This article provides a complete, practical walkthrough of the Manual Googlesheets Update Triggered 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 ICP Scoring from LinkedIn Using n8n and AI-Powered Data Extraction
   
   Meta Description:
   Learn how to automate Ideal Customer Profile (ICP) scoring from LinkedIn profiles using an n8n workflow. This guide explores data extraction with AirOps AI, Google Sheets automation, and business enrichment for smarter lead qualification.
   
   Keywords:
   ICP scoring automation, n8n workflow, LinkedIn automation, lead scoring, AirOps AI, Google Sheets, CRM automation, LinkedIn profile parsing, sales automation, AI in sales, data enrichment
   
   Third-Party APIs Used:
   
   - AirOps (formerly called "Airtop") – for AI-driven data extraction from LinkedIn URLs.
   - Google Sheets API – for reading and updating spreadsheet rows.
   
   —
   
   Article:
   
   Automating LinkedIn ICP Scoring with n8n and AI-Powered Extraction
   
   In B2B marketing and sales, speed and personalization are critical to winning over high-value prospects. One crucial strategy gaining traction is the use of Ideal Customer Profile (ICP) scoring—assigning numeric values to prospects based on factors like seniority, technical expertise, and specific interests. Traditionally, scoring leads has involved time-consuming research and manual data input. But with automation tools like n8n and AI-powered data extraction services, we can streamline this entire process.
   
   In this article, we’ll break down an end-to-end n8n workflow that automates ICP scoring derived from LinkedIn profile data. The results are not only more consistent, but also scalable—powering smarter sales playbooks and targeted outreach.
   
   Workflow Overview
   
   This particular n8n workflow is designed to take a LinkedIn profile URL from a Google Sheet, extract key professional and technical attributes using the AirOps API, calculate an ICP Score, and write that data back into the Google Sheet.
   
   Let’s walk through each step:
   
   1. Manual Trigger: Start the Automation
   
   The workflow begins with a Manual Trigger node labeled "When clicking ‘Test workflow’”. This makes it easy to test the flow iteratively or run it on-demand during development. The trigger initiates the process of fetching LinkedIn profile data to be scored.
   
   2. Get the Lead Data: Reading from Google Sheets
   
   The next step is the "Get person" node that interfaces with the Google Sheets API. It’s connected to a specific document and sheet—"ICP Score for Template", Sheet ID: gid=0. From this spreadsheet, it retrieves rows that contain the field Linkedin_URL_Person and the corresponding row_number, serving as the lookup identifier later in the workflow.
   
   3. Extract LinkedIn Data using AI: AirOps Magic
   
   This is where the real intelligence kicks in. The "Calculate ICP PersonScoring" node sends the LinkedIn URL to AirOps, an AI-powered data extraction and enrichment tool. AirOps receives a detailed prompt that asks it to extract several pieces of information from the LinkedIn profile:
   
   - Full name
   - Current or last job title
   - Company and LinkedIn URL of the employer
   - Location
   - Number of connections and followers
   - 'About' section text
   - AI interest level (e.g., beginner to expert)
   - Seniority level (junior to executive)
   - Technical depth (basic to expert)
   
   After pulling this information, the AirOps AI calculates an ICP Score using this scoring model:
   
   - AI Interest: beginner (5), intermediate (10), advanced (25), expert (35)
   - Technical Depth: basic (5), intermediate (15), advanced (25), expert (35)
   - Seniority: junior (5), mid-level (15), senior (25), executive (30)
   
   The final score is the sum of the three attributes, forming a high-level estimate of a lead’s alignment with your product or service.
   
   4. Process and Format the Data
   
   Once AirOps returns the enriched data and computed ICP Score, the "Format response" node takes over. This is a Code node where the raw JSON response is parsed to extract three key elements:
   
   - Row number
   - LinkedIn URL
   - ICP Score
   
   These are formatted appropriately to update the exact row within the original sheet.
   
   5. Write Back to Google Sheets
   
   In the final step, the "Update row" node uses this information to match the original row number in the Google Sheet and updates the column ICP_Score_Person with the new score. It ensures a two-way data sync: results from the AI scoring engine become part of your operational spreadsheet, unlocking potential for downstream automation or analytics.
   
   Benefits of This Workflow
   
   Speed and Scalability: Processes that used to take 15–30 minutes per lead are now automated completely.
   Consistency and Accuracy: AI scoring reduces human error and bias, providing structured, repeatable evaluations.
   CRM and Outbound Readiness: Once the sheet is enriched with ICP scores, you can filter top prospects, personalize outreach, or trigger campaigns from your CRM or email tools.
   
   Why Use AirOps?
   
   AirOps acts as the intelligence layer in this system. Unlike traditional scraping or scraping-centric no-code tools, AirOps is prompt-driven and can handle complex parsing and logic, such as computing values like "technical depth" or "AI interest level" based on free-text descriptions in the ‘About’ section of a profile. It’s structured, smart, and aligned with modern LLM capabilities.
   
   Final Thoughts
   
   If your sales or marketing team is still doing manual LinkedIn research to evaluate leads, you're operating at a disadvantage. With this workflow, you can transform LinkedIn URLs into structured, high-context lead scores in minutes—automatically.
   
   This n8n automation connects the human-readable insights of LinkedIn with the machine-readable needs of today’s sales and data pipelines. The beauty lies in its modularity: you can expand this further by piping ICP scores into CRMs, triggering Slack alerts, or refining your cold outreach lists based on real-time enrichment.
   
   Start building smarter pipelines with n8n and the power of AI.
   
   —
   
   Ready to learn more? Explore n8n.io or check out AirOps for building transformative workflows that even your sales team will thank you for.

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.