2c50bb5ae",
"type": "main",
"index": 0
}
]
]
},
"623feb13-cc50-4d71-964c-e076af7f7987": {
"main": [
[
{
"node": "c7676bae-f6c4-4746-a88f-ed877bb09d83",
"type": "main",
"index": 0
}
]
]
},
"107aee62-4ced-47a6-8063-ac0f0ecc2e49": {
"main": [
[
{
"node": "139536cf-6039-48e7-a41f-21d98171bb33",
"type": "main",
"index": 0
}
]
]
},
"139536cf-6039-48e7-a41f-21d98171bb33": {
"main": [
[
{
"node": "89ac2fe8-a5df-4150-b921-318e641b429c",
"type": "main",
"index": 0
}
]
]
},
"63f90fba-ffaa-434e-bdd0-b5368f35f0f2": {
"main": [
[
{
"node": "1124ab40-bbe3-4202-a16f-61228dbdb728",
"type": "main",
"index": 0
}
]
]
},
"a22810dc-dee3-4e34-8f64-85e4026785fa": {
"main": [
[
{
"node": "de1ccf48-08d0-4058-964c-c9e940546e42",
"type": "main",
"index": 0
}
]
]
},
"198608d6-f63c-41d1-b612-161c208a8514": {
"ai_outputParser": [
[
{
"node": "5d693aa7-79b0-4a19-b7b6-888710743617",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"f48f1abd-b702-451f-8e75-4aabe1e3515f": {
"main": [
[
{
"node": "623feb13-cc50-4d71-964c-e076af7f7987",
"type": "main",
"index": 0
}
]
]
}
}
}Häufig gestellte Fragen
Wie verwende ich diesen Workflow?
Kopieren Sie den obigen JSON-Code, erstellen Sie einen neuen Workflow in Ihrer n8n-Instanz und wählen Sie "Aus JSON importieren". Fügen Sie die Konfiguration ein und passen Sie die Anmeldedaten nach Bedarf an.
Für welche Szenarien ist dieser Workflow geeignet?
Experte - Lead-Generierung, KI-Zusammenfassung
Ist es kostenpflichtig?
Dieser Workflow ist völlig kostenlos. Beachten Sie jedoch, dass Drittanbieterdienste (wie OpenAI API), die im Workflow verwendet werden, möglicherweise kostenpflichtig sind.
Verwandte Workflows
Tägliche intelligente Analyse der WhatsApp-Gruppen: GPT-4.1-Analyse und Transkription von Sprachnachrichten
Tägliche intelligente Analyse von WhatsApp-Gruppen: GPT-4.1-Analyse und Transkription von Sprachnachrichten
If
Set
Code
+
If
Set
Code
52 NodesDaniel Lianes
Verschiedenes
Lead-Generierung und E-Mail-Arbeitsabläufe
Automatisierung der B2B-Lead-Generierung und E-Mail-Marketing mit Google Maps, SendGrid und KI
If
Set
Code
+
If
Set
Code
141 NodesEzema Kingsley Chibuzo
Lead-Generierung
Kontaktinformationen anreichern
Umfassende Kontaktanreicherung für HubSpot basierend auf Apollo, LinkedIn und GPT-4o
If
Set
Code
+
If
Set
Code
24 NodesInterlock GTM
Lead-Generierung
n8n-Knoten in der visuellen Referenzbibliothek erkunden
Erkundung von n8n-Knoten in der visuellen Referenzbibliothek
If
Ftp
Set
+
If
Ftp
Set
113 NodesI versus AI
Sonstiges
Shopify-Produktliste aus Bildern mit Gemini AI und Airtable generieren
Shopify-Produktlisten aus Bildern mit Gemini AI und Airtable generieren
If
Set
Code
+
If
Set
Code
33 NodesMANISH KUMAR
Content-Erstellung
Google Maps-Unternehmens-Scraping und Lead-Anreicherung mit Bright Data und Google Gemini
Google Maps-Unternehmens-Scraping und Lead-Anreicherung mit Bright Data und Google Gemini
Set
Code
Wait
+
Set
Code
Wait
29 NodesRanjan Dailata
Lead-Generierung
Workflow-Informationen
Schwierigkeitsgrad
Experte
Anzahl der Nodes26
Kategorie2
Node-Typen13
Autor
Anna Bui
@annabuiplaygroundExterne Links
Auf n8n.io ansehen →
Diesen Workflow teilen