An intelligent n8n workflow that scores resumes against job descriptions using AI, providing detailed reasoning without the keyword bias of traditional ATS systems.
Traditional ATS (Applicant Tracking Systems) checkers have significant limitations:
- They rely heavily on specific ATS keywords, which may not reflect actual job fit
- Resume parsing often fails to maintain context and formatting
- Scoring explanations are vague or non-existent
- They don't compare resumes directly against the actual job description
This n8n workflow solves these problems by:
- Using AI (Google Gemini) to intelligently compare resumes against job descriptions
- Providing detailed, transparent scoring with clear reasoning
- Evaluating based on actual relevance rather than keyword matching
- Automatically storing results in Google Sheets for easy tracking
- Automated Processing: Monitors Google Drive folder for new resume uploads
- AI-Powered Analysis: Uses Google Gemini to evaluate resume-JD fit
- Comprehensive Scoring:
- Skills match: 40%
- Experience relevance: 30%
- Education fit: 20%
- Keywords & tools: 10%
- Detailed Reasoning: Provides clear explanation for each score
- Data Extraction: Automatically extracts candidate name, email, and mobile
- Results Tracking: Stores all results in Google Sheets with timestamps
- PDF Support: Handles PDF resumes and job descriptions
1. Google Drive Trigger → Monitors folder for new resumes
2. File Validation → Checks if uploaded file is PDF
3. Download Resume → Fetches the resume file
4. Download JD → Fetches the job description
5. Extract Text → Converts PDFs to text
6. Merge Data → Combines resume and JD text
7. AI Agent → Analyzes and scores using Google Gemini
8. Parse Results → Extracts structured data
9. Save to Sheet → Stores results in Google Sheets
- n8n instance (self-hosted or n8n cloud)
- Google Drive account
- Google Sheets account
- Google Gemini API key
-
Import the Workflow
- Download
workflow/Automated Resume Relevance Scoring Workflow (1).json - In n8n, go to Workflows → Import from File
- Select the downloaded JSON file
- Download
-
Configure Google Drive
- Create a folder in Google Drive for resume uploads (e.g., "AI Resume")
- Note the folder ID from the URL
- Upload your job description PDF to Google Drive
- Note the file ID from the URL
-
Configure Credentials
- Google Drive OAuth2: Connect your Google account
- Google Sheets OAuth2: Connect your Google account
- Google Gemini API: Add your API key
- Get API key from: https://makersuite.google.com/app/apikey
-
Update Workflow Nodes
- Google Drive Trigger: Update
folderToWatchwith your folder ID - Download JD: Update
fileIdwith your job description file ID - Append row in sheet: Update
documentIdwith your Google Sheet ID
- Google Drive Trigger: Update
-
Create Results Spreadsheet
- Create a new Google Sheet
- Add column headers:
candidate_name,mobile,email,relevance_score,reasoning,resume_file,processed_date - Note the spreadsheet ID from the URL
-
Activate the Workflow
- Click "Active" toggle in n8n
- The workflow will now monitor for new resumes
- Upload a resume PDF to your designated Google Drive folder
- The workflow automatically triggers and processes the resume
- Results appear in your Google Sheet within seconds
- Review candidate details, scores, and detailed reasoning
| Category | Weight | Description |
|---|---|---|
| Skills Match | 40% | Alignment of technical and soft skills with JD requirements |
| Experience Relevance | 30% | Years and type of experience matching job needs |
| Education Fit | 20% | Educational background alignment with requirements |
| Keywords & Tools | 10% | Specific tools, technologies, and industry terms |
Edit the prompt in the "AI Agent" node to adjust scoring weights or add new criteria.
Replace "Google Gemini Chat Model" with other supported models (OpenAI, Anthropic, etc.)
Add nodes after "Append row in sheet" to send email/Slack notifications
Candidate Name: John Doe
Mobile: +1-234-567-8900
Email: john.doe@email.com
Relevance Score: 87/100
Reasoning:
- Strong skills match (35/40): Proficient in Python, SQL, and AWS
- Excellent experience (28/30): 5+ years in data engineering roles
- Education aligned (18/20): Master's in Computer Science
- Good keyword presence (6/10): Missing some specific tools mentioned in JD
Contributions are welcome! Please feel free to submit issues or pull requests.
This project is open source and available under the MIT License.
- Built on n8n workflow automation platform
- Powered by Google Gemini AI
For questions or suggestions, please open an issue on GitHub.
Note: Make sure to keep your API keys and credentials secure. Never commit them to version control.