# n8n: Resume Parser & Ranker for Job Applications

> Every emailed resume gets parsed, ranked against the JD, and pushed into your ATS with a summary and match score.

**Platform:** n8n  
**Category:** n8n  
**Difficulty:** Intermediate  
**Tags:** n8n, hr, recruiting, openai  
**Updated:** 2026-06-05  
**Source:** https://promptfoo.tech/workflows/n8n-hr-resume-parser

## Overview
Recruiters drown in resumes. This n8n workflow watches a jobs@ inbox, parses each PDF with GPT-4o vision, ranks the candidate against a job description using a structured rubric, and pushes to your ATS (Ashby, Greenhouse) with a match score and a two-line summary.

## How it works
1. Gmail trigger on new email with attachment.
2. GPT-4o vision extracts structured resume data (roles, tenure, skills, education).
3. Second call ranks against the JD rubric (skills match, seniority fit, red flags).
4. Ashby/Greenhouse API creates a candidate with tags and a summary note.

## Benefits
- Recruiters open pre-ranked candidates.
- Zero data entry per resume.
- Consistent screening rubric removes reviewer variance.

## Use cases
- Startups without a dedicated sourcing team.
- High-volume roles (support, sales, entry-level eng).
- Agencies managing multiple client roles.

## Step-by-step
### Step 1: Set up the jobs inbox
One inbox per role, or use +role addressing on a shared jobs@. Filter to attachments only.

### Step 2: Vision extraction
GPT-4o with a strict schema: {name, email, roles: [{title, company, start, end, bullets}], skills, education}.

### Step 3: Ranking rubric
JD-specific: must-have skills, nice-to-have, seniority (junior/mid/senior/staff), red flags (job hops without justification, sensitive gaps).

### Step 4: Match score + notes
'Return {score:0-10, must_haves_met, nice_haves_met, concerns[], one_line_summary}'.

### Step 5: ATS push
Create candidate with the extracted fields, attach original PDF, add score as a tag or custom field.

## Example
```
Match: 8/10. Summary: 'Senior FE eng, 6y React, ex-Stripe, strong design collab experience.' Concerns: 'Two < 1yr tenures in last 3 years.'
```

## FAQs
**Bias risks?**

Real and legal. Never let the model factor names, photos, or schools into the score. Audit outputs monthly against outcomes.

**PII compliance?**

For EU candidates, use Azure OpenAI in an EU region and honor deletion requests via the ATS.

**Cost?**

About $0.02-0.05 per resume with GPT-4o vision.

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Licensed under CC-BY-4.0. Attribution: PromptFoo.tech