// Make.com Scenario Configuration (JSON Blueprint)
{
  "name": "AI Content Pipeline",
  "flow": [
    {
      "id": 1,
      "module": "rss:ActionReadFeeds",
      "parameters": {
        "url": "https://news.ycombinator.com/rss",
        "maximum": 5
      }
    },
    {
      "id": 2,
      "module": "openai:CreateChatCompletion",
      "parameters": {
        "model": "gpt-4",
        "messages": [
          {
            "role": "system",
            "content": "Extract 3 key topics from this article for further research"
          },
          {
            "role": "user",
            "content": "{{1.title}} - {{1.description}}"
          }
        ],
        "temperature": 0.7
      }
    },
    {
      "id": 3,
      "module": "perplexity:Search",
      "parameters": {
        "query": "{{2.choices[0].message.content}}",
        "search_depth": "comprehensive"
      }
    },
    {
      "id": 4,
      "module": "anthropic:CreateMessage",
      "parameters": {
        "model": "claude-3-opus",
        "max_tokens": 2000,
        "messages": [
          {
            "role": "user",
            "content": "Write a comprehensive article about: {{2.topics}}\n\nResearch: {{3.results}}"
          }
        ]
      }
    },
    {
      "id": 5,
      "module": "builtin:BasicRouter",
      "routes": [
        {
          "condition": "{{length(4.content) > 500}}",
          "modules": [
            {
              "module": "wordpress:CreatePost",
              "parameters": {
                "title": "{{2.title}}",
                "content": "{{4.content}}",
                "status": "publish"
              }
            }
          ]
        }
      ]
    }
  ]
}
                

// Advanced Support Workflow
Scenario Components:

1. TRIGGER MODULES
   - Email (IMAP Watch)
   - Slack (Watch Messages)
   - WhatsApp Business (Webhook)
   - Website Chat (Custom Webhook)

2. PROCESSING CHAIN
   // Sentiment Analysis
   OpenAI Module: {
     prompt: "Analyze sentiment and urgency: {{message}}",
     response_format: {
       sentiment: "positive|neutral|negative",
       urgency: "low|medium|high|critical",
       category: "technical|billing|general"
     }
   }
   
   // Knowledge Base Search
   Pinecone Module: {
     action: "similarity_search",
     query: "{{message}}",
     top_k: 5,
     threshold: 0.8
   }
   
   // Response Generation
   Claude Module: {
     system: "You are a helpful support agent. Use this context: {{pinecone.results}}",
     user: "{{message}}",
     max_tokens: 500
   }

3. ROUTING LOGIC
   Router: {
     route_1: {
       condition: "urgency == 'critical'",
       action: [
         "Create Urgent Ticket",
         "Send Slack Alert to Team",
         "Send Auto-Response with ETA"
       ]
     },
     route_2: {
       condition: "sentiment == 'negative' AND category == 'billing'",
       action: [
         "Create High Priority Ticket",
         "Assign to Billing Team",
         "Generate Personalized Response"
       ]
     },
     route_3: {
       condition: "confidence > 0.9",
       action: [
         "Send AI Response",
         "Log to CRM",
         "Update Knowledge Base"
       ]
     },
     default: {
       action: [
         "Create Standard Ticket",
         "Send Acknowledgment",
         "Add to Queue"
       ]
     }
   }

4. DATA OPERATIONS
   // Aggregate daily metrics
   DataStore: {
     operation: "increment",
     key: "daily_{{category}}_count",
     value: 1
   }
   
   // Calculate response time
   Tools.calculateResponseTime({
     start: "{{ticket.created_at}}",
     end: "{{now}}"
   })
                

// Complex Data Processing Scenario

1. DATA COLLECTION
   // Watch Google Sheets for new data
   GoogleSheets.watchRows({
     spreadsheetId: "{{env.SHEET_ID}}",
     range: "Data!A:Z"
   })

2. DATA TRANSFORMATION
   // Iterator: Process each row
   Iterator.forEach(row => {
     // Parse and validate data
     Tools.parseJSON(row.data)
     
     // Data enrichment
     Clearbit.enrichCompany({
       domain: row.company_domain
     })
     
     // Calculate metrics
     Tools.aggregate({
       operation: "sum|avg|count",
       groupBy: row.category
     })
   })

3. AI ANALYSIS
   // Generate insights with GPT-4
   OpenAI.analyze({
     prompt: `
       Analyze this dataset and provide:
       1. Key trends and patterns
       2. Anomalies or outliers
       3. Predictive insights
       4. Actionable recommendations
       
       Data: {{aggregated_data}}
     `,
     temperature: 0.3,
     max_tokens: 2000
   })

4. VISUALIZATION
   // Create charts with QuickChart
   QuickChart.create({
     type: "bar|line|pie",
     data: {{processed_data}},
     options: {
       title: "Monthly Analytics",
       responsive: true
     }
   })

5. REPORTING
   // Generate PDF report
   PDFGenerator.create({
     template: "analytics_template",
     data: {
       insights: {{ai_analysis}},
       charts: {{chart_urls}},
       raw_data: {{data_summary}}
     }
   })
   
   // Distribute reports
   Email.send({
     to: "{{stakeholders}}",
     subject: "Weekly Analytics Report",
     attachments: [{{pdf_report}}]
   })