Puntuación de ICP de empresa con Explorium
Este es unLead Generation, AI Summarizationflujo de automatización del dominio deautomatización que contiene 8 nodos.Utiliza principalmente nodos como FormTrigger, HttpRequest, Agent, McpClientTool, LmChatAnthropic. Automatización de la puntuación de ICP de empresas con datos de Explorium y análisis con Claude AI
- •Pueden requerirse credenciales de autenticación para la API de destino
- •Clave de API de Anthropic
Nodos utilizados (8)
Categoría
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"name": "Score Company ICP with Explorium",
"tags": [],
"nodes": [
{
"id": "53ac44a9-4774-42f5-8b3d-d7c83272c1fa",
"name": "Al enviar el formulario",
"type": "n8n-nodes-base.formTrigger",
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"webhookId": "2d5e3676-5284-4da1-bdf5-34f92d8d435f",
"parameters": {
"options": {},
"formTitle": "Company ICP scoring",
"formFields": {
"values": [
{
"fieldLabel": "Company Name",
"placeholder": "Apple",
"requiredField": true
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},
"formDescription": "=This automation takes company's Linkedin Profile URL and Airtop Profile (authenticated for Linkedin) and returns the company's ICP score"
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"content": "## Input Parameters\nRun this workflow using a form "
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"content": "## Calculate ICP"
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"parameters": {
"width": 700,
"height": 880,
"content": "# 🧠 ICP Scoring Agent (n8n + Explorium + LLM)\n\n## 🔧 How It Works\n1. Input: Company name\n2. MCP Server pulls firmographic & tech data\n3. LLM scores the company using 3-pillar framework\n4. Output: Structured Google doc created with leveraged @AgentGeeks formater \n\n## 📊 Scoring System (100 pts total)\n| Pillar | Max |\n|---------------------------|-----|\n| Strategic Fit | 40 |\n| AI / Tech Readiness | 40 |\n| Engagement & Reachability | 20 |\n\n## 🧠 Criteria\n- **Strategic Fit:** Industry, size, buyer roles, use case\n- **Tech Readiness:** AI focus, hiring, stack maturity\n- **Reachability:** Geography, contactability, data quality\n\n## 🏁 Verdicts\n- **90–100:** ⭐ Ideal ICP \n- **70–89:** ✅ Good Fit \n- **40–69:** ⚠️ Medium Fit \n- **< 40:** ❌ Poor Fit \n\n## 💼 Use Case\nScore and rank companies automatically for GTM prioritization. Use structured JSON to map into CRMs, Docs, or lead routing systems.\n"
},
"typeVersion": 1
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{
"id": "7c5a0104-f73c-42be-bb1b-6b335e81501f",
"name": "Agentee de IA",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
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"parameters": {
"text": "=Generate a clean Markdown report for the company \"{{ $json['Company Name'] }}\" based on the following:\n\n- Strategic Fit (score out of 40, summary, justification)\n- AI/Tech Readiness (score out of 40, summary, justification)\n- Engagement & Reachability (score out of 20, summary, justification)\n- Final Summary (1–2 sentence wrap-up)\n- Total ICP Score: Sum of the 3 categories (max = 100)\n- Verdict: Poor Fit, Medium Fit, Good Fit, or Ideal ICP\n\nThe output should be a clean Markdown document with headers and bold labels, like this:\n\n## 📌 Strategic Fit \n**Score:** 36 / 40 \n**Summary:** ... \n**Justification:** ...\n\nDo not include any explanation or JSON. Just return the report in Markdown.\n",
"options": {
"systemMessage": "=You are an AI business analyst tasked with generating clean Markdown reports summarizing ICP (Ideal Customer Profile) evaluations.\n\nUse this 3-pillar scoring system (max 100 points total):\n- Strategic Fit: 0–40 points\n- AI/Tech Readiness: 0–40 points\n- Engagement & Reachability: 0–20 points\n\nYour output must:\n- Be formatted in Markdown\n- Use headers (##) and bold labels (e.g., **Score:**)\n- Include only the report — no preamble, explanation, or extra intro\n- Always show the total score out of 100\n- Use one of the following verdicts: Poor Fit, Medium Fit, Good Fit, Ideal ICP\n\nNever scale the total to 300. Never include anything outside the report.\n"
},
"promptType": "define"
},
"typeVersion": 1.9
},
{
"id": "53b09fbf-c8da-43a0-b7ac-ed9ebacd2dba",
"name": "Cliente MCP",
"type": "@n8n/n8n-nodes-langchain.mcpClientTool",
"position": [
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],
"parameters": {
"sseEndpoint": "mcp.explorium.ai/sse",
"authentication": "headerAuth"
},
"credentials": {
"httpHeaderAuth": {
"id": "LZOE1nqmRk3X6r1J",
"name": "Explorium"
}
},
"typeVersion": 1
},
{
"id": "6f0c8ee4-5aad-4b49-9202-bb2071f6b933",
"name": "Anthropic Modelo de chat",
"type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"position": [
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],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "claude-3-7-sonnet-20250219",
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{
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"name": "HTTP Solicitud",
"type": "n8n-nodes-base.httpRequest",
"position": [
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"parameters": {
"url": "https://md2doc.n8n.aemalsayer.com",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "output",
"value": "={{ $json.output }}"
},
{
"name": "fileName",
"value": "={{ $('On form submission').item.json['Company Name'] }} ICP Report"
}
]
},
"nodeCredentialType": "googleDocsOAuth2Api"
},
"credentials": {
"googleDocsOAuth2Api": {
"id": "mZUWrRtmU1aouO4A",
"name": "Google Docs account"
}
},
"typeVersion": 4.2
}
],
"active": false,
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}¿Cómo usar este flujo de trabajo?
Copie el código de configuración JSON de arriba, cree un nuevo flujo de trabajo en su instancia de n8n y seleccione "Importar desde JSON", pegue la configuración y luego modifique la configuración de credenciales según sea necesario.
¿En qué escenarios es adecuado este flujo de trabajo?
Intermedio - Generación de leads, Resumen de IA
¿Es de pago?
Este flujo de trabajo es completamente gratuito, puede importarlo y usarlo directamente. Sin embargo, tenga en cuenta que los servicios de terceros utilizados en el flujo de trabajo (como la API de OpenAI) pueden requerir un pago por su cuenta.
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