Parse & Evaluate HR Candidates with GPT-4.1 and LinkedIn Data in CSV/XLSX

AI-Powered HR Candidate Evaluation Agent with LinkedIn Data Enrichment in CSV/XLSX Format

🎯 Overview

Transform your manual hiring process into an intelligent evaluation system that saves 15-20 minutes per candidate! This workflow automates the entire candidate assessment pipeline - from CSV/XLSX upload to AI-powered scoring with LinkedIn insights.

When you upload a candidate list, this workflow automatically: 📊 Converts your file into a formatted Google Sheet with RTL support 🔍 Researches each candidate's recent LinkedIn posts via Apify 🤖 Evaluates candidates using GPT-4.1 with context-aware scoring (0-100) 💬 Generates professional Hebrew explanations for each score 📈 Auto-sorts by score and applies professional formatting ⚠️ Sends error alerts to keep everything running smoothly

Cost per candidate: ~$0.05 | Time saved: 15-20 minutes each

👥 Who's it for?

HR teams drowning in candidate applications Recruitment agencies needing consistent evaluation criteria
Hiring managers seeking data-driven candidate insights Companies looking to scale their team Anyone tired of manual spreadsheet juggling

⚡ How it works

Form submission triggers with CSV/XLSX upload Google Drive stores the file and creates a new Sheet Data extraction processes the file content AI Agent loops through each candidate: Fetches up to 3 recent LinkedIn posts via Apify Analyzes qualifications against job requirements Generates evaluation score and Hebrew explanation Sheet formatting applies filters, sorting, and styling Error handling notifies admin of any issues

🛠️ Setup Instructions

Time to deploy: 15 minutes

Requirements: Google account (Drive + Sheets access) OpenAI API key (GPT-4.1 access) Apify API key (for LinkedIn scraping) Gmail account (for error notifications)

Step-by-step: Import this template into your n8n instance Configure Google credentials: Connect Google Drive OAuth2 Connect Google Sheets OAuth2 Add OpenAI API key to the GPT-4.1 node Set up Apify credentials for LinkedIn scraping Configure Gmail for error alerts (update email in "Send a message" node) Update folder IDs in Google Drive nodes to your folders Test with a sample CSV containing 2-3 candidates Activate and share the form URL with your team!

📋 Input File Format

Your CSV/XLSX should include these columns (Hebrew): שם פרטי (First name) שם משפחה (Last name)
חשבון לינקדאין (LinkedIn URL) Your custom evaluation questions

🎨 Customization Options

Easy tweaks: Scoring criteria**: Modify the AI agent's system message Language**: Switch from Hebrew to any language Scoring rubric**: Adjust the 50/25/15/10 weighting LinkedIn posts**: Change from 3 posts to more/fewer Sheet styling**: Customize colors and formatting

Advanced modifications: Add integration with your ATS (Greenhouse, Lever, etc.) Connect to Slack for real-time notifications Add multiple evaluation agents for different roles Implement multi-language support Add candidate email automation

💡 Pro Tips

Better LinkedIn data**: Ensure candidates provide complete LinkedIn URLs (not just usernames) Consistent scoring**: Run batches of similar roles together for normalized scoring Cost optimization**: Adjust Apify settings to fetch only essential data Scale smartly**: Process in batches of min 10-20 for optimal performance

⚠️ Important Notes

LinkedIn scraping respects Apify's rate limits Scores are relative within each batch - don't compare across different job roles The workflow handles both CSV and XLSX formats automatically Error notifications help you catch issues before they cascade

📊 Expected Results

After implementation, expect: Data-driven evaluation across candidates Professional explanation for hiring decisions Happy recruiters who can focus on human connection

Built with ❤️ by Elay Guez