KI-gesteuerter Nachrichtenmonitor und Social-Media-Beitragsgenerator - OpenAI und Upload-Post

• •Google Sheets API-Anmeldedaten
• •OpenAI API Key

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      "position": [
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      "parameters": {
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        "height": 448,
        "content": "**Fetch**\n- RSS Feed Trigger"
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        "width": 344,
        "height": 448,
        "content": "**Analyze & Score**\n- OpenAI Chat Model (Scoring)\n- Scoring AI"
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        "height": 416,
        "content": "**Parse & Enrich**\n- Parse Scoring AI JSON"
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      "parameters": {
        "width": 400,
        "height": 448,
        "content": "**Route**\n- Quality Gate\n- Review vs Archive Filter"
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    },
    {
      "id": "ff8b6280-94ed-42bb-bdd3-c1a5cb4c74ec",
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        "height": 416,
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        "content": "**Store & Publish**\n- Store Content (Google Sheets)\n- Publish to X, Threads, and LinkedIn"
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      "parameters": {
        "feedUrl": "https://morss.it/https://techcrunch.com/feed/",
        "pollTimes": {
          "item": [
            {
              "mode": "everyHour"
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      "position": [
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      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o",
          "cachedResultName": "gpt-4o"
        },
        "options": {}
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      "credentials": {
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        "jsCode": "// Parse AI Agent JSON response\nconst items = $input.all();\nconst parsedItems = [];\n\nfor (const item of items) {\n  const rawResponse = item.json.output;\n  \n  try {\n    // Clean up escaped characters and parse the JSON response from AI\n    const cleanedResponse = rawResponse.replace(/\\\\n/g, '').replace(/\\\\/g, '');\n    const aiAnalysis = JSON.parse(cleanedResponse);\n    \n    // Handle key_topics - it might be a string or array\n    let topicsString = \"\";\n    if (Array.isArray(aiAnalysis.key_topics)) {\n      topicsString = aiAnalysis.key_topics.join(\", \");\n    } else if (typeof aiAnalysis.key_topics === 'string') {\n      topicsString = aiAnalysis.key_topics;\n    }\n    \n    // Combine original article data with parsed AI analysis\n    parsedItems.push({\n      json: {\n        // Original article data\n        title: $node[\"RSS Feed Trigger\"].json.title,\n        link: $node[\"RSS Feed Trigger\"].json.link,\n        pubDate: $node[\"RSS Feed Trigger\"].json.pubDate,\n        content: $node[\"RSS Feed Trigger\"].json['content:encoded'],\n        \n        // Parsed AI analysis\n        quality_score: aiAnalysis.quality_score || 5,\n        relevance_score: aiAnalysis.relevance_score || 5,\n        key_topics: topicsString,\n        summary: aiAnalysis.summary || \"No summary provided\",\n        content_angle: aiAnalysis.content_angle || \"No angle provided\",\n        action: aiAnalysis.action || \"REVIEW\"\n      }\n    });\n    \n  } catch (error) {\n    // Handle parsing errors gracefully\n    console.log(\"Parsing error:\", error.message);\n    console.log(\"Raw response:\", rawResponse);\n    \n    parsedItems.push({\n      json: {\n        title: $node[\"RSS Feed Trigger\"].json.title,\n        link: $node[\"RSS Feed Trigger\"].json.link,\n        pubDate: $node[\"RSS Feed Trigger\"].json.pubDate,\n        content: $node[\"RSS Feed Trigger\"].json['content:encoded'],\n        quality_score: 5,\n        relevance_score: 5,\n        key_topics: \"Parse Error\",\n        summary: \"AI response could not be parsed: \" + error.message,\n        content_angle: \"Manual review needed\",\n        action: \"REVIEW\"\n      }\n    });\n  }\n}\n\nreturn parsedItems;"
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      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
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      ],
      "parameters": {
        "sessionKey": "={{ $execution.id }}",
        "sessionIdType": "customKey"
      },
      "typeVersion": 1.3
    },
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      "position": [
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      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1",
          "cachedResultName": "gpt-4.1"
        },
        "options": {}
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        "jsCode": "// Parse content creation AI response\nconst items = $input.all();\nconst contentItems = [];\n\nfor (const item of items) {\n  try {\n    const rawResponse = item.json.output;\n    const cleanedResponse = rawResponse.replace(/\\\\\\\\n/g, '').replace(/\\\\\\\\/g, '');\n    const contentData = JSON.parse(cleanedResponse);\n\n    // Get the original article data\n    const originalData = $node[\"Code\"].json; // Reference our enhanced parsing node\n\n    contentItems.push({\n      json: {\n        // Original article info\n        title: originalData.title,\n        link: originalData.link,\n        quality_score: originalData.quality_score,\n        relevance_score: originalData.relevance_score,\n        summary: originalData.summary,\n\n        // Generated content\n        twitter_thread: contentData.twitter_thread || [],\n        linkedin_post: contentData.linkedin_post || \"Content generation failed\",\n        content_ready: contentData.content_ready || false,\n\n        // Metadata\n        created_at: new Date().toISOString(),\n        platform_ready: true\n      }\n    });\n\n  } catch (error) {\n    // Handle parsing errors\n    contentItems.push({\n      json: {\n        title: $node[\"Code\"].json.title,\n        link: $node[\"Code\"].json.link,\n        twitter_thread: [\"Content generation failed\"],\n        linkedin_post: \"Content generation failed: \" + error.message,\n        content_ready: false,\n        error: error.message\n      }\n    });\n  }\n}\n\nreturn contentItems;\n"
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    },
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      "name": "Store Content",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
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      ],
      "parameters": {
        "columns": {
          "value": {
            "Article Link": "={{ $json.link }}",
            "Date Created": "={{ $now }}",
            "Article Title": "={{ $json.title }}",
            "Quality Score": "={{ $json.quality_score }}",
            "Twitter Content": "={{ $json.twitter_thread[0] }}{{ $json.twitter_thread[1] }}{{ $json.twitter_thread[2] }}{{ $json.twitter_thread[3] }}",
            "LinkedIn Content": "={{ $json.linkedin_post }}"
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              "display": true,
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              "id": "Quality Score",
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              "required": false,
              "displayName": "Quality Score",
              "defaultMatch": false,
              "canBeUsedToMatch": true
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              "id": "Twitter Content",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Twitter Content",
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              "canBeUsedToMatch": true
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              "id": "Published (Yes/No)",
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              "canBeUsedToMatch": true
            },
            {
              "id": "Performance Notes",
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              "canBeUsedToMatch": true
            }
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          "mode": "list",
          "value": "gid=0",
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      "parameters": {
        "columns": {
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            "Title": "={{ $json.title }}",
            "Summary": "={{ $json.summary }}",
            "Pub Date": "={{ $json.pubDate }}",
            "Content Angle": "={{ $json.content_angle }}",
            "Quality Score": "={{ $json.quality_score }}",
            "Relevance Score": "={{ $json.relevance_score }}"
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              "canBeUsedToMatch": true
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              "canBeUsedToMatch": true
            },
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      "parameters": {
        "columns": {
          "value": {
            "Link": "={{ $json.link }}",
            "Title": "={{ $json.title }}",
            "Reason": "={{ $json.summary }}",
            "Pub Date": "={{ $json.pubDate }}",
            "Quality Score": "={{ $json.quality_score }}"
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    {
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      "name": "Content AI",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -1520,
        1248
      ],
      "parameters": {
        "text": "=Create social media content for this article:\n\nTitle: {{ $json.title }}\nSummary: {{ $json.summary }}\nSuggested Angle: {{ $json.content_angle }}\nLink: {{ $json.link }}\n\nArticle scored {{ $json.quality_score }}/10 for quality and {{ $json.relevance_score }}/10 for relevance.\n",
        "options": {
          "systemMessage": "You are an expert social media content creator specializing in AI and automation topics.\n\nYour job is to take high-quality articles and create engaging, platform-specific content that drives audience engagement and positions the author as a thought leader.\n\nFor each article, create content for these platforms:\n\nTWITTER THREAD (3-4 tweets):\n- Tweet 1: Hook that grabs attention and introduces the main insight\n- Tweet 2-3: Key takeaways with specific examples or data\n- Tweet 4: Call-to-action with link to original article\n- Use emojis strategically\n- Keep each tweet under 280 characters\n\nLINKEDIN POST:\n- Professional tone suitable for business audience\n- 150-200 word summary highlighting business value\n- Include 2-3 key insights from the article\n- End with an engaging question to drive comments\n- Use professional hashtags (#artificialintelligence #businessautomation #productivity)\n\nReturn your response as valid JSON in this exact format:\n{\n  \"twitter_thread\": [\"tweet 1 text\", \"tweet 2 text\", \"tweet 3 text\", \"tweet 4 text\"],\n  \"linkedin_post\": \"full linkedin post text here\",\n  \"content_ready\": true\n}\n\nFocus on value, insights, and engagement. Avoid being overly promotional."
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      "position": [
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      ],
      "parameters": {
        "text": "=Analyze this article: {{ $json.title }} - {{ $json['content:encoded'] }}",
        "options": {
          "systemMessage": "You are a content research assistant specializing in AI and automation topics.\n\nYour job is to analyze articles and determine their value for a content creator who focuses on:\n- AI tools and techniques\n- No-code automation platforms \n- Business automation workflows\n- AI news and developments\n\nIMPORTANT: You must respond with valid JSON in exactly this format:\n\n{\n  \"quality_score\": 8,\n  \"relevance_score\": 9,\n  \"key_topics\": \"AI tools\", \"automation\", \"business efficiency\",\n  \"summary\": \"Brief 2-3 sentence summary of the key points and main value.\",\n  \"content_angle\": \"Suggested angle for social media content creation\",\n  \"action\": \"PUBLISH\"\n}\n\nCRITICAL: Ensure all strings are properly quoted with double quotes. Arrays should contain quoted strings like [\"item1\", \"item2\", \"item3\"].\n\nScoring guidelines:\n- Quality Score (1-10): Based on source credibility, content depth, and usefulness\n- Relevance Score (1-10): How well it matches our AI/automation focus\n- Action: PUBLISH (scores 8+), REVIEW (scores 5-7), or ARCHIVE (scores <5)\n\nOnly return the JSON object, nothing else."
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      "typeVersion": 2.2
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    {
      "id": "4f150948-56e7-49e3-aec6-84ea4f34cd64",
      "name": "Quality Filter",
      "type": "n8n-nodes-base.if",
      "position": [
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      ],
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