This article provides a complete, practical walkthrough of the Manual Googlesheets Automation 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 for B2B Prospecting Using n8n, LinkedIn, and Airtop
   
   Meta Description: Learn how to automate Ideal Customer Profile (ICP) scoring for companies using n8n. This workflow integrates Google Sheets, Airtop, and LinkedIn data to generate structured, AI-powered ICP scores for effective B2B prospecting.
   
   Keywords: n8n workflow, ICP scoring, B2B prospecting automation, ideal customer profile, Airtop API, LinkedIn scraping, Google Sheets automation, sales lead scoring, AI-powered ICP analysis, automation agency classification
   
   Third-Party APIs Used:
   
   - Google Sheets API (via Google Drive credentials)
   - Airtop API (used for data extraction and AI model processing)
   - LinkedIn company profiles (accessed publicly via URL for analysis)
   
   Article:
   
   —
   
   Automating ICP Scoring with n8n: A Smarter Way to Identify High-Value Prospects
   
   In B2B sales and marketing, identifying companies that fit your Ideal Customer Profile (ICP) is crucial to focusing your outreach on high-converting targets. Traditionally, this process is manual and time-consuming, requiring research across scattered resources like LinkedIn, job boards, and funding databases.
   
   But what if you could automate ICP scoring based on real-time LinkedIn data, structured AI analysis, and seamless integration into your lead management system?
   
   That’s exactly what this n8n workflow accomplishes—by bringing together Google Sheets, LinkedIn company URLs, and the Airtop API to calculate intelligent, data-driven ICP scores for every organization in your prospecting list.
   
   Let’s explore how it works and why it can be a game-changer for revenue teams seeking an edge in go-to-market strategy.
   
   —
   
   The Workflow at a Glance
   
   The n8n workflow, titled “ICP Company Scoring,” consists of six key steps:
   
   1. Manual Trigger:
   The process begins with a manual trigger node labeled "When clicking ‘Test workflow’", allowing users to initiate lead scoring on-demand—ideal for testing batches of company data or automating updates on a scheduled basis in future iterations.
   
   2. Input Company Data from Google Sheets:
   Next, the workflow connects to a Google Sheet named “ICP Score for Template” where a tab called “Company” stores raw company LinkedIn URLs and associated metadata. Using the Google Sheets API, these URLs are retrieved row-by-row to begin ICP evaluation.
   
   3. Extract & Analyze LinkedIn Data with Airtop:
   LinkedIn company profile pages contain valuable descriptive and behavioral data. The workflow passes each company’s LinkedIn URL to the Airtop API—an advanced extraction and analysis engine that interprets the webpage using an AI prompt.
   
   This prompt instructs Airtop to extract specific attributes such as:
   
   - Full company name, tagline, location, and description
   - Employee count and size bracket
   - Whether the company is an automation agency
   - Level of artificial intelligence (AI) adoption
   - Overall technical sophistication
   - Most recent funding information
   
   All extracted information is structured as JSON and validated against a specified schema.
   
   4. Calculate ICP Score:
   The core of the system is a composite ICP scoring system based on five weighted criteria:
   
   | Category             | Max Points |
   |----------------------|------------|
   | AI Focus             | 25         |
   | Technical Sophistication | 35     |
   | Employee Size        | 30         |
   | Automation Agency    | 20         |
   | Geography (US/EU)    | 10         |
   | Total Possible Score | 120        |
   
   Each company receives a score between 0 and 120, giving sales teams a quick view of how well a lead aligns with their ideal tech-forward, automation-savvy customer base.
   
   5. Parse and Format Response:
   A custom JavaScript node labeled “Format response” extracts the calculated ICP score and relevant identifiers (like LinkedIn URL and spreadsheet row number) into a simplified object for easy mapping to the original spreadsheet.
   
   6. Update the Spreadsheet:
   The final step is a “Google Sheets Update Row” node. It writes the newly calculated ICP score back to the correct row in the “Company” sheet, associating it with the corresponding LinkedIn URL.
   
   This closes the loop—input comes from Google Sheets, gets enhanced via AI, and returns enriched with actionable data.
   
   —
   
   Why It Matters
   
   Automated ICP scoring offers major benefits:
   
   - Saves time otherwise spent on manual research.
   - Standardizes lead qualification using objective criteria.
   - Helps SDRs and marketers focus their efforts on high-likelihood prospects.
   - Makes enterprise or mid-market lead generation scalable and repeatable.
   
   This specific setup is especially useful for teams targeting automation agencies, AI-powered companies, or tech-forward startups—segments that are growing but difficult to filter manually.
   
   Because the criteria are transparent and AI-enriched, the scoring outcomes offer both speed and context—critical for sales prioritization and campaign personalization.
   
   —
   
   Customization Opportunities
   
   This template can be modified for different industries or ICP definitions. For example:
   
   - Weight funding stage or investor type more heavily.
   - Replace the LinkedIn data source with Crunchbase or Clearbit.
   - Automatically email high-scoring leads via email marketing tools integrated in n8n.
   
   You can also schedule the workflow to run weekly, pair it with scraping automation for LinkedIn URLs, or sync enriched data into a CRM like HubSpot or Salesforce.
   
   —
   
   In Summary
   
   This n8n-powered ICP scoring workflow transforms LinkedIn URLs into insightful company profiles and business-readiness assessments—automatically. By integrating Google Sheets input, Airtop’s intelligent data extraction, and a transparent scoring model, it equips sales and marketing teams with sharper tools for identifying their next best customer.
   
   In an age where speed and personalization win deals, workflows like this help you scale both.
   
   —
   
   Want to try it out? You can clone and customize the workflow right inside n8n, connect your credentials, and start scoring your B2B leads—smarter and faster than ever.

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.