This n8n automation finds recent LinkedIn job posts (via SerpAPI/Google search), scrapes each post for job + recruiter contact info, stores results in Google Sheets, extracts the candidate’s resume from Google Drive, uses an LLM (Google Gemini) to generate a personalized application email, and then sends that email through Gmail.

High-level flow

• Manual trigger starts the flow.

• Query SerpAPI (Google search) for LinkedIn posts matching your hiring keywords and location.

• Extract only LinkedIn post URLs.

• Fetch each LinkedIn post HTML and run a parsing function to extract title, company, JD, location, email, phone.

• Save or update each lead into a Google Sheet (email used as unique key).

• Download & extract candidate resume from Google Drive (text extraction).

• Merge resume text with the scraped job info and call an AI Agent (Google Gemini) to create an HTML-formatted email JSON.

• Filter flows without a valid recruiter email.

• Send the generated email via Gmail.

• Looping and batching are used to process many posts safely.

N8N Workflow :

Node-by-node explanation

1. Manual Trigger

• Purpose: Start pipeline manually for testing or ad-hoc runs.

• Notes:You can replace this with automatic scheduled trigger(Currently its manual trigger).

2. Fetch Google Results2 (HTTP Request → SerpAPI)

   • Purpose: Query SerpAPI with a Google search for site:linkedin.com/posts hiring Node.js typescript in a target location (Pune) and time filter (tbs=qdr:m = last month).

   • Why SerpAPI: Returns structured search results (organic_results) that are easier to parse than scraping Google directly.

   • Key param: api_key=<APIKEY>

   • SerpApi : Link.

3. Extract LinkedIn URLs2

   • Purpose: Filter organic_results to only items whose link includes linkedin.com/posts.

   • Why: Search results include many domains — this ensures we only process real LinkedIn post links.

4. Fetch LinkedIn Post HTML2 (HTTP Request)

   • Purpose: Request the LinkedIn post page and return raw HTML (responseFormat: string) for parsing.

   • Notes: LinkedIn may return dynamic content; raw HTML must have the meta tags used in parsing (og:description, og:title). Handle potential bot blocks or missing meta tags.

5. Loop Over Items (SplitInBatches)

   • Purpose: Prevent rate-limit issues and control concurrency. Processes items in small batches.

   • Why: Reduces risk of being blocked by LinkedIn/SerpAPI and avoids hitting Gmail/Sheets quotas.

6. Extract Recruiter Info2 (Function — large parser)

   • Purpose: This is the heart of the scraper. It:

     • Normalizes obfuscated Unicode characters (e.g., ＠ / bold Unicode).

     • Extracts og:description and og:title.

     • Derives job title (multiple fallbacks), company name, job description, location, email, and phone.

     • Filters results down to items that have a valid email before returning them.

   • Important details in the function:

     • Multiple title-extraction strategies (first-line patterns + og:title + fallback).

     • Email extraction checks mailto%3A links and raw text with regex.

     • Phone cleanup enforces numeric length (Currently not in work avoid using phone numbers).

     • Returns only items with extractedEmail (prevents sending blind emails).

7. Append or update row in sheet (Google Sheets)

   • Purpose: Append new leads or update existing rows by matching on email to avoid duplicates.

   • Why: Acts as a persistent job-leads database and audit trail.

   • Schema mapped columns: Title, companyName, JD, email, phoneNumber.

   • Note: Use robust column mapping and escape/trim JDs because job descriptions can be long or include HTML.

8. Download file (Google Drive) → Extract from File (extractFromFile)

   • • Purpose: Download the candidate’s resume (PDF) from Drive and extract text (operation pdf).

       • How it integrates: The extracted resume text is merged with scraped job data later so the AI can craft a targeted mail.

       • Important: The workflow uses a fixed fileId for the resume for multi-candidate flows, make this dynamic or pull from a folder.

       • Notes: This node require OAuth setup and has sending quotas .

9. Merge / Merge1 (Merge nodes)

   • Purpose: Combine the resume text and the scraped job/recruiter info into one data object for the AI.

   • Details: Merge ensures AI receives both the job context and the candidate’s resume content.

10. AI Agent (Langchain agent) + Google Gemini Chat Model

    • Purpose: Use the LLM to create a short, professional, personalized email (JSON output with mailBody, mailSubject, recruiterEmail).

    • Prompting approach: The prompt defines JSON output and HTML formatting for mailBody (use <br> for breaks). The Structured Output Parser enforces schema.

    • Why LLM: Generates human-like, role-specific emails that reference resume highlights and job details.

11. Structured Output Parser

    • Purpose: Validates and parses the LLM response to ensure it fits the expected JSON schema. This avoids malformed outputs being sent.

12. Filter

    • Purpose: Ensures only outputs with a non-empty recruiterEmail proceed to sending.

    • Why necessary: LLM or parser might produce incomplete outputs; filter prevents sending to blank/invalid addresses.

13. Send a message (Gmail)

    • Purpose: Send the generated HTML email to the recruiter’s email address with mailSubject and mailBody from the LLM output.

    • Notes: Gmail node require OAuth setup and has sending quotas .

Suggested improvements & advanced options

• Auto-schedule: Replace Manual Trigger with Cron for regular scanning (e.g., hourly or daily).

• Human-in-the-loop: Add a Slack/Telegram notification for approval before sending batches of emails.

• Retry & backoff: Add retry logic and exponential backoff for failed HTTP requests.

• Rate-limit aware queueing: Integrate a queue (Redis or built-in n8n) if volume grows.

Final summary (TL;DR)

This n8n workflow automates job lead discovery and outreach by:

1. Searching Google (via SerpAPI) for LinkedIn hiring posts.

2. Scraping each post to extract job + recruiter contact info.

3. Storing leads in Google Sheets (deduped by email).

4. Downloading and extracting candidate resume from Google Drive.

5. Using an LLM to craft a tailored HTML email that includes resume info.

6. Automatically sending the email via Gmail to recruiters that have valid emails.

It combines scraping, parsing, LLM generation, persistence, and email delivery and with small hardening steps (rate limits, validation, security) becomes a reliable automated outreach pipeline.

Copy this JSON into n8n and speed up your job applications instantly! 🚀

{
  "nodes": [
    {
      "parameters": {},
      "name": "Manual Trigger",
      "type": "n8n-nodes-base.manualTrigger",
      "typeVersion": 1,
      "position": [
        -2848,
        800
      ],
      "id": "f3fc205e-7b4c-42ef-b789-5ac95f2f3a37"
    },
    {
      "parameters": {
        "url": "=https://serpapi.com/search.json?q=site:linkedin.com/posts+hiring+Node.js+typescript&location=Pune,+India&tbs=qdr:m&filter=0&api_key=<APIKEY>",
        "options": {}
      },
      "name": "Fetch Google Results2",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 1,
      "position": [
        -2592,
        800
      ],
      "id": "71298061-b7b0-45ff-9e52-577126f24e58"
    },
    {
      "parameters": {
        "functionCode": "const results = $json.organic_results || []; return results.filter(r => r.link.includes('linkedin.com/posts')).map(r => ({ json: { link: r.link } }));"
      },
      "name": "Extract LinkedIn URLs2",
      "type": "n8n-nodes-base.function",
      "typeVersion": 1,
      "position": [
        -2352,
        800
      ],
      "id": "6537628c-9abc-4bdf-b0aa-a61f277fde59"
    },
    {
      "parameters": {
        "url": "={{$json.link}}",
        "responseFormat": "string",
        "options": {}
      },
      "name": "Fetch LinkedIn Post HTML2",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 1,
      "position": [
        -2096,
        800
      ],
      "id": "72b62222-01d2-4534-bc6d-5533c68087e7"
    },
    {
      "parameters": {
        "functionCode": "const results = [];\n\n// Function to normalize obfuscated Unicode characters (e.g., bold letters in emails) to ASCII\nconst unicodeToAsciiMap = {\n    '𝐚': 'a', '𝐛': 'b', '𝐜': 'c', '𝐝': 'd', '𝐞': 'e', '𝐟': 'f', '𝐠': 'g', '𝐡': 'h', '𝐢': 'i', '𝐣': 'j', '𝐤': 'k', '𝐥': 'l', '𝐦': 'm', \n    '𝐧': 'n', '𝐨': 'o', '𝐩': 'p', '𝐪': 'q', '𝐫': 'r', '𝐬': 's', '𝐭': 't', '𝐮': 'u', '𝐯': 'v', '𝐰': 'w', '𝐱': 'x', '𝐲': 'y', '𝐳': 'z',\n    '＠': '@', '．': '.'\n};\n\nfunction normalizeUnicode(text) {\n    if (!text) return text;\n    let result = text;\n    for (const [unicode, ascii] of Object.entries(unicodeToAsciiMap)) {\n        result = result.replace(new RegExp(unicode, 'g'), ascii);\n    }\n    return result.replace(/[\\u200B-\\u200D\\uFEFF]/g, ''); \n}\n\n// Loop over every item in the incoming array\nfor (const item of items) {\n    const htmlContent = item.json.data;\n\n    // --- 1. Pre-process HTML ---\n    let cleanedHtmlContent = htmlContent.replace(/urn:li:activity:\\d{18,}/g, '');\n    \n    // --- 2. Extract Full Description and Normalize ---\n    const descriptionMatch = cleanedHtmlContent.match(/<meta property=\"og:description\" content=\"(.*?)\"/s);\n    let fullDescription = descriptionMatch && descriptionMatch[1] ? descriptionMatch[1].trim() : \"\";\n    fullDescription = fullDescription.replace(/&#10;/g, '\\n');\n    \n    const normalizedDescription = normalizeUnicode(fullDescription);\n    const descriptionLines = normalizedDescription.split('\\n').map(line => line.trim()).filter(line => line.length > 0);\n    const firstLine = descriptionLines.length > 0 ? descriptionLines[0] : '';\n    \n    let jobTitle = null;\n\n    // --- 3. Extract Job Title (IMPROVED AND SAFELY GUARDED LOGIC) ---\n    \n    // Priority 1: Search common opening patterns in the first line\n    const firstLineTitleRegex = /(?:🚀\\s*We Are Hiring\\s*\\||📢\\s*We’re hiring)\\s*(.*?)(?:\\n|$)/i;\n    const titleMatch1 = firstLine.match(firstLineTitleRegex);\n    if (titleMatch1 && titleMatch1[1]) {\n        jobTitle = titleMatch1[1].trim();\n    }\n    \n    // Priority 2: Fallback to <meta property=\"og:title\"> and clean it up\n    if (!jobTitle) {\n        const ogTitleMatch = cleanedHtmlContent.match(/<meta property=\"og:title\" content=\"(.*?)\">/);\n        \n        // Initialize metaTitle to an empty string for safety\n        let metaTitle = ogTitleMatch && ogTitleMatch[1] ? ogTitleMatch[1] : '';\n        \n        if (metaTitle) {\n            // Remove hashtags, company names, and separators\n            metaTitle = metaTitle.replace(/#\\w+/g, '').replace(/\\|.*?LLC/i, '').replace(/\\|/g, '').trim();\n            // Remove leading/trailing dashes/spaces\n            metaTitle = metaTitle.replace(/^[-\\s]+|[-\\s]+$/g, '').trim();\n        }\n        \n        // Now it is safe to check the length, as metaTitle is guaranteed to be a string\n        if (metaTitle.length > 5) {\n            jobTitle = metaTitle;\n        }\n    }\n    \n    // Priority 3: Fallback to the entire first line\n    if (!jobTitle && firstLine.length > 0 && firstLine.length < 150) {\n        jobTitle = firstLine;\n    }\n\n\n    // --- 4. Extract Company Name ---\n    const companyTitleMatch = cleanedHtmlContent.match(/<meta property=\"og:title\" content=\".*? \\| (.*?)\">/);\n    let companyName = companyTitleMatch && companyTitleMatch[1] ? companyTitleMatch[1].trim() : null;\n    \n    if (!companyName) {\n        const companyDescMatch = normalizedDescription.match(/📍 (.*?)\\n/);\n        companyName = companyDescMatch && companyDescMatch[1] ? companyDescMatch[1].trim().split(',')[0] : null;\n    }\n\n    // --- 5. Extract Job Description ---\n    let jobDescription = fullDescription;\n    // Safely remove the header lines if a title was found\n    if (jobTitle && descriptionLines.length > 1) {\n         // Use the end of the second line as the starting point for the description\n         const secondLine = descriptionLines[1];\n         const headerEndIndex = fullDescription.indexOf(secondLine) + secondLine.length;\n         if (headerEndIndex > 0) {\n            jobDescription = fullDescription.substring(headerEndIndex).trim();\n         }\n    }\n\n    // --- 6. Extract Job Location ---\n    let jobLocation = null;\n    const locationKeywordsRegex = /Location:\\s*(.*?)(?:\\n|Type:|Apply Now:)/i;\n    const locationKeywordMatch = jobDescription.match(locationKeywordsRegex);\n\n    if (locationKeywordMatch && locationKeywordMatch[1]) {\n        jobLocation = locationKeywordMatch[1].trim();\n    } \n    \n    if (!jobLocation) {\n        const locationLineMatch = normalizedDescription.match(/📍 (.*?)\\n/);\n        if (locationLineMatch && locationLineMatch[1] && locationLineMatch[1].includes(',')) {\n            jobLocation = locationLineMatch[1].split(',').slice(1).join(',').trim();\n            if (jobLocation === '') jobLocation = null;\n        } else if (locationLineMatch && locationLineMatch[1]) {\n            if (locationLineMatch[1] !== companyName) {\n                 jobLocation = locationLineMatch[1].trim();\n            }\n        }\n    }\n\n\n    // --- 7. Extract Email ---\n    let extractedEmail = null;\n\n    // Priority 1: Check the mailto link's URL-encoded value\n    const mailtoRegex = /mailto%3A(.*?)[&\"']/i;\n    const mailtoMatch = cleanedHtmlContent.match(mailtoRegex);\n    if (mailtoMatch && mailtoMatch[1]) {\n        extractedEmail = decodeURIComponent(mailtoMatch[1]);\n    }\n\n    // Priority 2: Run on the **normalized** full description content\n    if (!extractedEmail && normalizedDescription) {\n        const plainEmailRegex = /([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,6})/g;\n        const plainEmailMatches = normalizedDescription.match(plainEmailRegex);\n        \n        if (plainEmailMatches && plainEmailMatches.length > 0) {\n            extractedEmail = [...new Set(plainEmailMatches)][0];\n        }\n    }\n\n    // --- 8. Extract Phone Number ---\n    let extractedNumber = null;\n    const phoneRegex = /(\\+?\\d{1,4}[-.\\s()]?(\\d{2,4})[-.\\s()]?(\\d{3,4})[-.\\s()]?(\\d{4,5}))/g;\n    const potentialNumbers = cleanedHtmlContent.match(phoneRegex);\n\n    if (potentialNumbers) {\n        const uniqueNumbers = [...new Set(potentialNumbers)];\n        for (let num of uniqueNumbers) {\n            const cleanedNum = num.replace(/[^0-9]/g, '');\n            if (cleanedNum.length >= 8 && cleanedNum.length <= 15) {\n                extractedNumber = num.trim();\n                break;\n            }\n        }\n    }\n\n    // --- 9. Filter Logic: Only push results that have an email ---\n    if (extractedEmail) {\n        results.push({\n            json: {\n                jobTitle: jobTitle,\n                companyName: companyName,\n                jobDescription: jobDescription,\n                jobLocation: jobLocation,\n                emailjs: extractedEmail,\n                phoneNumber: extractedNumber\n            }\n        });\n    }\n}\n\nreturn results;"
      },
      "name": "Extract Recruiter Info2",
      "type": "n8n-nodes-base.function",
      "typeVersion": 1,
      "position": [
        -1776,
        544
      ],
      "id": "41e0f9b7-85ef-428b-a14e-e67a8128356b"
    },
    {
      "parameters": {
        "options": {}
      },
      "type": "n8n-nodes-base.splitInBatches",
      "typeVersion": 3,
      "position": [
        -1872,
        1056
      ],
      "id": "10512a94-50bd-4146-9720-482b18a2fe39",
      "name": "Loop Over Items"
    },
    {
      "parameters": {},
      "type": "n8n-nodes-base.noOp",
      "name": "Replace Me",
      "typeVersion": 1,
      "position": [
        -1104,
        1008
      ],
      "id": "e1b08df7-c3a9-4c7e-82a9-4383e5882330"
    },
    {
      "parameters": {
        "operation": "appendOrUpdate",
        "documentId": {
          "__rl": true,
          "value": "1cd77WEywNQYJlE_YAX8Q0mPepXQ94cYSDlak1g7W4jg",
          "mode": "list",
          "cachedResultName": "post scraper",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1cd77WEywNQYJlE_YAX8Q0mPepXQ94cYSDlak1g7W4jg/edit?usp=drivesdk"
        },
        "sheetName": {
          "__rl": true,
          "value": "gid=0",
          "mode": "list",
          "cachedResultName": "Sheet1",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1cd77WEywNQYJlE_YAX8Q0mPepXQ94cYSDlak1g7W4jg/edit#gid=0"
        },
        "columns": {
          "mappingMode": "defineBelow",
          "value": {
            "Title": "={{ $json.jobTitle }}",
            "companyName": "={{ $json.companyName }}",
            "JD": "={{ $json.jobDescription }}",
            "email": "={{ $json.emailjs }}",
            "phoneNumber": "={{ $json.phoneNumber }}"
          },
          "matchingColumns": [
            "email"
          ],
          "schema": [
            {
              "id": "Title",
              "displayName": "Title",
              "required": false,
              "defaultMatch": false,
              "display": true,
              "type": "string",
              "canBeUsedToMatch": true,
              "removed": false
            },
            {
              "id": "companyName",
              "displayName": "companyName",
              "required": false,
              "defaultMatch": false,
              "display": true,
              "type": "string",
              "canBeUsedToMatch": true
            },
            {
              "id": "JD",
              "displayName": "JD",
              "required": false,
              "defaultMatch": false,
              "display": true,
              "type": "string",
              "canBeUsedToMatch": true
            },
            {
              "id": "email",
              "displayName": "email",
              "required": false,
              "defaultMatch": false,
              "display": true,
              "type": "string",
              "canBeUsedToMatch": true,
              "removed": false
            },
            {
              "id": "phoneNumber",
              "displayName": "phoneNumber",
              "required": false,
              "defaultMatch": false,
              "display": true,
              "type": "string",
              "canBeUsedToMatch": true
            },
            {
              "id": "emailBody",
              "displayName": "emailBody",
              "required": false,
              "defaultMatch": false,
              "display": true,
              "type": "string",
              "canBeUsedToMatch": true
            }
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {}
      },
      "type": "n8n-nodes-base.googleSheets",
      "typeVersion": 4.7,
      "position": [
        -1392,
        624
      ],
      "id": "7d3da9ee-efb0-46ca-8b42-e4c87415ce51",
      "name": "Append or update row in sheet",
      "alwaysOutputData": false
    },
    {
      "parameters": {
        "sendTo": "={{ $json.output.recruiterEmail }}",
        "subject": "={{ $json.output.mailSubject }}",
        "message": "={{ $json.output.mailBody }}",
        "options": {
          "appendAttribution": false,
          "attachmentsUi": {
            "attachmentsBinary": [
              {}
            ]
          }
        }
      },
      "type": "n8n-nodes-base.gmail",
      "typeVersion": 2.1,
      "position": [
        32,
        128
      ],
      "id": "fa7ead25-bb81-4043-a53f-d1408ea0f520",
      "name": "Send a message",
      "webhookId": "82f6e577-9203-47db-9463-36d847ac3a70"
    },
    {
      "parameters": {
        "promptType": "define",
        "text": "=You are a concise, professional email writer.  \n\nGenerate a short, well-formatted, and personalized email to the recruiter based on the provided resume and job details.  \n\nGuidelines:\n- Use the candidate’s **full name** exactly as given in the `CandidateName` field from the resume.  \n- Start with a greeting like “Hi” or “Hello” followed by the recruiter’s name if available.  \n- The email should be professional and naturally written in paragraph format.  \n- Mention the candidate’s total experience and notice period (60 days).  \n- Keep the tone polite and confident, expressing interest in the specific job title and company.  \n- End with a professional signature including name, phone number, email, and links (GitHub and LinkedIn if available).  \n- Output strictly in **JSON format** with the email text formatted in HTML (use `<br>` for line breaks).  \n\nInput:\nCandidateName: <ENTER YOUR NAME>  \nResume: {{ $json.text }}  \nJob Title:<ENTER YOUR JOB ROLE> (eg : Node js developer)  \nCompany Name: {{ $json.companyName }}  \nRecruiter Email: {{ $json.email }}  \n\nOutput JSON format:\n\n{\n  \"mailBody\": \"<WRITE CUSTOME MAIL BODY as per your needs>\",\n  \"mailSubject\": \"Application for <JOB TITLE>\",\n  \"recruiterEmail\": \"{{ $json.email }}\"\n}\n",
        "hasOutputParser": true,
        "options": {}
      },
      "type": "@n8n/n8n-nodes-langchain.agent",
      "typeVersion": 2.2,
      "position": [
        -896,
        400
      ],
      "id": "67964336-92b3-4f96-b9c2-4525a8122b64",
      "name": "AI Agent"
    },
    {
      "parameters": {
        "options": {}
      },
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "typeVersion": 1,
      "position": [
        -896,
        640
      ],
      "id": "28faeabf-e18b-4f8d-a2b4-219759aa211d",
      "name": "Google Gemini Chat Model"
    },
    {
      "parameters": {
        "operation": "pdf",
        "options": {}
      },
      "type": "n8n-nodes-base.extractFromFile",
      "typeVersion": 1,
      "position": [
        -1904,
        176
      ],
      "id": "edb2f3e3-20f2-4afd-90ee-bfd8fe6dba83",
      "name": "Extract from File"
    },
    {
      "parameters": {
        "operation": "download",
        "fileId": {
          "__rl": true,
          "value": "1Tgl9pTP807QO--DW2kDn-7cdF2eIqPHr",
          "mode": "list",
          "cachedResultName": "rahulMane_senior_engineer.pdf",
          "cachedResultUrl": "https://drive.google.com/file/d/1Tgl9pTP807QO--DW2kDn-7cdF2eIqPHr/view?usp=drivesdk"
        },
        "options": {}
      },
      "type": "n8n-nodes-base.googleDrive",
      "typeVersion": 3,
      "position": [
        -2576,
        240
      ],
      "id": "c5cceb2c-a269-4c5f-b255-c65658605217",
      "name": "Download file"
    },
    {
      "parameters": {},
      "type": "n8n-nodes-base.merge",
      "typeVersion": 3.2,
      "position": [
        -1152,
        224
      ],
      "id": "c92aef43-2ec8-4114-8678-1f324d2507b2",
      "name": "Merge"
    },
    {
      "parameters": {
        "jsonSchemaExample": "{\n  \"mailBody\": \"...\",          \n  \"mailSubject\": \"Application for Node js developer \", \n  \"recruiterEmail\": \"{{ $json.email }}\"\n}"
      },
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "typeVersion": 1.3,
      "position": [
        -720,
        608
      ],
      "id": "c506d304-8d1f-4b2d-aa8c-47e3fe701072",
      "name": "Structured Output Parser"
    },
    {
      "parameters": {
        "mode": "combine",
        "combineBy": "combineAll",
        "options": {}
      },
      "type": "n8n-nodes-base.merge",
      "typeVersion": 3.2,
      "position": [
        -368,
        352
      ],
      "id": "1190cb85-f143-46f2-bef2-40bef773dc3b",
      "name": "Merge1"
    },
    {
      "parameters": {
        "conditions": {
          "options": {
            "caseSensitive": true,
            "leftValue": "",
            "typeValidation": "strict",
            "version": 2
          },
          "conditions": [
            {
              "id": "261f952e-51fc-469b-a93f-ef726f089bb0",
              "leftValue": "={{ $json.output.recruiterEmail }}",
              "rightValue": "",
              "operator": {
                "type": "string",
                "operation": "notEmpty",
                "singleValue": true
              }
            }
          ],
          "combinator": "and"
        },
        "options": {}
      },
      "type": "n8n-nodes-base.filter",
      "typeVersion": 2.2,
      "position": [
        -160,
        128
      ],
      "id": "5907fbd4-ccab-49ca-a9fa-8153a398fc54",
      "name": "Filter"
    }
  ],
  "connections": {
    "Manual Trigger": {
      "main": [
        [
          {
            "node": "Fetch Google Results2",
            "type": "main",
            "index": 0
          },
          {
            "node": "Download file",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Fetch Google Results2": {
      "main": [
        [
          {
            "node": "Extract LinkedIn URLs2",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Extract LinkedIn URLs2": {
      "main": [
        [
          {
            "node": "Fetch LinkedIn Post HTML2",
            "type": "main",
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        ]
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    },
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            "node": "Loop Over Items",
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    },
    "Extract Recruiter Info2": {
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          {
            "node": "Append or update row in sheet",
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        ]
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    },
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            "node": "Replace Me",
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    },
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      ]
    },
    "Google Gemini Chat Model": {
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    "Download file": {
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            "node": "Extract from File",
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          }
        ]
      ]
    },
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    "Structured Output Parser": {
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          }
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      ]
    },
    "Filter": {
      "main": [
        [
          {
            "node": "Send a message",
            "type": "main",
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          }
        ]
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  },
  "pinData": {},
  "meta": {
    "templateCredsSetupCompleted": true,
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}

Note :

If you’d like me to personally set this up on your system, you can schedule a call by clicking the link below.

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