自動コンテンツ作成＆公開ツール

• •Telegram Bot Token
• •ターゲットAPIの認証情報が必要な場合あり
• •Google Sheets API認証情報

{
  "id": "Automated_Content_Creator_Publisher",
  "meta": {
    "instanceId": "c0790e3e488ca88963d79065ff780585b2f3f2f125aa65e7cac85370af219e8e",
    "templateCredsSetupCompleted": true
  },
  "name": "Automated Content Creator & Publisher",
  "tags": [
    "AI",
    "Content Generation",
    "Marketing",
    "SEO",
    "Publishing",
    "Telegram",
    "Google Sheets"
  ],
  "nodes": [
    {
      "id": "Ollama--0",
      "name": "Ollama: 記事作成モデル",
      "type": "@n8n/n8n-nodes-langchain.lmOllama",
      "position": [
        -2660,
        -220
      ],
      "parameters": {
        "model": "llama3:latest",
        "options": {}
      },
      "credentials": {
        "ollamaApi": {
          "id": "",
          "name": "Ollama account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "12a28d97-2679-4bc5-a8a0-65ec7ef76398",
      "name": "HTTP Request: ウェブサイト投稿",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -960,
        -220
      ],
      "httpMethod": "POST",
      "parameters": {
        "url": "={{ $connections.websiteApi.url }}",
        "method": "POST",
        "options": {},
        "sendBody": true,
        "contentType": "multipart-form-data",
        "sendHeaders": true,
        "bodyParameters": {
          "parameters": [
            {
              "name": "user_id",
              "value": "={{ $('Function: Prepare Post Data').item.json.user_id }}"
            },
            {
              "name": "username",
              "value": "={{ $('Function: Prepare Post Data').item.json.username }}"
            },
            {
              "name": "postTitle",
              "value": "={{ $('Function: Prepare Post Data').item.json.postTitle }}"
            },
            {
              "name": "postContent",
              "value": "={{ $('LLM: Article Writer').item.json.text }}\n\n{{ $('Function: Parse LLM Output').item.json.tags2[0] }}\n{{ $('Function: Parse LLM Output').item.json.tags2[1] }}\n{{ $('Function: Parse LLM Output').item.json.tags2[2] }}\n{{ $('Function: Parse LLM Output').item.json.tags2[3] }}\n{{ $('Function: Parse LLM Output').item.json.tags2[4] }}\n{{ $('Function: Parse LLM Output').item.json.tags2[5] }}\n{{ $('Function: Parse LLM Output').item.json.tags2[6] }}\n\n{{ $('Function: Parse LLM Output').item.json.username }}\n"
            },
            {
              "name": "bgColor",
              "value": "={{ $('Function: Prepare Post Data').item.json.bg_color }}"
            },
            {
              "name": "LinkAff",
              "value": "={{ $('Function: Prepare Post Data').item.LinkAff }}"
            },
            {
              "name": "postType",
              "value": "={{ $('Function: Prepare Post Data').item.postType }}"
            },
            {
              "name": "embeddedContent",
              "value": "={{ $('Function: Prepare Post Data').item.embeddedContent }}"
            },
            {
              "name": "tags",
              "value": "={{ $('Function: Parse LLM Output').item.json.tags1.join(',') }}"
            },
            {
              "name": "post-thumbnail",
              "parameterType": "formBinaryData",
              "inputDataFieldName": "generated-image.png"
            },
            {
              "name": "aff_image",
              "value": "="
            }
          ]
        },
        "headerParameters": {
          "parameters": [
            {
              "name": "X-Api-Key",
              "value": "={{ $connections.websiteApi.apiKey }}"
            }
          ]
        }
      },
      "typeVersion": 3,
      "bodyContentType": "multipart-form-data"
    },
    {
      "id": "3c460292-1341-48e8-83cb-784a7a5f2d54",
      "name": "Telegram: 通知送信",
      "type": "n8n-nodes-base.telegram",
      "position": [
        -800,
        -220
      ],
      "parameters": {
        "text": "New Article Posted: {{ $('Function: Parse LLM Output').item.json.postTitle }}\n\nSummary:\n{{ $('Function: Parse LLM Output').item.json.postContent }}\n\nTags: {{ $('Function: Parse LLM Output').item.json.tags2.join(', ') }}",
        "chatId": "={{ $connections.telegramApi.chatId }}",
        "additionalFields": {}
      },
      "credentials": {
        "telegramApi": {
          "id": "em5mbDzWm1EygPa5",
          "name": "Telegram account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "b21535d9-db05-4d51-b0d5-344ca70ea2b5",
      "name": "Google Gemini: 写真生成",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -1480,
        -220
      ],
      "parameters": {
        "url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-preview-image-generation:generateContent?key={{ $connections.googleGeminiApi.apiKey }}",
        "options": {},
        "requestMethod": "POST",
        "jsonParameters": true,
        "bodyParametersJson": "={\n  \"contents\": [\n    {\n      \"parts\": [\n        {\n          \"text\": \"masterpiece, 8k, ultra-detailed, photorealistic cinematic shot of: {{ $('Function: Parse LLM Output').item.json.imagePrompt }} thai-face. professional photography, sharp focus, high dynamic range (HDR), intricate details, dramatic natural lighting, aspect ratio 16:9\"\n        }\n      ]\n    }\n  ],\n  \"generationConfig\": {\n    \"responseModalities\": [\n      \"TEXT\",\n      \"IMAGE\"\n    ]\n  }\n}",
        "headerParametersJson": "{\"Content-Type\":\"application/json\"}"
      },
      "typeVersion": 1
    },
    {
      "id": "12be1078-6a82-46e3-a35f-c497dc7789c7",
      "name": "AI画像をファイルに変換",
      "type": "n8n-nodes-base.convertToFile",
      "position": [
        -1120,
        -220
      ],
      "parameters": {
        "options": {},
        "operation": "toBinary",
        "sourceProperty": "candidates.0.content.parts.1.inlineData.data",
        "binaryPropertyName": "generated-image.png"
      },
      "typeVersion": 1.1
    },
    {
      "id": "Ollama-SEO--5",
      "name": "Ollama: SEO・マーケティングモデル",
      "type": "@n8n/n8n-nodes-langchain.lmOllama",
      "position": [
        -2360,
        80
      ],
      "parameters": {
        "model": "llama3:latest",
        "options": {}
      },
      "credentials": {
        "ollamaApi": {
          "id": "",
          "name": "Ollama account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "47c8db8e-d6fb-4caa-8332-43ac033c9fef",
      "name": "LLM: 記事作成",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        -2680,
        -120
      ],
      "parameters": {
        "text": "=### INSTRUCTION ###\nYou are a professional Thai editor and adaptive long-form article writer. Your primary task is to analyze the user's input and decide on the best format for the article (either a list-based article or a standard analytical article), then write a high-quality, flawless article in Thai.\n\n### STRICT RULES ###\n1. Your ENTIRE response MUST BE ONLY the article content.\n2. NO titles, NO labels, NO conversation, NO explanations.\n3. FLAWLESS THAI: Pay meticulous attention to spelling and grammar. NO typos or malformed words.\n4. PURE LANGUAGE: Use only natural Thai. DO NOT mix in words from other languages unless they are common loanwords.\n\n### USER INPUT ###\nSUMMARY: {{ $('Google Sheets: Read News').item.json.description }}\n\n\n\n### TASK ###\n1.  **Analyze the TOPIC**: First, determine if the TOPIC suggests a list of items, steps, or methods (e.g., contains numbers like \"5 วิธี...\", \"7 เทคนิค...\", \"10 สิ่งที่...\").\n\n2.  **Choose the Format and Write**:\n    * **IF the TOPIC is a list**: Write the article in a \"listicle\" format. Start with an introduction, then present each numbered item (e.g., \"1. [ชื่อวิธีแรก]\", \"2. [ชื่อวิธีที่สอง]\", etc.) with a detailed explanation for each point. Ensure the total word count is at least 800 words. Conclude with a summary.\n    * **IF the TOPIC is NOT a list**: Write a standard detailed, long-form analytical article. The article MUST BE AT LEAST 800 WORDS and structured with an introduction, at least 5-7 main body paragraphs, and a concluding paragraph.\n\n3.  **Final Touches**: The concluding paragraph for either format should provide a powerful and memorable final thought. After writing, proofread the entire article to ensure it adheres to all rules and is free of errors.",
        "batching": {},
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 1.7
    },
    {
      "id": "b2acb79c-ce1f-4a78-b6ec-b9ec644ca362",
      "name": "LLM: SEO・マーケティング",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        -2320,
        -120
      ],
      "parameters": {
        "text": "=INSTRUCTION\nYou are a structured data generation AI. Your task is to follow a sequence of steps to create content components based on the user's input.\n\nSTRICT RULES\n\nYour ENTIRE response MUST be ONLY the text between the ---START_DATA--- and ---END_DATA--- markers.\n\nYou MUST generate ALL fields listed in the output structure. DO NOT omit any field.\n\nThe IMAGE_PROMPT field is MANDATORY.\n\nFor TAGS2, you MUST generate exactly 3 pairs of corresponding Thai and English tags. Each pair must represent the same concept and be DIRECTLY RELEVANT to the USER INPUT.\n\nDO NOT use placeholders such as ‘ไทยคำ’, ‘ThaiWord’ or any generic filler. Each Thai-English pair must be meaningful, unique, and reflect a core concept from the USER INPUT. If unable to do this exactly, leave TAGS2 blank.\n\nNO conversation, NO explanations, NO apologies.\n\nAll LABELS must be uppercase and end with a colon (e.g., TITLE:).\n\nThe TITLE must be strictly no longer than  words. If you cannot create a TITLE within this limit, leave it blank.\n\nUSER INPUT\nINPUT_DESCRIPTION: {{ $json.text }}\n\nTHINKING_PROCESS (Internal thought process for you, do not output this section):\nStep 1: Deeply analyze the core theme of the USER INPUT.\nStep 2: Generate the Thai TITLE. The TITLE must be attractive, concise, and strictly no more than 8 words. If unable, leave TITLE blank.\nStep 3: Write the Thai EXCERPT.\nStep 4: Create 8 relevant TAGS in Thai, based on the input.\nStep 5: Identify 8 core concepts from the USER INPUT. Create 8 Thai-English tag pairs (TAGS2) strictly relevant to the main topics. Do not use placeholders or repeat.\nStep 6: Generate a detailed English IMAGE_PROMPT reflecting the main theme.\n\nREQUIRED OUTPUT STRUCTURE\n---START_DATA---\nTITLE: [หัวข้อข่าวหลัก (H1) เป็นภาษาไทย ดึงดูดใจ กระชับ ไม่เกิน 8 คำ]\n\nEXCERPT: [ย่อหน้าเกริ่นนำประมาณ 150 คำ]\n\nTAGS: [8 แท็กภาษาไทย คั่นด้วยจุลภาค]\n\nTAGS2: [3 คู่แท็ก ไทย-อังกฤษ แบบ #คำไทย, #EnglishWord แต่ละคู่ต้องสื่อสารเรื่องเดียวกันโดยตรง]\n\nIMAGE_PROMPT: [ภาษาอังกฤษ 1 ย่อหน้า แสดงแนวคิดหลัก]\n\n---END_DATA---",
        "batching": {},
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 1.7
    },
    {
      "id": "912fd524-acdd-47cf-92f2-cdc7a00bc77d",
      "name": "Function: 投稿データ準備",
      "type": "n8n-nodes-base.code",
      "position": [
        -1860,
        -120
      ],
      "parameters": {
        "jsCode": "// This node prepares the final JSON payload for the website POST request.\n// It combines data from various upstream nodes.\n\nconst parsedLLMOutput = $('Function: Parse LLM Output').item.json;\nconst imageBinaryData = $('Merge Image Data').item.json.imageBinaryData;\nconst postContentFromWriter = $('LLM: Article Writer').item.json.text;\n\n// Default values for website post, can be configured via workflow parameters or credentials\nconst defaultUserId = '{{ $connections.websiteApi.userId }}'; \nconst defaultUsername = '{{ $connections.websiteApi.username }}';\nconst defaultBgColor = '#FFFFFF';\nconst defaultLinkAff = '';\nconst defaultPostType = 'General';\nconst defaultEmbeddedContent = '';\n\nconst outputItem = {\n    json: {\n        user_id: parsedLLMOutput.user_id || defaultUserId,\n        username: parsedLLMOutput.username || defaultUsername,\n        postTitle: parsedLLMOutput.postTitle,\n        postContent: postContentFromWriter,\n        bgColor: parsedLLMOutput.bg_color || defaultBgColor,\n        LinkAff: parsedLLMOutput.LinkAff || defaultLinkAff,\n        postType: parsedLLMOutput.postType || defaultPostType,\n        embeddedContent: parsedLLMOutput.embeddedContent || defaultEmbeddedContent,\n        tags1: parsedLLMOutput.tags1,\n        tags2: parsedLLMOutput.tags2,\n        imageBinaryData: imageBinaryData, // Pass binary data reference\n        description: postContentFromWriter // Use full article as description for Telegram\n    },\n    // Ensure binary data is also passed through for the HTTP Request node\n    binary: {\n        'generated-image.png': imageBinaryData\n    }\n};\n\nreturn [outputItem];"
      },
      "typeVersion": 2
    },
    {
      "id": "760df977-da46-4d10-9f19-d2cb2099e5b8",
      "name": "If: 実画像か？",
      "type": "n8n-nodes-base.if",
      "position": [
        -1700,
        -120
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "16513b65-0fae-4aa6-a30d-5c2bc5b2a0ba",
              "operator": {
                "type": "string",
                "operation": "equal"
              },
              "leftValue": "={{ $('LLM: Image Type Analyzer').item.json.type }}",
              "rightValue": "ภาพจริง"
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "3c916018-d0ad-4111-814a-be6c1cb84537",
      "name": "AIリソ印刷画像をファイルに変換",
      "type": "n8n-nodes-base.convertToFile",
      "position": [
        -1120,
        -40
      ],
      "parameters": {
        "options": {},
        "operation": "toBinary",
        "sourceProperty": "candidates[0].content.parts[1].inlineData.data",
        "binaryPropertyName": "generated-image.png"
      },
      "typeVersion": 1.1
    },
    {
      "id": "a021bbfd-870d-41c6-b481-729643acb196",
      "name": "Function: LLM出力解析",
      "type": "n8n-nodes-base.code",
      "position": [
        -2020,
        -120
      ],
      "parameters": {
        "jsCode": "// This node parses the structured text output from the LLM.\n// It extracts TITLE, EXCERPT, TAGS, TAGS2, and IMAGE_PROMPT.\n\nconst rawLLMText = $json.text || \"\";\n\n// --- 1. Extract desired data using Markers ---\nconst startMarker = \"---START_DATA---\";\nconst endMarker = \"---END_DATA---\";\nlet articleDataText = rawLLMText;\n\nconst startIndex = rawLLMText.indexOf(startMarker);\nconst endIndex = rawLLMText.indexOf(endMarker, startIndex);\n\nif (startIndex !== -1 && endIndex !== -1) {\n    articleDataText = rawLLMText.substring(startIndex + startMarker.length, endIndex);\n}\n\nfunction sanitizeValue(value) {\n    if (typeof value !== 'string') return \"\";\n    return value.trim().replace(/^\"|\"$|^|$/g, '').trim();\n}\n\n// --- 2. Extract each Field ---\nconst fields = {\n    TITLE: \"\",\n    EXCERPT: \"\",\n    TAGS: \"\",\n    TAGS2: \"\",\n    IMAGE_PROMPT: \"\"\n};\nconst regex = /(?:^|\\n)\\**\\s*(TITLE|EXCERPT|TAGS|TAGS2|IMAGE_PROMPT)\\s*\\**:\\s*([\\s\\S]*?)(?=(?:\\n)\\**\\s*[A-Z_]+:|$)/gi;\n\nlet match;\nwhile ((match = regex.exec(articleDataText)) !== null) {\n    const fieldName = match[1].trim().toUpperCase();\n    const fieldValue = sanitizeValue(match[2]);\n    if (fields.hasOwnProperty(fieldName)) {\n        fields[fieldName] = fieldValue;\n    }\n}\n\n// --- 3. Post-processing data correction (preventing data bleed) ---\nconst tagsMarker = 'TAGS:';\nif (fields.EXCERPT && fields.EXCERPT.includes(tagsMarker)) {\n    const parts = fields.EXCERPT.split(new RegExp(`\\\\s*${tagsMarker}`));\n    fields.EXCERPT = parts[0];\n    if (!fields.TAGS && parts.length > 1) {\n        fields.TAGS = parts[1];\n    }\n}\n\nconst tags2Marker = 'TAGS2:';\nif (fields.TAGS && fields.TAGS.includes(tags2Marker)) {\n    const parts = fields.TAGS.split(new RegExp(`\\\\s*${tags2Marker}`));\n    fields.TAGS = parts[0];\n    if (!fields.TAGS2 && parts.length > 1) {\n        fields.TAGS2 = parts[1];\n    }\n}\n\n// --- 4. Cleanse TAGS and TAGS2 separately ---\nconst cleanedTags1 = fields.TAGS.split(',')\n    .map(tag => tag.trim())\n    .filter(tag => tag.length > 0)\n    .map(tag => tag.replace(/^#/, '')); // Removes leading '#' for tags1\n\nconst cleanedTags2 = fields.TAGS2.split(',')\n    .map(tag => tag.trim())\n    .filter(tag => tag.length > 0)\n    .map(tag => tag.startsWith('#') ? tag : `#${tag}`); // Ensures each tag in tags2 starts with '#'\n\n// --- 5. Create JSON Object for export ---\nconst jsonOutput = {\n    \"postTitle\": fields.TITLE,\n    \"postContent\": fields.EXCERPT,\n    \"tags1\": cleanedTags1,\n    \"tags2\": cleanedTags2,\n    \"imagePrompt\": fields.IMAGE_PROMPT\n};\n\nreturn [jsonOutput];"
      },
      "typeVersion": 2
    },
    {
      "id": "f6ff9656-7342-492a-87d0-d290c76047a4",
      "name": "If: AI画像か？",
      "type": "n8n-nodes-base.if",
      "position": [
        -3260,
        -320
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "or",
          "conditions": [
            {
              "id": "d206cefb-3142-4bc0-b3d7-0669c94b06c9",
              "operator": {
                "type": "string",
                "operation": "equal"
              },
              "leftValue": "={{ $('LLM: Image Type Analyzer').item.json.type }}",
              "rightValue": "ภาพ AI"
            }
          ]
        },
        "looseTypeValidation": "="
      },
      "typeVersion": 2.2
    },
    {
      "id": "308fc7dc-97fc-4a79-8340-f5ced91c015b",
      "name": "Function: 画像タイプ分析",
      "type": "n8n-nodes-base.code",
      "position": [
        -3220,
        -160
      ],
      "parameters": {
        "jsCode": "// This node analyzes the news content to determine the appropriate image type (Real or AI) and extracts relevant keywords for real images.\n\nconst responseText = $json.text || \"\";\n\nconst outputItem = {\n    json: {\n        type: \"\",\n        keywords: []\n    }\n};\n\nif (!responseText) {\n    console.warn(\"Input text is undefined or null. Returning default empty values.\");\n    return [outputItem];\n}\n\nconst lines = responseText.split('\\n').map(line => line.trim()).filter(Boolean);\n\nlet foundType = \"\";\nlet foundKeywords = [];\n\nfor (let i = 0; i < lines.length; i++) {\n    const currentLine = lines[i];\n\n    if (currentLine.startsWith(\"ภาพจริง\")) {\n        foundType = \"ภาพจริง\";\n        if (i + 1 < lines.length) {\n            const nextLine = lines[i + 1];\n            if (!nextLine.startsWith(\"ภาพ AI\") && !nextLine.toLowerCase().startsWith(\"keywords:\")) {\n                foundKeywords = nextLine.split(',')\n                                       .map(kw => kw.trim())\n                                       .filter(Boolean);\n                break;\n            }\n        }\n    } else if (currentLine.startsWith(\"ภาพ AI\")) {\n        foundType = \"ภาพ AI\";\n        break;\n    }\n\n    if (currentLine.toLowerCase().startsWith(\"keywords:\")) {\n        const keywordString = currentLine.substring(\"keywords:\".length);\n        foundKeywords = keywordString.split(',')\n                                    .map(kw => kw.trim())\n                                    .filter(Boolean);\n        if (foundType) {\n            break;\n        }\n    }\n}\n\noutputItem.json.type = foundType;\noutputItem.json.keywords = foundKeywords;\n\nreturn [outputItem];"
      },
      "typeVersion": 2
    },
    {
      "id": "9965d31f-85fc-450f-8918-de20504495bb",
      "name": "HTTP Request: 実画像取得",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -3220,
        -20
      ],
      "parameters": {
        "url": "={{ $connections.localImageServer.url }}",
        "method": "POST",
        "options": {},
        "jsonBody": "={\n  \"keyword\": \"{{ $('Function: Image Type Analyzer').item.json.keywords }}\",\n  \"status\": \"started\"\n}\n",
        "sendBody": true,
        "sendQuery": true,
        "specifyBody": "json",
        "queryParameters": {
          "parameters": [
            {
              "name": "Key",
              "value": "Content-Type"
            },
            {
              "name": "Value",
              "value": "application/json"
            }
          ]
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "29ce8614-edd0-4496-9deb-493e2ad9d430",
      "name": "Read/Write Files from Disk (Local)",
      "type": "n8n-nodes-base.readWriteFile",
      "position": [
        -3220,
        140
      ],
      "parameters": {
        "options": {},
        "fileSelector": "/pic/**/*.{png,jpg,jpeg,gif,bmp,tiff,webp}"
      },
      "typeVersion": 1
    },
    {
      "id": "d85f8b4c-8170-41d9-8d7b-329e41c249b4",
      "name": "画像バイナリデータ抽出",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        -3220,
        300
      ],
      "parameters": {
        "options": {},
        "operation": "binaryToPropery"
      },
      "typeVersion": 1
    },
    {
      "id": "bb69c31c-837b-4714-b0cb-10d47a86796f",
      "name": "画像データ統合",
      "type": "n8n-nodes-base.merge",
      "position": [
        -2940,
        -120
      ],
      "parameters": {
        "mode": "combine",
        "options": {},
        "combineBy": "combineByPosition"
      },
      "typeVersion": 3.2
    },
    {
      "id": "1c358faf-7240-4b85-b9c1-e047668aae7a",
      "name": "抽出画像をファイルに変換",
      "type": "n8n-nodes-base.convertToFile",
      "position": [
        -3220,
        420
      ],
      "parameters": {
        "options": {},
        "operation": "toBinary",
        "sourceProperty": "data",
        "binaryPropertyName": "generated-image.png"
      },
      "typeVersion": 1.1
    },
    {
      "id": "fefba463-aa01-48e3-aeb7-40423b1e9ed1",
      "name": "Function: 画像バイナリ添付",
      "type": "n8n-nodes-base.code",
      "position": [
        -2920,
        320
      ],
      "parameters": {
        "jsCode": "for (const item of items) {\n  if (item.binary && item.binary['generated-image.png']) {\n    item.json.imageBinaryData = item.binary['generated-image.png'];\n  }\n}\nreturn items;"
      },
      "typeVersion": 2
    },
    {
      "id": "Google-Sheets--20",
      "name": "Google Sheets: ニュース読み取り",
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