Clasificación automatizada de correos electrónicos de Outlook con IA

• •No hay requisitos previos especiales, puede importar y usarlo directamente

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  "name": "Fully Automatic Categorise Outlook Emails with AI",
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        "content": "# Fully Automatic Categorise Outlook Emails with AI\nBuilt by [Can KURT](https://github.com/ck-cankurt/) at [ubden.com](https://ubden.com)"
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      "name": "Markdown1",
      "type": "n8n-nodes-base.markdown",
      "notes": "Converts the body of the email to markdown",
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      ],
      "parameters": {
        "html": "={{ $('Loop Over Items1').item.json.body.content }}",
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      "notes": "Set email fields",
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              "name": "subject",
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              "name": "from",
              "type": "object",
              "value": "={{ $json.from.emailAddress }}"
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              "value": "={{ $json.data\n    .replace(/<[^>]*>/g, '')                      // Remove HTML tags\n    .replace(/\\[(.*?)\\]\\((.*?)\\)/g, '')            // Remove Markdown links like [text](link)\n    .replace(/!\\[.*?\\]\\(.*?\\)/g, '')               // Remove Markdown images like ![alt](image-link)\n    .replace(/\\|/g, '')                            // Remove table separators \"|\"\n    .replace(/-{3,}/g, '')                         // Remove horizontal rule \"---\"\n    .replace(/\\n+/g, ' ')                          // Remove multiple newlines\n    .replace(/([^\\w\\s.,!?@])/g, '')                // Remove special characters except essential ones\n    .replace(/\\s{2,}/g, ' ')                       // Replace multiple spaces with a single space\n    .trim()                                        // Trim leading/trailing whitespace\n}}\n"
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      "notes": "Checks if the email has been read",
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                "type": "boolean",
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              "leftValue": "={{ $json.isRead }}",
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      "parameters": {
        "text": "=Categorise the following email\n<email>\n{{ $('varEmal1').first().json.toJsonString() }}\n</email>\n\nEnsure your final output is valid JSON with no additional text or token in the following format:\n\n{\n  \"subject\": \"SUBJECT_LINE\",1\n  \"category\": \"CATEGORY\",\n  \"subCategory\": \"SUBCATEGORY\", //use sparingly\n  \"analysis\": \"ANALYSIS_REASONING\"\n}\n\nRemember you can only use ONE of the following categories {{ $json.category }}. No other categories can be used. Use the subcategory for additional context, for example, if a SaaS email requires action, or if a business email requires action. Do not create any additional subcategories, you can only use ONE of the following {{ $json.category }}",
        "options": {
          "systemMessage": "=You are an advanced AI email categorization system for a freelance developer. Your mission is to intelligently analyze and categorize emails with maximum accuracy and context awareness.\n\n**INTELLIGENT CATEGORIZATION ENGINE:**\n\n**Phase 1 - Dynamic Category Processing:**\n- Parse and interpret all available categories from {{ $json.category }}\n- Automatically detect category hierarchies and relationships\n- Build intelligent mapping between email characteristics and categories\n\n**Phase 2 - Multi-Layer Email Analysis:**\n- **Sender Intelligence**: Domain reputation, email patterns, historical behavior\n- **Subject Line Analysis**: Keywords, urgency indicators, spam patterns, language detection\n- **Content Deep Scan**: Body text analysis, link analysis, attachment detection, HTML structure\n- **Contextual Understanding**: Business relevance, timing, frequency patterns\n- **Spam/Threat Detection**: Advanced phishing detection, scam patterns, malicious content identification\n\n**Phase 3 - Advanced Decision Logic:**\n\n**PRIORITY CATEGORIZATION RULES:**\n1. **SECURITY THREATS** → Immediate junk classification for phishing, scams, suspicious links\n2. **ACTION REQUIRED** → Critical business emails needing immediate response get \"Action\" category\n3. **BUSINESS CONTEXT** → Legitimate business communications vs promotional content distinction\n4. **SENDER REPUTATION** → Known vendors/partners vs unknown senders treatment\n5. **CONTENT ANALYSIS** → Marketing language detection vs technical communication\n6. **URGENCY DETECTION** → Time-sensitive content identification\n\n**SPECIALIZED CATEGORIZATION INTELLIGENCE:**\n- **Domain/Hosting/Server**: Automatically detect hosting providers, domain registrars, server notifications\n- **SaaS Action Items**: Distinguish between promotional SaaS emails vs critical system notifications\n- **E-commerce Detection**: Advanced marketplace email recognition (Amazon, local platforms)\n- **Financial Intelligence**: Invoice detection, payment confirmations, banking communications\n- **Support Ticket Analysis**: Technical support vs general customer service distinction\n- **Corporate Communication**: Contract emails, official business correspondence detection\n\n**ANTI-SPAM INTELLIGENCE:**\n- **Marketing Pattern Detection**: Newsletter signatures, unsubscribe links, promotional language\n- **Emoji Spam Detection**: Excessive emoji usage indicating promotional content\n- **Subject Line Red Flags**: Discount offers, limited time offers, \"act now\" language\n- **Sender Spoofing Detection**: Fake sender analysis, domain verification\n- **Content Authenticity**: Legitimate business communication vs mass marketing\n\n**DUAL CATEGORY LOGIC:**\n- Primary category assignment based on email nature\n- Secondary category for action requirements or business context\n- Smart combination logic (e.g., \"Technology & SaaS\" + \"Action\" for critical system alerts)\n\n**CONTEXTUAL LEARNING PATTERNS:**\n- Developer-specific email patterns recognition\n- Client communication identification\n- Project-related correspondence detection\n- Vendor relationship understanding\n- Support ticket escalation recognition\n\n**OUTPUT PRECISION:**\nReturn only valid JSON with enhanced analysis:\n{\n  \"subject\": \"EXACT_EMAIL_SUBJECT\",\n  \"category\": \"PRIMARY_CATEGORY_FROM_AVAILABLE_LIST\",\n  \"subCategory\": \"SECONDARY_CATEGORY_IF_APPLICABLE\",\n  \"analysis\": \"Detailed reasoning explaining categorization decision with specific indicators found\",\n  \"confidence\": \"HIGH/MEDIUM/LOW based on analysis certainty\"\n}\n\n**AVAILABLE CATEGORIES:** {{ $json.category }}\n\n**CRITICAL INSTRUCTIONS:**\n- Use ONLY categories from the provided list\n- Never create new categories\n- Prioritize accuracy over speed\n- When uncertain, err on the side of caution (prefer Action category for potentially important emails)\n- Always provide detailed analysis reasoning\n- Consider the freelance developer context in all decisions\n\nAnalyze the email in <email> tags and provide intelligent categorization."
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    {
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      "name": "Get many folders",
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        },
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            "childFolderCount"
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          "includeChildFolders": true
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        "resource": "folder",
        "operation": "getAll",
        "returnAll": true
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              "id": "de2ad4f2-7381-4715-a3f4-59611e161b74",
              "name": "id",
              "type": "string",
              "value": "={{ $('RecievedEmail').item.json.id }}"
            },
            {
              "id": "ea74a279-6763-4b67-9eb4-5e347e1d32f4",
              "name": "category",
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            "childFolderCount",
            "parentFolderId"
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          "includeChildFolders": true
        },
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        "operation": "getAll",
        "returnAll": true
      },
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              "operator": {
                "name": "filter.operator.equals",
                "type": "string",
                "operation": "equals"
              },
              "leftValue": "={{ $('varJSON1').item.json.output.category }}",
              "rightValue": "={{ $json.displayName }}"
            }
          ]
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      },
      "typeVersion": 2.2
    },
    {
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      "name": "Disparador programado",
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      "parameters": {
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              "minutesInterval": 1
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      "typeVersion": 1.2
    },
    {
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      "name": "RecievedCorreo electrónico",
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          "importance",
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              "foldersToInclude": [
                "AQMkAGExN2Q4YTQ0AC01NDI4LTRhNmYtODYwZS05YzU0YzE5MzAyZjQALgAAA9jkZp65v9BIjLPAC68z-1MBAKhQNfuxpX5CpnvHa8QQO84AAAIBDAAAAA=="
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          "__rl": true,
          "mode": "id",
          "value": "={{ $('VarID Category').item.json.id }}"
        },
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          "categories": "={{ \n  [$('varJSON1').first().json.output.category, $('varJSON1').first().json.output.subCategory]\n    .filter(item => item && item.trim() !== \"\")\n    .map(item => item.charAt(0).toUpperCase() + item.slice(1))\n}}"
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          "value": "={{ $('If').item.json.id }}"
        },
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          "mode": "id",
          "value": "={{ $('VarID Category').item.json.id }}"
        },
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      },
      "credentials": {
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        }
      },
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      "name": "OpenRouter Chat Model",
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      "position": [
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      ],
      "parameters": {
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        "options": {}
      },
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      "onError": "continueErrorOutput",
      "position": [
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      "parameters": {
        "text": "=Categorise the following email\n<email>\n{{ $('varEmal1').first().json.toJsonString() }}\n</email>\n\nEnsure your final output is valid JSON with no additional text or token in the following format:\n\n{\n  \"subject\": \"SUBJECT_LINE\",1\n  \"category\": \"CATEGORY\",\n  \"subCategory\": \"SUBCATEGORY\", //use sparingly\n  \"analysis\": \"ANALYSIS_REASONING\"\n}\n\nRemember you can only use ONE of the following categories {{ $json.category }}. No other categories can be used. Use the subcategory for additional context, for example, if a SaaS email requires action, or if a business email requires action. Do not create any additional subcategories, you can only use ONE of the following {{ $json.category }}",
        "options": {
          "systemMessage": "=You are an advanced AI email categorization system for a freelance developer. Your mission is to intelligently analyze and categorize emails with maximum accuracy and context awareness.\n\n**INTELLIGENT CATEGORIZATION ENGINE:**\n\n**Phase 1 - Dynamic Category Processing:**\n- Parse and interpret all available categories from {{ $json.category }}\n- Automatically detect category hierarchies and relationships\n- Build intelligent mapping between email characteristics and categories\n\n**Phase 2 - Multi-Layer Email Analysis:**\n- **Sender Intelligence**: Domain reputation, email patterns, historical behavior\n- **Subject Line Analysis**: Keywords, urgency indicators, spam patterns, language detection\n- **Content Deep Scan**: Body text analysis, link analysis, attachment detection, HTML structure\n- **Contextual Understanding**: Business relevance, timing, frequency patterns\n- **Spam/Threat Detection**: Advanced phishing detection, scam patterns, malicious content identification\n\n**Phase 3 - Advanced Decision Logic:**\n\n**PRIORITY CATEGORIZATION RULES:**\n1. **SECURITY THREATS** → Immediate junk classification for phishing, scams, suspicious links\n2. **ACTION REQUIRED** → Critical business emails needing immediate response get \"Action\" category\n3. **BUSINESS CONTEXT** → Legitimate business communications vs promotional content distinction\n4. **SENDER REPUTATION** → Known vendors/partners vs unknown senders treatment\n5. **CONTENT ANALYSIS** → Marketing language detection vs technical communication\n6. **URGENCY DETECTION** → Time-sensitive content identification\n\n**SPECIALIZED CATEGORIZATION INTELLIGENCE:**\n- **Domain/Hosting/Server**: Automatically detect hosting providers, domain registrars, server notifications\n- **SaaS Action Items**: Distinguish between promotional SaaS emails vs critical system notifications\n- **E-commerce Detection**: Advanced marketplace email recognition (Amazon, local platforms)\n- **Financial Intelligence**: Invoice detection, payment confirmations, banking communications\n- **Support Ticket Analysis**: Technical support vs general customer service distinction\n- **Corporate Communication**: Contract emails, official business correspondence detection\n\n**ANTI-SPAM INTELLIGENCE:**\n- **Marketing Pattern Detection**: Newsletter signatures, unsubscribe links, promotional language\n- **Emoji Spam Detection**: Excessive emoji usage indicating promotional content\n- **Subject Line Red Flags**: Discount offers, limited time offers, \"act now\" language\n- **Sender Spoofing Detection**: Fake sender analysis, domain verification\n- **Content Authenticity**: Legitimate business communication vs mass marketing\n\n**DUAL CATEGORY LOGIC:**\n- Primary category assignment based on email nature\n- Secondary category for action requirements or business context\n- Smart combination logic (e.g., \"Technology & SaaS\" + \"Action\" for critical system alerts)\n\n**CONTEXTUAL LEARNING PATTERNS:**\n- Developer-specific email patterns recognition\n- Client communication identification\n- Project-related correspondence detection\n- Vendor relationship understanding\n- Support ticket escalation recognition\n\n**OUTPUT PRECISION:**\nReturn only valid JSON with enhanced analysis:\n{\n  \"subject\": \"EXACT_EMAIL_SUBJECT\",\n  \"category\": \"PRIMARY_CATEGORY_FROM_AVAILABLE_LIST\",\n  \"subCategory\": \"SECONDARY_CATEGORY_IF_APPLICABLE\",\n  \"analysis\": \"Detailed reasoning explaining categorization decision with specific indicators found\",\n  \"confidence\": \"HIGH/MEDIUM/LOW based on analysis certainty\"\n}\n\n**AVAILABLE CATEGORIES:** {{ $json.category }}\n\n**CRITICAL INSTRUCTIONS:**\n- Use ONLY categories from the provided list\n- Never create new categories\n- Prioritize accuracy over speed\n- When uncertain, err on the side of caution (prefer Action category for potentially important emails)\n- Always provide detailed analysis reasoning\n- Consider the freelance developer context in all decisions\n\nAnalyze the email in <email> tags and provide intelligent categorization."
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