This article provides a complete, practical walkthrough of the Wait Splitout Create Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating High-Intent Job Discovery with n8n: Using Bright Data, Google Sheets, and OpenAI LLMs
   
   Meta Description:
   Discover how to automate the process of finding high-intent hiring signals from Indeed using n8n. This workflow integrates Bright Data, Google Sheets, and OpenAI to streamline job scraping and candidate fit analysis.
   
   Keywords:
   n8n workflow, job scraping automation, Bright Data API, OpenAI GPT, Google Sheets automation, LinkedIn prospecting, indeed job search, LLM for job match, API automation, SaaS prospecting, hiring signals
   
   Third-Party APIs Used:
   
   1. Bright Data API
   2. Google Sheets API (via n8n OAuth2)
   3. OpenAI API (GPT via n8n's LangChain integration)
   
   —
   
   Article:
   
   Unlocking Hiring Signals with Automation: Scraping Indeed with Bright Data and n8n
   
   Hiring intent is one of the most valuable signals a business can act on. Whether you're in B2B sales, recruitment, or lead generation, understanding which companies are openly hiring for key roles can drastically improve your outreach strategy. Fortunately, thanks to the automation capabilities of n8n and powerful integrations like Bright Data, OpenAI GPT, and Google Sheets, you can create an automated engine to surface high-intent hiring signals in real time.
   
   In this article, we’ll unpack a highly-detailed n8n workflow developed to search Indeed job listings, extract structured job post data, and enhance them with AI-based personal fit analysis—all automatically.
   
   📍 The Use Case: Hyper-Targeted Job and Talent Intelligence
   
   The workflow is designed for marketers, recruiters, or founders who want to search for specific job roles in certain locations and instantly analyze which employers are worth reaching out to. With inputs like job location, keywords, and country, users can create customized job searches. Bright Data scrapes Indeed listings, which are later enriched, analyzed, and scored.
   
   🎯 Step-by-Step Breakdown of the Workflow
   
   Let’s explore how each piece of the automation works:
   
   1. User-Driven Form Submission
   It all begins with a user-facing form built into n8n using the Form Trigger node. The form captures three inputs:
   - Job Location
   - Keyword (e.g., “CMO”, “UX Designer”, “AI Architect”)
   - Country (2-letter ISO code like “US” or “UK”)
   
   2. Submitting Job Criteria to Bright Data
   
   Upon form submission, a POST HTTP Request is triggered to the Bright Data API. Using the Bright Data Dataset API, we request a snapshot creation based on the job search filters passed from the form.
   
   Bright Data scrapes jobs from Indeed within the parameters:
   - Domain: indeed.com
   - Keyword: Defined by the user
   - Location: City or region
   - Country: Based on ISO country code
   - Date Posted: Limited to “Last 24 hours” for freshness
   
   3. Polling and Fetching the Snapshot
   
   To prevent premature data access, the workflow includes a Wait node followed by polling the status of Bright Data's snapshot. If the status is still “running,” it continues waiting. Once data is ready, it fetches the scraped job listings via HTTP Request.
   
   4. Writing Raw Job Data to Google Sheets
   
   The job data is then appended to a structured Google Sheet. Thanks to Google Sheets integration in n8n, all relevant columns—like job title, company name, salary, description, and posting date—are stored in a ready-to-use database.
   
   You can start quickly using the Google Sheet template here: [Template Sheet](https://docs.google.com/spreadsheets/d/1vHHNShHD96AWsPnbXzlDAhPg_DbXr_Yx3wsAnQEtuyU/edit?usp=sharing)
   
   5. Splitting and Personalizing the Listings
   
   We then use a Split Out node to break out each job posting into individual records, parsing out fields like company_name, job_title, and description_text for AI processing.
   
   6. LLM-Driven Job Compatibility Check
   
   Here’s where things get really smart. Each job post is passed to a LangChain-powered GPT model (e.g., gpt-4o-mini) via n8n’s OpenAI integration. The prompt is simple but powerful:
   
   “Read the following job post from {{company_name}}, for the job title {{job_title}}, and job description {{description_text}}. Tell me if you think I’m a good fit. Answer only YES or NO. I’m looking for roles in Pfizer.”
   
   This allows the model to give a yes/no answer per job, personalizing which listings are relevant for you based on your career goals or target companies.
   
   7. Updating the Google Sheet With LLM Results
   
   Finally, we update the original Google Sheet with a new column: “AM I a Fit?”, which contains the AI’s response (YES/NO). Now, your spreadsheet acts not just as a job repository but a filterable, intelligent database of meaningful leads.
   
   💡 Highlights and Flexibility
   
   - Uses a low-code interface to manage multiple APIs and automate complex logic without programming.
   - Capable of being edited for different job boards, other geographies, or role types.
   - AI component can be adapted to recommend roles, create summaries, or even write personalized outreach messages.
   
   🌐 Setup Tips and Resources
   
   - Don’t forget to set your Bright Data API key and Google Sheet credentials in the n8n node configuration.
   - Use the Sticky Notes inside the workflow—many include links, field guides, and parameter examples.
   - Get the form running publicly or embed it inside a site to allow team members or clients to use it.
   
   📌 Where to Learn More
   
   This workflow was created by Yaron Been. For tutorials, walkthroughs, or other tips on lead generation automation using n8n and Bright Data:
   - YouTube: https://www.youtube.com/@YaronBeen/videos  
   - LinkedIn: https://www.linkedin.com/in/yaronbeen/  
   - Bright Data Docs: https://docs.brightdata.com/introduction
   
   🚀 Final Thoughts
   
   This n8n workflow doesn’t just automate scraping jobs—it automates discernment. By combining public hiring data with an LLM’s reasoning, we get a powerful, scalable system for identifying high-intent prospects or job opportunities that truly fit our focus.
   
   Whether you're doing lead gen, talent sourcing, or just hunting for roles smarter—not harder—this workflow is your automated ally.
   
   Ready to stop scrolling through job boards manually? Let automation point you toward intent and insight like never before.
   
   —  
   Written by AI, fine-tuned for productivity.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.