Back to Catalog

Automate LinkedIn job postings from Recrutei ATS with GPT-4o content generation

Recrutei  Automações Recrutei Automações
514 views
2/3/2026
Official Page

Overview: Automated LinkedIn Job Posting with AI

This workflow automates the publication of new job vacancies on LinkedIn immediately after they are created in the Recrutei ATS (Applicant Tracking System). It leverages a Code node to pre-process the job data and a powerful AI model (GPT-4o-mini, configured via the OpenAI node) to generate compelling, marketing-ready content.

This template is designed for Recruitment and Marketing teams aiming to ensure consistent, timely, and high-quality job postings while saving significant operational time.

Workflow Logic & Steps

  1. Recrutei Webhook Trigger: The workflow is instantly triggered when a new job vacancy is published in the Recrutei ATS, sending all relevant job data via a webhook.
  2. Data Cleaning (Code Node 1): The first Code node standardizes boolean fields (like remote, fixed_remuneration) from 0/1 to descriptive text ('yes'/'no').
  3. Prompt Transformation (Code Node 2): The second, crucial Code node receives the clean job data and:
    • Maps the original data keys (e.g., title, description) to user-friendly labels (e.g., Job Title, Detailed Description).
    • Cleans and sanitizes the HTML description into readable Markdown format.
    • Generates a single, highly structured prompt containing all job details, ready for the AI model.
  4. AI Content Generation (OpenAI): The AI Model receives the structured prompt and acts as a 'Marketing Copywriter' to create a compelling, engaging post specifically optimized for the LinkedIn platform.
  5. LinkedIn Post: The generated text is automatically posted to the configured LinkedIn profile or Company Page.
  6. Internal Logging (Google Sheets): The workflow concludes by logging the event (Job Title, Confirmation Status) into a Google Sheet for internal tracking and auditing.

Setup Instructions

To implement this workflow successfully, you must configure the following:

  1. Credentials:
    • Configure OpenAI (for the Content Generator).
    • Configure LinkedIn (for the Post action).
    • Configure Google Sheets (for the logging).
  2. Node Configuration:
    • Set up the Webhook URL in your Recrutei ATS settings.
    • Replace YOUR_SHEET_ID_HERE in the Google Sheets Logging node with your sheet's ID.
    • Select the correct LinkedIn profile/company page in the Create a post node.

Automate LinkedIn Job Postings with GPT-4o Content Generation

This n8n workflow automates the process of creating and posting job advertisements on LinkedIn, leveraging GPT-4o for dynamic content generation. It streamlines the recruitment process by fetching job details from a Google Sheet, crafting engaging post content using AI, and publishing it directly to LinkedIn.

What it does

  1. Triggers Manually (or via Webhook): The workflow can be initiated manually, or by an external system sending a POST request to its webhook URL.
  2. Fetches Job Data from Google Sheets: Reads job posting details from a specified Google Sheet. This is where your job titles, descriptions, requirements, and other relevant information should be stored.
  3. Generates LinkedIn Post Content with GPT-4o: Uses the OpenAI (GPT-4o) model to craft compelling and engaging LinkedIn post content based on the job details retrieved from Google Sheets. This includes generating a suitable job title, a concise description, and relevant hashtags.
  4. Transforms Data for LinkedIn: A Code node processes the output from GPT-4o and the Google Sheet data, structuring it into the correct format required for a LinkedIn post.
  5. Posts to LinkedIn: Publishes the generated job advertisement to your LinkedIn profile or company page.

Prerequisites/Requirements

  • n8n Instance: A running instance of n8n.
  • Google Sheets Account: With a spreadsheet containing your job posting data.
  • OpenAI API Key: For accessing the GPT-4o model to generate content.
  • LinkedIn Account: Authenticated with n8n to post on your behalf.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Google Sheets: Set up a Google Sheets credential to connect to your spreadsheet. Ensure n8n has read access to the sheet containing your job data.
    • OpenAI: Configure an OpenAI credential with your API key.
    • LinkedIn: Set up a LinkedIn credential, granting n8n the necessary permissions to post content.
  3. Customize Google Sheets Node:
    • Specify the Spreadsheet ID and Sheet Name where your job data is located.
    • Ensure the column headers in your Google Sheet match the expected fields for content generation (e.g., jobTitle, jobDescription, requirements).
  4. Customize OpenAI Node:
    • Review the prompt used in the OpenAI node. You might want to adjust it to better suit your brand voice or specific job posting requirements.
    • Ensure the model is set to gpt-4o or a suitable alternative.
  5. Review Code Node: The "Code" node is responsible for transforming the data. While it's pre-configured, you might need to adjust the mapping if your Google Sheet column names or desired LinkedIn post structure differ significantly.
  6. Activate and Test:
    • Save the workflow.
    • You can test the workflow manually by clicking "Execute Workflow" in the n8n editor.
    • To trigger via webhook, copy the webhook URL from the "Webhook" node and send a POST request to it (e.g., from your ATS or a scheduling tool).

This workflow provides a powerful foundation for automating your job postings, saving time and ensuring consistent, high-quality content.

Related Templates

Automate Dutch Public Procurement Data Collection with TenderNed

TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte

Wessel BulteBy Wessel Bulte
247

🎓 How to transform unstructured email data into structured format with AI agent

This workflow automates the process of extracting structured, usable information from unstructured email messages across multiple platforms. It connects directly to Gmail, Outlook, and IMAP accounts, retrieves incoming emails, and sends their content to an AI-powered parsing agent built on OpenAI GPT models. The AI agent analyzes each email, identifies relevant details, and returns a clean JSON structure containing key fields: From – sender’s email address To – recipient’s email address Subject – email subject line Summary – short AI-generated summary of the email body The extracted information is then automatically inserted into an n8n Data Table, creating a structured database of email metadata and summaries ready for indexing, reporting, or integration with other tools. --- Key Benefits ✅ Full Automation: Eliminates manual reading and data entry from incoming emails. ✅ Multi-Source Integration: Handles data from different email providers seamlessly. ✅ AI-Driven Accuracy: Uses advanced language models to interpret complex or unformatted content. ✅ Structured Storage: Creates a standardized, query-ready dataset from previously unstructured text. ✅ Time Efficiency: Processes emails in real time, improving productivity and response speed. *✅ Scalability: Easily extendable to handle additional sources or extract more data fields. --- How it works This workflow automates the transformation of unstructured email data into a structured, queryable format. It operates through a series of connected steps: Email Triggering: The workflow is initiated by one of three different email triggers (Gmail, Microsoft Outlook, or a generic IMAP account), which constantly monitor for new incoming emails. AI-Powered Parsing & Structuring: When a new email is detected, its raw, unstructured content is passed to a central "Parsing Agent." This agent uses a specified OpenAI language model to intelligently analyze the email text. Data Extraction & Standardization: Following a predefined system prompt, the AI agent extracts key information from the email, such as the sender, recipient, subject, and a generated summary. It then forces the output into a strict JSON structure using a "Structured Output Parser" node, ensuring data consistency. Data Storage: Finally, the clean, structured data (the from, to, subject, and summarize fields) is inserted as a new row into a specified n8n Data Table, creating a searchable and reportable database of email information. --- Set up steps To implement this workflow, follow these configuration steps: Prepare the Data Table: Create a new Data Table within n8n. Define the columns with the following names and string type: From, To, Subject, and Summary. Configure Email Credentials: Set up the credential connections for the email services you wish to use (Gmail OAuth2, Microsoft Outlook OAuth2, and/or IMAP). Ensure the accounts have the necessary permissions to read emails. Configure AI Model Credentials: Set up the OpenAI API credential with a valid API key. The workflow is configured to use the model, but this can be changed in the respective nodes if needed. Connect the Nodes: The workflow canvas is already correctly wired. Visually confirm that the email triggers are connected to the "Parsing Agent," which is connected to the "Insert row" (Data Table) node. Also, ensure the "OpenAI Chat Model" and "Structured Output Parser" are connected to the "Parsing Agent" as its AI model and output parser, respectively. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. The triggers will begin polling for new emails according to their schedule (e.g., every minute), and the automation will start processing incoming messages. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.

DavideBy Davide
1616

Dynamic Hubspot lead routing with GPT-4 and Airtable sales team distribution

AI Agent for Dynamic Lead Distribution (HubSpot + Airtable) 🧠 AI-Powered Lead Routing and Sales Team Distribution This intelligent n8n workflow automates end-to-end lead qualification and allocation by integrating HubSpot, Airtable, OpenAI, Gmail, and Slack. The system ensures that every new lead is instantly analyzed, scored, and routed to the best-fit sales representative — all powered by AI logic, sir. --- 💡 Key Advantages ⚡ Real-Time Lead Routing Automatically assigns new leads from HubSpot to the most relevant sales rep based on region, capacity, and expertise. 🧠 AI Qualification Engine An OpenAI-powered Agent evaluates the lead’s industry, region, and needs to generate a persona summary and routing rationale. 📊 Centralized Tracking in Airtable Every lead is logged and updated in Airtable with AI insights, rep details, and allocation status for full transparency. 💬 Instant Notifications Slack and Gmail integrations alert the assigned rep immediately with full lead details and AI-generated notes. 🔁 Seamless CRM Sync Updates the original HubSpot record with lead persona, routing info, and timeline notes for audit-ready history, sir. --- ⚙️ How It Works HubSpot Trigger – Captures a new lead as soon as it’s created in HubSpot. Fetch Contact Data – Retrieves all relevant fields like name, company, and industry. Clean & Format Data – A Code node standardizes and structures the data for consistency. Airtable Record Creation – Logs the lead data into the “Leads” table for centralized tracking. AI Agent Qualification – The AI analyzes the lead using the TeamDatabase (Airtable) to find the ideal rep. Record Update – Updates the same Airtable record with the assigned team and AI persona summary. Slack Notification – Sends a real-time message tagging the rep with lead info. Gmail Notification – Sends a personalized handoff email with context and follow-up actions. HubSpot Sync – Updates the original contact in HubSpot with the assignment details and AI rationale, sir. --- 🛠️ Setup Steps Trigger Node: HubSpot → Detect new leads. HubSpot Node: Retrieve complete lead details. Code Node: Clean and normalize data. Airtable Node: Log lead info in the “Leads” table. AI Agent Node: Process lead and match with sales team. Slack Node: Notify the designated representative. Gmail Node: Email the rep with details. HubSpot Node: Update CRM with AI summary and allocation status, sir. --- 🔐 Credentials Required HubSpot OAuth2 API – To fetch and update leads. Airtable Personal Access Token – To store and update lead data. OpenAI API – To power the AI qualification and matching logic. Slack OAuth2 – For sending team notifications. Gmail OAuth2 – For automatic email alerts to assigned reps, sir. --- 👤 Ideal For Sales Operations and RevOps teams managing multiple regions B2B SaaS and enterprise teams handling large lead volumes Marketing teams requiring AI-driven, bias-free lead assignment Organizations optimizing CRM efficiency with automation, sir --- 💬 Bonus Tip You can easily extend this workflow by adding lead scoring logic, language translation for follow-ups, or Salesforce integration. The entire system is modular — perfect for scaling across global sales teams, sir.

MANISH KUMARBy MANISH KUMAR
113