Automatisierte App-Analyse und ASO-Berichtsgenerator

• •Telegram Bot Token
• •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich

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  "name": "Automated App Analysis & ASO Report Generator",
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      "name": "Bei Formularübermittlung",
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        "formTitle": "ASO Report",
        "formFields": {
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              "fieldLabel": "Play Store URL",
              "requiredField": true
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      "name": "HTTP Request",
      "type": "n8n-nodes-base.httpRequest",
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      "parameters": {
        "url": "=https://app.sensortower.com/api/android/apps/{{ $json.packageName }}?country=US",
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        "text": "=Create an ASO Report based on the following data. \n\nThe report must include:\n📱 App Overview (Name, Publisher, Category, Installs, Rating, In-app purchases, Last update)  \n⭐ User Ratings & Reviews (average rating, rating count, summary of featured reviews with sentiment highlights)  \n📊 Competitor Analysis (App Name, Publisher, Rating, Installs)  \n📈 Market Insights (downloads & revenue last month, top countries if available)  \n💡 Recommendations (actionable suggestions to improve monetization, retention, competitiveness)  \n\n---\n\n### Input Data:\n\nApp Name: {{ $json.appInfo.name }}\nApp ID: {{ $json.appInfo.app_id }}\nPublisher: {{ $json.appInfo.publisher }}\nCategory: {{ $json.appInfo.category }}\nInstalls: {{ $json.appInfo.installs }}\nRating: {{ $json.appInfo.rating }}\nRating Count: {{ $json.appInfo.rating_count }}\nIn-App Purchases: {{ $json.appInfo.in_app_purchases }}\nLast Update: {{ $json.appInfo.last_update }}\n\nFeatured Reviews:\n{{ $json.reviewsText }}\n\nCompetitors:\n{{ $json.competitorsText }}\n\nMarket Insights:\nDownloads Last Month: {{ $json.market.downloads }}\nRevenue Last Month: {{ $json.market.revenue }} {{ $json.market.currency }}\n",
        "batching": {},
        "messages": {
          "messageValues": [
            {
              "message": "=You are an ASO (App Store Optimization) Analyst. \nGenerate an executive report in plain text only.\nDo not use HTML or Markdown. \nFormat the report neatly using emojis as section headers and bullet points. \nAlways write in business English.\n"
            }
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      "name": "Code",
      "type": "n8n-nodes-base.code",
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      "parameters": {
        "jsCode": "// n8n Code Node: Parse + Format App Intelligence Data\n// Input: JSON from HTTP Request (SensorTower / similar API)\n// Output: Summary data ready for LLM (formatted text)\n\nconst data = items[0].json;\n\n// --- Get basic info ---\nconst appInfo = {\n  name: data.name,\n  app_id: data.app_id,\n  publisher: data.publisher_name,\n  rating: data.rating,\n  rating_count: data.rating_count,\n  installs: data.installs,\n  category: data.categories?.map(c => c.name).join(\", \") || \"N/A\",\n  in_app_purchases: data.top_in_app_purchases?.US || \"None\",\n  last_update: data.current_version,\n};\n\n// --- Get 3 latest featured reviews ---\nconst reviews = (data.featured_reviews || [])\n  .slice(0, 3)\n  .map(r => ({\n    user: r.username,\n    date: r.date,\n    rating: r.rating,\n    content: r.content,\n    tags: r.tags || []\n  }));\n\n// Format reviews into clean string\nconst reviewsText = reviews.map(r => \n  `- User: ${r.user}\\n  Date: ${r.date}\\n  Rating: ${r.rating}\\n  Content: ${r.content}\\n  Tags: ${r.tags.join(\", \")}`\n).join(\"\\n\\n\");\n\n// --- Get top 3 competitors ---\nconst competitors = (data.related_apps || [])\n  .slice(0, 3)\n  .map(app => ({\n    name: app.name,\n    publisher: app.publisher_name,\n    rating: app.rating,\n    installs: app.rating_count,\n  }));\n\n// Format competitors into clean string\nconst competitorsText = competitors.map(c => \n  `- Name: ${c.name}\\n  Publisher: ${c.publisher}\\n  Rating: ${c.rating}\\n  Installs: ${c.installs}`\n).join(\"\\n\\n\");\n\n// --- Get market data (downloads & revenue last month) ---\nconst market = {\n  downloads: data.worldwide_last_month_downloads?.value || 0,\n  revenue: data.worldwide_last_month_revenue?.value || 0,\n  currency: data.worldwide_last_month_revenue?.currency || \"USD\"\n};\n\n// --- Return final data for LLM ---\nreturn [\n  {\n    json: {\n      appInfo,\n      reviewsText,\n      competitorsText,\n      market\n    }\n  }\n];\n"
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        "text": "=📄 New document for app analysis: {{ $('Code').item.json.appInfo.name }}\n🔗 Document link: https://docs.google.com/document/d/{{ $json.documentId }}/edit?tab=t.0\n",
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        "jsCode": "// Input from form trigger\nconst url = items[0].json[\"Play Store URL\"];\n\n// Find the 'id=' parameter in the URL\nlet packageName = null;\ntry {\n  const urlObj = new URL(url);\n  packageName = urlObj.searchParams.get(\"id\");\n} catch (e) {\n  // fallback manual if not a valid URL\n  const match = url.match(/id=([^&]+)/);\n  packageName = match ? match[1] : null;\n}\n\nreturn [\n  {\n    json: {\n      packageName\n    }\n  }\n];\n"
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        "content": "# Automated App Analysis & ASO Report Generator\n\nThis workflow automates the process of analyzing a mobile app from the Google Play Store and generating a professional **ASO (App Store Optimization) Report**.  \nSimply submit a Play Store URL, and the workflow will fetch app intelligence data, parse it, run an AI-based analysis, and deliver a formatted report in Google Docs. A Telegram notification with the document link ensures you never miss a new report.\n\n\n## ✨ Key Features\n- **Form Input Trigger** – Start the workflow by submitting a Play Store URL.  \n- **Automated Data Retrieval** – Uses HTTP request to fetch app intelligence (via SensorTower or similar APIs).  \n- **Smart Data Parsing** – Extracts essential app details, competitor insights, reviews, downloads, and revenue data.  \n- **AI-Powered ASO Report** – Generates a professional analysis using LLM (Gemini via OpenRouter) with structured sections:  \n  - 📱 App Overview  \n  - ⭐ User Ratings & Reviews  \n  - 📊 Competitor Analysis  \n  - 📈 Market Insights  \n  - 💡 Actionable Recommendations  \n- **Google Docs Integration** – Creates and updates a Google Doc with the generated report.  \n- **Instant Notification** – Sends a Telegram message with the app report link for quick access.  \n\n---\n\n## 🔐 Required Credentials\nTo run this workflow, you'll need:\n- **SensorTower API (or alternative App Intelligence API)** – for app details, reviews, competitors, and market data.  \n- **OpenRouter API** – to access LLM model.  \n- **Google Docs OAuth2** – to create and update the ASO report in Google Docs.  \n- **Telegram API** – for instant notifications with the report link.  \n\n---\n\n## 🎁 Benefits\n- **Save Time** – Automates the manual process of app research and reporting.  \n- **Consistent Reporting** – Ensures every report follows a professional structure with clear sections.  \n- **Actionable Insights** – Get AI-generated recommendations to improve app performance and competitiveness.  \n- **Collaboration-Ready** – Reports are stored in Google Docs for easy sharing and editing.  \n- **Real-Time Alerts** – Stay updated via Telegram whenever a new report is generated.  \n\n---\n"
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        "content": "- The workflow starts when a user submits a form with the app's Play Store URL\n- Extracts the package name (the id= parameter) from the submitted URL.\n- Uses the package name to call the SensorTower API (or a similar app intelligence API) and fetch details such as the app's name, publisher, category, rating, and more."
      },
      "typeVersion": 1
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        "content": "- Parses the raw JSON and formats it into structured text, including app details, featured reviews, competitor data, and market insights.\n- Sends the structured data to an AI model with a prompt that instructs it to create a neatly formatted ASO report.\n- Automatically creates a new Google Doc titled with the app's name and stores the generated report inside the specified folder."
      },
      "typeVersion": 1
    },
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      "position": [
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        "content": "- Inserts the ASO report text into the previously created Google Doc.\n- Once the document is updated, a Telegram notification is sent to the specified user."
      },
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    }
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