Googleトレンド、ニュース、Firecrawl、そしてClaude AIを使った自動コンテンツ戦略

• •Google Sheets API認証情報
• •Anthropic API Key

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      "parameters": {
        "jsonSchemaExample": "{\n\t\"query1\": [\"query\", \"évolution\"],\n    \"query2\": [\"query\", \"évolution\"],\n    \"query3\": [\"query\", \"évolution\"]\n}"
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      "parameters": {
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          "value": "claude-sonnet-4-20250514",
          "cachedResultName": "Claude Sonnet 4"
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          "value": "claude-sonnet-4-20250514",
          "cachedResultName": "Claude 4 Sonnet"
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          "name": "Anthropic account"
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    {
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      "name": "トレンド検索",
      "type": "n8n-nodes-serpapi.serpApi",
      "position": [
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      ],
      "parameters": {
        "q": "={{ $json.Query }}",
        "operation": "google_trends",
        "requestOptions": {},
        "additionalFields": {
          "hl": "fr",
          "geo": "FR",
          "date": "today 1-m",
          "data_type": "RELATED_QUERIES"
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        "serpApi": {
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      "id": "435c4eb8-6225-4566-af04-6baf8f6743a7",
      "name": "スプレッドシート作成",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        1584,
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      ],
      "parameters": {
        "title": "={{ new Date(new Date().setMonth(new Date().getMonth() - 1)).toLocaleDateString('fr-FR', { month: 'long' }).charAt(0).toUpperCase() + new Date(new Date().setMonth(new Date().getMonth() - 1)).toLocaleDateString('fr-FR', { month: 'long' }).slice(1) }} {{ $('Loop Over Items').item.json.Query }}",
        "options": {},
        "operation": "create",
        "documentId": {
          "__rl": true,
          "mode": "url",
          "value": "https://docs.google.com/spreadsheets/d/1z7iP_i98PT9BQuUypAi0c3NHkdhREEPJWkDjyj8Snfw"
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      "parameters": {
        "q": "={{ $json.Query }}",
        "operation": "google_news",
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          "hl": "fr"
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        "jsCode": "// Récupérer les données d'entrée\nconst inputData = $input.all()[0].json;\n\n// Extraire les résultats de news\nconst newsResults = inputData.news_results || [];\n\n// Trier par position (ordre croissant)\nconst sortedResults = newsResults.sort((a, b) => a.position - b.position);\n\n// Prendre les 3 premiers résultats et extraire seulement l'URL\nconst top3Results = sortedResults.slice(0, 3).map(result => ({\n  link: result.link\n}));\n\n// Retourner les 3 premiers résultats\nreturn top3Results.map(item => ({ json: item }));"
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      "name": "記事スクレイピング",
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        "operation": "scrape",
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      "credentials": {
        "firecrawlApi": {
          "id": "E34WDB80ik5VHjiI",
          "name": "Firecrawl account"
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        "content": "# Automated trend monitoring for content strategy\n\n## Who's it for\nContent creators, marketers, and social media managers who want to stay ahead of emerging trends and generate relevant content ideas based on data-driven insights.\n\n## What it does\nThis workflow automatically identifies trending topics related to your industry, collects recent news articles about these trends, and generates content suggestions. It transforms raw trend data into actionable editorial opportunities by analyzing search volume growth and current news coverage.\n\n## How it works\nThe workflow follows a three-step automation process:\n\nTrend Analysis: Examines searches related to your topics and identifies those with the strongest recent growth\nArticle Collection: Searches Google News for current articles about emerging trends and scrapes their full content\nContent Generation: Creates personalized content suggestions based on collected articles and trend data\n\nThe system automatically excludes geo-localized searches to provide a global perspective on trends, though this can be customized.\n\n## Requirements\n\nSerpAPI account (for trend and news data)\nFirecrawl API key (for scraping article content from Google News results)\nGoogle Sheets access\nAI model API key (for content analysis and recommendations - you can use any LLM provider you prefer)\n\n## How to set up\n### Step 1: Prepare your tracking sheet\nDuplicate this [Google Sheets template ](https://docs.google.com/spreadsheets/d/1z7iP_i98PT9BQuUypAi0c3NHkdhREEPJWkDjyj8Snfw)\nRename your copy and ensure it's accessible\n\n### Step 2: Configure API credentials\nBefore running the workflow, set up the following credentials in n8n:\n\nSerpAPI: For trend analysis and Google News search\nFirecrawl API: For scraping article content\nAI Model API: For content analysis and recommendations (Anthropic Claude, OpenAI GPT, or any other LLM provider)\nGoogle Sheets OAuth2: For accessing and updating your tracking spreadsheet\n\n### Step 3: Configure the workflow\nIn the \"Get Query\" node, paste your duplicated Google Sheets URL in the \"Document\" field\nIn your Google Sheet \"Query\" tab, enter the topics you want to monitor\n\n### Step 4: Customize language and location settings\nThe workflow is currently configured for French content and France location. You can modify these settings in the SerpAPI nodes:\n\nLanguage (hl): Change from \"fr\" to your preferred language code\nGeographic location (geo/gl): Change from \"FR\" to your target country code\nDate range: Currently set to \"today 1-m\" (last month) but can be adjusted\n\n### Step 5: Adjust filtering (optional)\nThe \"Sorting Queries\" node excludes geo-localized queries by default. You can modify the AI agent's instructions to include location-specific queries or change filtering criteria based on your requirements.\n### Step 6: Configure scheduling (optional)\nThe workflow includes an automated scheduler that runs monthly (1st day of each month at 8 AM). You can modify the cron expression 0 8 1 * * in the Schedule Trigger node to change:\n\nFrequency (daily, weekly, monthly)\nTime of execution\nDay of the month\n\n## How to customize the workflow\n\nChange trend count: The workflow processes up to 10 related queries per topic but filters them through AI to select the most relevant non-geolocalized ones\nAdjust article collection: Currently collects exactly 3 news articles per query for analysis\nContent style: Customize the AI prompts in content generation nodes to match your brand voice\nOutput format: Modify the Google Sheets structure to include additional data points\nAI model: Replace the Anthropic model with your preferred LLM provider\nScraping options: Configure Firecrawl settings to extract specific content elements from articles\n\n## Results interpretation\n\nFor each monitored topic, the workflow generates a separate sheet named by month and topic (e.g., \"January Digital Marketing\") containing:\nData structure (four columns):\n\nQuery: The trending search term ranked by growth\nÉvolution: Growth percentage over the last month\nNews: Links to 3 relevant news articles\nIdée: AI-generated content suggestions based on comprehensive article analysis\n\nThe workflow provides monthly retrospective analysis, helping you identify emerging topics before competitors and optimize your content calendar with high-potential subjects.\n\n## Workflow limitations\n\nProcesses up to 10 related queries per topic with AI filtering\nCollects exactly 3 news articles per query\nResults are automatically organized in monthly sheets\nRequires stable internet connection for API calls"
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    {
      "id": "8a61dc73-4805-434a-85b9-8088f25bb28d",
      "name": "クエリ取得",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
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        "sheetName": {
          "__rl": true,
          "mode": "name",
          "value": "Query"
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          "__rl": true,
          "mode": "url",
          "value": "=https://docs.google.com/spreadsheets/d/1z7iP_i98PT9BQuUypAi0c3NHkdhREEPJWkDjyj8Snfw"
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      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "wBRLUCktxqXE6DVJ",
          "name": "Google Sheets account"
        }
      },
      "typeVersion": 4.6
    },
    {
      "id": "5a61f1ba-682d-4ce9-9ab5-25b6278c6fd5",
      "name": "クエリ分類",
      "type": "n8n-nodes-base.code",
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      "parameters": {
        "jsCode": "// N8N Code Node - Create Nested Structure for Related Queries\n// This code creates a nested structure: Topic -> related queries\n// Get the input data (assuming it's the first item)\nconst inputData = $input.all()[0].json;\n// Initialize arrays to store extracted data\nlet relatedQueries = [];\nlet risingQueries = [];\ntry {\n  // Check if the response contains related_queries data\n  if (inputData.related_queries) {\n    \n    // Extract \"top\" related queries if they exist\n    if (inputData.related_queries.top) {\n      relatedQueries = \n        inputData.related_queries.top.map((query, index) => ({\n          query: query.query,\n          value: query.value,\n          extracted_value: query.extracted_value,\n          link: query.link,\n          serpapi_link: query.serpapi_link,\n          type: 'top',\n          rank: index + 1\n        }));\n    }\n    \n    // Extract \"rising\" related queries if they exist\n    if (inputData.related_queries.rising) {\n      risingQueries = \n        inputData.related_queries.rising.map((query, index) => ({\n          query: query.query,\n          value: query.value,\n          extracted_value: query.extracted_value,\n          link: query.link,\n          serpapi_link: query.serpapi_link,\n          type: 'rising',\n          rank: index + 1\n        }));\n    }\n  }\n  \n  // Combine all queries with their types\n  const allQueries = [...relatedQueries, ...risingQueries];\n  \n  // Sort by extracted_value (descending) to get top performers\n  const sortedQueries = allQueries.sort((a, b) => {\n    const aVal = typeof a.extracted_value === 'number' ? a.extracted_value : 0;\n    const bVal = typeof b.extracted_value === 'number' ? b.extracted_value : 0;\n    return bVal - aVal;\n  });\n  \n  // Get top 10 queries\n  const top10Queries = sortedQueries.slice(0, 10);\n  \n  // Return only top 10 queries\n  return [\n    {\n      json: {\n        topic: inputData.search_parameters?.q || 'Unknown',\n        top_10_queries: top10Queries\n      }\n    }\n  ];\n  \n} catch (error) {\n  // Handle errors gracefully\n  return [\n    {\n      json: {\n        error: 'Failed to extract and structure related queries data',\n        error_message: error.message,\n        topic: inputData.search_parameters?.q || 'Unknown'\n      }\n    }\n  ];\n}"
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      "parameters": {
        "text": "=Votre tâche est de sélectionner lister les requêtes qui correspond étroitement au créneau de \"{{ $('Loop Over Items').item.json.Query }}\" mais elle ne doit pas être géolocalisée, par exemple \"{{ $('Loop Over Items').item.json.Query }} Paris\" car nous ne voulons pas de sujets liés à la localisation.\n\nPour chaque requête indique son évolution en pourcentage (sans le +).\n\n{{ JSON.stringify($json.top_10_queries, null, 2) }}\n\n",
        "batching": {},
        "promptType": "define",
        "hasOutputParser": true
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      "name": "出力のテーブル化",
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        "jsCode": "// Récupérer les données d'entrée\nconst inputData = $input.all()[0].json.output;\n\n// Initialiser le tableau de sortie\nlet restructuredData = [];\n\n// Parcourir chaque query dans l'objet\nObject.keys(inputData).forEach(key => {\n  const queryData = inputData[key];\n  \n  restructuredData.push({\n    Query: queryData[0],           // Le nom de la requête\n    Évolution: queryData[1],       // Le pourcentage d'évolution\n    News: \"\",                       // Colonne vide pour l'instant\n    Idée: \"\"                        // Colonne vide pour l'instant\n  }); \n});\n\n// Retourner le tableau restructuré\nreturn restructuredData.map(item => ({ json: item }));"
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      "parameters": {
        "columns": {
          "value": {},
          "schema": [],
          "mappingMode": "autoMapInputData",
          "matchingColumns": [],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
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        "options": {},
        "operation": "append",
        "sheetName": {
          "__rl": true,
          "mode": "name",
          "value": "={{ new Date(new Date().setMonth(new Date().getMonth() - 1)).toLocaleDateString('fr-FR', { month: 'long' }).charAt(0).toUpperCase() + new Date(new Date().setMonth(new Date().getMonth() - 1)).toLocaleDateString('fr-FR', { month: 'long' }).slice(1) }} {{ $('Loop Over Items').item.json.Query }}"
        },
        "documentId": {
          "__rl": true,
          "mode": "url",
          "value": "https://docs.google.com/spreadsheets/d/1z7iP_i98PT9BQuUypAi0c3NHkdhREEPJWkDjyj8Snfw"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "wBRLUCktxqXE6DVJ",
          "name": "Google Sheets account"
        }
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      "typeVersion": 4.6
    },
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      "name": "クエリフィルター",
      "type": "n8n-nodes-base.set",
      "position": [
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      "parameters": {
        "include": "selected",
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "245bf100-21a3-4de2-9b92-74e09e3347a7",
              "name": "Query",
              "type": "string",
              "value": "={{ $json.Query }}"
            }
          ]
        },
        "includeOtherFields": true
      },
      "typeVersion": 3.4
    },
    {
      "id": "e51a3336-4a8a-407e-9529-37658bf74132",
      "name": "データコンパイル",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        2928,
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      ],
      "parameters": {
        "options": {},
        "aggregate": "aggregateAllItemData"
      },
      "typeVersion": 1
    },
    {
      "id": "e7aa0bac-7576-4d55-b0be-411cb7c60b7f",
      "name": "記事分析",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
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      "parameters": {
        "text": "=Source 1\n\n{{ $json.data[0].data.markdown }}\n\nSource 2 \n\n{{ $json.data[1].data.markdown }}\n\nSource 3\n\n{{ $json.data[2].data.markdown }}\n",
        "batching": {},
        "messages": {
          "messageValues": [
            {
              "message": "=Voilà le contenu de 3 article sur le thème \"{{ $('Loop Over Items').item.json.Query }}\", peux tu les analyser en en déduire 3 idée d'article de blog SEO avec à chaque fois une proposition de mot clé associé.\n\nExemple : \nThème de l'article 1, proposition de mot clé 1\nThème de l'article 2, proposition de mot clé 2\nThème de l'article 3, proposition de mot clé 3\n\nNe fais pas d'introduction ou de conclusion à ta réponse répond simplement à la requête"
            }
          ]
        },
        "promptType": "define"
      },
      "typeVersion": 1.7
    },
    {
      "id": "c3918ddb-f7e6-4021-8c0d-71f36e5ae5ff",
      "name": "記事データ追加",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        3552,
        288
      ],
      "parameters": {
        "columns": {
          "value": {
            "News": "={{ $('Search GNews').item.json.news_results[0].link }}\n{{ $('Search GNews').item.json.news_results[1].link }}\n{{ $('Search GNews').item.json.news_results[2].link }}",
            "Idée": "={{ $json.text }}",
            "Query": "={{ $('Loop Over Items1').item.json.Query }}"
          },
          "schema": [
            {
              "id": "Query",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Query",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Évolution",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Évolution",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "News",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "News",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Idée",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Idée",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "row_number",
              "type": "string",
              "display": true,
              "removed": false,
              "readOnly": true,
              "required": false,
              "displayName": "row_number",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "Query"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {},
        "operation": "update",
        "sheetName": {
          "__rl": true,
          "mode": "name",
          "value": "={{ new Date(new Date().setMonth(new Date().getMonth() - 1)).toLocaleDateString('fr-FR', { month: 'long' }).charAt(0).toUpperCase() + new Date(new Date().setMonth(new Date().getMonth() - 1)).toLocaleDateString('fr-FR', { month: 'long' }).slice(1) }} {{ $('Loop Over Items').item.json.Query }}"
        },
        "documentId": {
          "__rl": true,
          "mode": "url",
          "value": "https://docs.google.com/spreadsheets/d/1z7iP_i98PT9BQuUypAi0c3NHkdhREEPJWkDjyj8Snfw"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "wBRLUCktxqXE6DVJ",
          "name": "Google Sheets account"
        }
      },
      "typeVersion": 4.6
    }
  ],
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  "connections": {
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