Mein Workflows 4

• •HTTP Webhook-Endpunkt (wird von n8n automatisch generiert)
• •LinkedIn API-Anmeldedaten
• •AWS Access Key und Secret

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  "name": "My workflow 4",
  "tags": [
    {
      "id": "3lDSaFyWblKZQ4N6",
      "name": "AI News Brief",
      "createdAt": "2025-09-18T12:44:44.400Z",
      "updatedAt": "2025-09-18T12:44:44.400Z"
    }
  ],
  "nodes": [
    {
      "id": "9b2b1be7-761a-430f-ba65-5f3bc1ab8bee",
      "name": "Beitrag erstellen",
      "type": "n8n-nodes-base.linkedIn",
      "position": [
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      "parameters": {
        "text": "= {{ $json.output }} #AWS #CloudComputing #Technology #Innovation #AWSNews",
        "person": "gG5YYLBASA",
        "additionalFields": {
          "visibility": "PUBLIC"
        }
      },
      "credentials": {
        "linkedInOAuth2Api": {
          "id": "YIFLLjDlvCpWuJIB",
          "name": "LinkedIn account"
        }
      },
      "typeVersion": 1
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    {
      "id": "826c77b3-540e-4faf-978b-08fbe231e330",
      "name": "Webhook",
      "type": "n8n-nodes-base.webhook",
      "position": [
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      "webhookId": "e4878fda-90b9-4503-8410-6ec14a3dc1ed",
      "parameters": {
        "path": "e4878fda-90b9-4503-8410-6ec14a3dc1ed",
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        "httpMethod": "POST"
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      "parameters": {
        "text": "={{ JSON.stringify($json) }}",
        "options": {
          "systemMessage": "You are a Cloud Architect with 10+ years of experience and genuine passion for cloud technologies. Your mission is to transform AWS industry news into engaging, professional LinkedIn posts that resonate with technical professionals and business leaders.\nCore Instructions:\n\nOutput only the LinkedIn post content - no preamble, explanations, or additional commentary\nAlways include the source link at the end of the post\nWrite from your perspective as an experienced Solutions Architect\nTarget audience: CTOs, DevOps engineers, cloud architects, and tech-forward business leaders\n\nPost Structure & Style:\n\nHook: Start with an attention-grabbing insight post title or question that connects to business impact\nContext: Briefly explain what this AWS news means in practical terms\nAnalysis: Share your expert perspective on why this matters (include 2-3 key benefits or implications)\nForward-looking: Comment on industry trends or what this signals for the future\nCall to action: End with a question or statement that encourages engagement\nSource link: Include the original news link\n\nVoice & Tone:\n\nProfessional yet conversational\nDemonstrate deep technical knowledge without being overly technical\nShow genuine enthusiasm for innovation\nInclude personal insights and opinions\nUse industry terminology appropriately\nBe concise but substantive (150-300 words optimal)\n\nContent Guidelines:\n\nFocus on business impact, not just technical features\nConnect AWS developments to broader industry trends\nInclude relevant hashtags (3-5 maximum)\nMention specific use cases or customer benefits when applicable\nAvoid marketing speak - provide authentic expert analysis\n\nExample Elements to Include:\n\n\"In my experience working with enterprise clients...\"\n\"This addresses a pain point I've seen repeatedly...\"\n\"What excites me most about this announcement...\"\n\"For organizations considering...\"\n\nGenerate the LinkedIn post now based on the provided AWS news."
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    {
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      "name": "AWS Bedrock Chat Model1",
      "type": "@n8n/n8n-nodes-langchain.lmChatAwsBedrock",
      "position": [
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      "parameters": {
        "model": "anthropic.claude-3-sonnet-20240229-v1:0",
        "options": {}
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      "credentials": {
        "aws": {
          "id": "jB9HmTXJTS46QqSf",
          "name": "AWS account"
        }
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      "typeVersion": 1.1
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    {
      "id": "c14c0a4f-9cd1-4d5e-ac46-1d005f1c2518",
      "name": "Flow 1: News-Sammlung & Analyse",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "color": 3,
        "width": 1936,
        "height": 528,
        "content": "## 📰 Flow 1: AWS News Collection & Analysis\n\n**Automated daily news monitoring and AI-powered analysis**\n\n### Process Flow:\n1. **Scheduled Trigger** → Runs daily at 8 PM\n2. **RSS Reader** → Fetches latest AWS news\n3. **Data Debugger** → Validates and cleans RSS data\n4. **AI Agent** → Analyzes news with Claude 3 Sonnet\n5. **Data Cleaner** → Formats structured output\n6. **Feishu Bitable** → Stores analyzed news\n\n### AI Analysis Includes:\n- Professional 200-word summary\n- Key themes and keywords\n- Importance rating (Low/Medium/High)\n- Business impact assessment\n- Source link preservation"
      },
      "typeVersion": 1
    },
    {
      "id": "5a38c8a5-4b69-44a9-9e22-50740478c64c",
      "name": "Flow 2: LinkedIn Inhaltsgenerierung",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "color": 4,
        "width": 1952,
        "height": 560,
        "content": "## 📱 Flow 2: LinkedIn Content Generation & Publishing\n\n**Manual approval workflow for professional LinkedIn content**\n\n### Process Flow:\n1. **Feishu Automation** → Triggers on approval status change\n2. **Webhook** → Receives approved news data\n3. **AI Agent** → Generates LinkedIn-optimized content\n4. **LinkedIn Post** → Publishes with hashtags\n\n### Content Features:\n- Attention-grabbing headlines\n- Technical insights from Solutions Architect perspective\n- Business impact analysis\n- Call-to-action engagement\n- Relevant hashtags (#AWS #CloudComputing #Technology)\n\n### Approval Workflow:\n- News stored with \"Pending\" status\n- Manual review in Feishu Bitable\n- Change status to \"Approved\" to trigger posting"
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        "content": "## AWS News Analysis & LinkedIn Automation Pipeline\n\n**Transform AWS industry news into engaging LinkedIn content with AI-powered analysis and automated approval workflows.**\n\n### What This Template Does:\n- **Automatically fetches** latest AWS news from RSS feeds\n- **AI-analyzes** content for business impact and technical insights\n- **Stores analyzed news** in Feishu Bitable for review\n- **Generates professional LinkedIn posts** with approval workflow\n- **Publishes content** automatically to LinkedIn\n\n### Perfect For:\n- Cloud architects & DevOps engineers\n- Content creators & marketing teams\n- AWS consultants building thought leadership\n- Technical leaders sharing industry insights\n"
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        "content": "## 📊 Feishu Bitable: News Storage & Management\n\n**Centralized database for AWS news analysis and approval workflow**\n\n### Table Structure:\n- **title**: News headline\n- **pubDate**: Publication date\n- **summary**: AI-generated 200-word analysis\n- **keywords**: Extracted themes\n- **rating**: Importance level (Low/Medium/High)\n- **link**: Original source URL\n- **approval_status**: Pending/Approved/Rejected\n\n### Workflow Integration:\n- Flow 1 populates table with analyzed news\n- Manual review and approval process\n- Flow 2 triggers on status change to \"Approved\"\n\n![Feishu Bitable Example](https://pincloud.fr/n8n/feishuawsnews.png)"
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      "name": "Zeitgesteuerter Auslöser",
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      "name": "RSS-Reader",
      "type": "n8n-nodes-base.rssFeedRead",
      "position": [
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      "parameters": {
        "url": "https://aws.amazon.com/about-aws/whats-new/recent/feed",
        "options": {}
      },
      "typeVersion": 1.2
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    {
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      "name": "RSS-Daten-Debugger",
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      "parameters": {
        "jsCode": "// RSS Data Debugger\nconst rssData = $input.all();\nconsole.log('=== RSS Data Debug Info ===');\nconsole.log('Input data type:', typeof rssData);\nconsole.log('Input data length:', rssData ? rssData.length : 'undefined');\nconsole.log('Input data structure:', JSON.stringify(rssData, null, 2));\n\n// If data is empty, return default test data\nif (!rssData || rssData.length === 0) {\n  console.log('RSS data is empty, returning test data');\n  return [\n    {\n      title: 'Test AI News Title',\n      description: 'This is a test AI-related news description',\n      pubDate: new Date().toISOString(),\n      link: 'https://example.com/test'\n    }\n  ];\n}\n\nreturn rssData;"
      },
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    },
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      "onError": "continueRegularOutput",
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      "parameters": {
        "text": "={{ JSON.stringify($json) }}",
        "options": {
          "maxIterations": 5,
          "systemMessage": "You are a professional AWS analyst. Your task is to analyze AWS industry news. Please strictly follow the required JSON format for output, ensuring all quotes are properly escaped.\nFor each news item, you need to:\n1. Extract news title, publication date, and original link\n2. Generate a news summary of about 200 words, highlighting core points and key information\n3. Extract 1-3 keywords that reflect the main themes of the news\n4. Assess news importance, divided into three levels: Low, Medium, High\n- High: Major technological breakthroughs, important policy releases, large mergers and acquisitions, major developments of industry benchmark companies\n- Medium: New product launches, funding news, technological progress, industry reports, important partnerships\n- Low: General reports, opinion articles, small-scale developments, routine updates\n\nEach output format must strictly follow the following JSON structure, do not add any other content:\n{\n\"title\":\"News Title\",\n\"pubDate\":\"Publication Date\",\n\"summary\":\"News summary of about 200 words, highlighting core points\",\n\"keywords\":[\"Keyword1\",\"Keyword2\",\n\"Keyword3\"],\n\"rating\":\"Low/Medium/High\",\n\"link\":\"Original Link\"\n}",
          "returnIntermediateSteps": false
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        "hasOutputParser": true
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        "jsonSchemaExample": "{\n\"title\":\"News Title\",\n\"pubDate\":\"Publication Date\",\n\"summary\":\"News summary of about 200 words, highlighting core points\",\n\"keywords\":[\"Keyword1\",\"Keyword2\",\n\"Keyword3\"],\n\"rating\":\"Low/Medium/High\",\n\"link\":\"Original Link\"\n}"
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      "typeVersion": 1.3
    },
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      "name": "Bitable: Datensatz hinzufügen",
      "type": "n8n-nodes-feishu-lite.feishuNode",
      "position": [
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      "parameters": {
        "body": "={\n      \"fields\": {\n        \"title\": \"{{ $json.output.title }}\",\n        \"pubDate\": \"{{ $json.output.pubDate }}\",\n        \"summary\": \"{{ $json.output.summary }}\",\n        \"keywords\": \"{{ $json.output.keywords }}\",\n        \"rating\": \"{{ $json.output.rating }}\",\n        \"link\": \"{{ $json.output.link }}\",\n        \"approval_status\": \"Pending\"\n      }\n    }",
        "app_toke": "QJUhbp3WPaxOlfsjHhyccN7xnld",
        "resource": "bitable",
        "table_id": "tbl8yKlxebKcep2z",
        "operation": "bitable:table:record:add"
      },
      "credentials": {
        "feishuCredentialsApi": {
          "id": "RfXcydpGCZxnDMkx",
          "name": "Feishu Credentials account"
        }
      },
      "typeVersion": 1
    },
    {
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      "name": "Datenbereiniger",
      "type": "n8n-nodes-base.code",
      "position": [
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      "parameters": {
        "jsCode": "// Data Cleaner Node - Fixed Version\nconst inputData = $input.all();\nconsole.log('=== Data Cleaner Node Started ===');\nconsole.log('Input data type:', typeof inputData);\nconsole.log('Input data length:', inputData ? inputData.length : 'undefined');\nconsole.log('Input data structure:', JSON.stringify(inputData, null, 2));\n\nconst cleanedData = [];\n\n// Ensure there is input data\nif (!inputData || inputData.length === 0) {\n  console.log('No input data');\n  return [];\n}\n\n// Process each input item\nfor (let i = 0; i < inputData.length; i++) {\n  const item = inputData[i];\n  console.log(`\\nProcessing item ${i + 1}:`, item);\n  \n  try {\n    // Extract actual data content\n    let newsData;\n    \n    // Check different data structures\n    if (item.output) {\n      // Standard format: {output: {...}}\n      newsData = item.output;\n      console.log('Found output format data');\n    } else if (item.json && item.json.output) {\n      // n8n format: {json: {output: {...}}}\n      newsData = item.json.output;\n      console.log('Found json.output format data');\n    } else if (item.json) {\n      // Direct json format: {json: {...}}\n      newsData = item.json;\n      console.log('Found direct json format data');\n    } else {\n      // Direct data format\n      newsData = item;\n      console.log('Found direct data format');\n    }\n    \n    console.log('Extracted news data:', newsData);\n    \n    // Validate required fields\n    if (!newsData.title || !newsData.pubDate) {\n      console.log('Skipping invalid data item, missing required fields');\n      continue;\n    }\n    \n    // Clean and standardize data\n    const cleanOutput = {\n      title: (newsData.title || '').replace(/\"/g, '').trim(),\n      pubDate: (newsData.pubDate || '').trim(),\n      summary: (newsData.summary || '').replace(/\"/g, '').trim(),\n      keywords: Array.isArray(newsData.keywords) ? newsData.keywords : [],\n      rating: (newsData.rating || '').trim(),\n      link: (newsData.link || '').trim()\n    };\n    \n    console.log('Cleaned data:', cleanOutput);\n    \n    // Add to cleaned data array\n    cleanedData.push({\n      json: {\n        output: cleanOutput\n      }\n    });\n    \n    console.log('✅ Successfully processed data item:', cleanOutput.title.substring(0, 50));\n    \n  } catch (error) {\n    console.log('❌ Error processing data item:', error.message);\n    console.log('Problematic data item:', item);\n    continue;\n  }\n}\n\nconsole.log('\\n=== Data Cleaning Completed ===');\nconsole.log('Cleaned data count:', cleanedData.length);\nconsole.log('Cleaned data:', JSON.stringify(cleanedData, null, 2));\n\nreturn cleanedData;"
      },
      "typeVersion": 2
    },
    {
      "id": "e8caf226-0813-4eac-8d82-8959d607fff8",
      "name": "AWS Bedrock Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatAwsBedrock",
      "position": [
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      "parameters": {
        "model": "anthropic.claude-3-sonnet-20240229-v1:0",
        "options": {}
      },
      "credentials": {
        "aws": {
          "id": "jB9HmTXJTS46QqSf",
          "name": "AWS account"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "db023c43-8f68-4e34-9d66-198cd5d509d7",
      "name": "Feishu-Automatisierungseinrichtung",
      "type": "n8n-nodes-base.stickyNote",
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      "parameters": {
        "width": 1088,
        "height": 928,
        "content": "## ⚙️ Feishu Automation Setup\n\n**Configure webhook automation to trigger LinkedIn posting**\n\n### Automation Configuration:\n1. **Trigger**: When field value changes\n2. **Field**: approval_status\n3. **Condition**: approval_status equals \"Approved\"\n4. **Action**: Send HTTP request\n\n### Webhook Settings:\n- **Method**: POST\n- **URL**: Copy from Flow 2 Webhook node\n- **Headers**: Content-Type: application/json\n- **Body**: {{record}}\n\n### Important:\n- URL must match Flow 2 Webhook exactly\n- Test automation with sample data\n- Ensure webhook is accessible from Feishu\n\n![Feishu Automation Setup](https://pincloud.fr/n8n/feishuautomation.png)"
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      "typeVersion": 1
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    {
      "id": "6d11b8a5-47e2-442b-9e88-539ef7a26622",
      "name": "Manueller Freigabeprozess",
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      "parameters": {
        "width": 480,
        "height": 832,
        "content": "## ✅ Manual Approval Process\n\n**Review and approve news items for LinkedIn publication**\n\n### Approval Steps:\n1. **Review** analyzed news in Feishu Bitable\n2. **Check** AI-generated summary and rating\n3. **Verify** source link and publication date\n4. **Change status** from \"Pending\" to \"Approved\"\n5. **Automation triggers** LinkedIn post generation\n\n### Quality Control:\n- Review AI analysis for accuracy\n- Ensure content aligns with brand voice\n- Check for appropriate technical depth\n- Verify hashtags and engagement elements\n\n### Rejection Process:\n- Change status to \"Rejected\" for unsuitable content\n- Add comments for improvement\n- No automation trigger for rejected items\n\n![Approval Interface](https://pincloud.fr/n8n/awsnewslinkedinapproved.png)"
      },
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      "name": "Einrichtungsanleitung",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "width": 904,
        "height": 800,
        "content": "## 🔧 Setup Instructions\n\n**Configure all required services before activating this workflow**\n\n### 1. AWS Bedrock Setup\n- **Enable Claude 3 Sonnet** in AWS Bedrock console\n- **Create IAM user** with Bedrock permissions\n- **Configure credentials** in n8n\n- **Links**: [AWS Bedrock](https://console.aws.amazon.com/bedrock/) | [IAM Console](https://console.aws.amazon.com/iam/)\n\n### 2. Feishu Bitable Setup\n- **Create Feishu account** and Bitable\n- **Set up table structure** with required columns\n- **Create developer app** with Bitable permissions\n- **Configure automation** for webhook triggers\n- **Links**: [Feishu Platform](https://www.feishu.cn/en/) | [Developer Console](https://open.feishu.cn/)\n\n### 3. LinkedIn Company Account\n- **Create LinkedIn Developer app**\n- **Configure OAuth2** with posting permissions\n- **Set up company page** admin access\n- **Test posting permissions**\n- **Links**: [LinkedIn Developers](https://www.linkedin.com/developers/) | [Company Pages](https://www.linkedin.com/company/)\n\n### 4. n8n Configuration\n- **Import workflow** JSON\n- **Configure all credentials**\n- **Test webhook** connectivity\n- **Activate scheduled trigger**\n\n**⚠️ Requires self-hosted n8n for community nodes**\n- **Links**: [n8n-nodes-feishu-lite](https://www.npmjs.com/package/n8n-nodes-feishu-lite) "
      },
      "typeVersion": 1
    }
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