Match job descriptions with resumes using Google Gemini and log scores to Google Sheets

Description

Smart Resume Screener — JD ↔ Resume AI Match & Sheet Logger

Smart Resume Screener ingests a candidate resume and a job description link, extracts clean text from both, runs an LLM-powered screening agent to produce a structured assessment (strengths, weaknesses, risk/reward, justification, and a 0–10 fit score), extracts contact details, and appends a single, validated row to a Google Sheet for tracking.

How It Works (Step-by-Step)

1. Trigger — On Form Submission
Public form webhook sends:
Binary resume file (PDF / DOCX)
Job Description (JD) URL or text

2. Extract & Fetch Content

Resume Extraction node**
Converts the uploaded binary resume into plain text (data.resume).

HTTP Request node**
Fetches the JD HTML/text from the provided link.

Job Description Extractor (LLM-driven)**
Parses the fetched content into structured JD fields:
Requirements
Responsibilities
Skills
Seniority
etc.

3. Prepare and Aggregate

Set Resume node**
Normalizes the resume into a clean JSON object.

Merge/Aggregate node**
Builds a single payload containing:
{ "resume": "...", "job_description": "...", "meta": "..." }
4. AI Evaluation

Recruiter Agent (LangChain node, powered by Google Gemini)**
Receives aggregated payload
Returns a strict JSON-formatted screening report including:

candidate_strengths
candidate_weaknesses
risk
reward
overall_fit_rating (0–10 numeric)
justification

Structured Output Parser**
Enforces JSON schema
Ensures predictable downstream data

5. Identity Extraction & Logging

Contact Info Extractor**
Extracts:
Name
Email

Append to Google Sheets**
Writes:
Date
Name
Email
Strengths
Weaknesses
Risk
Reward
Justification
Overall Fit Score

6. (Optional) Notifications / Follow-Ups

Add Slack / Email / Webhook nodes
Trigger alerts for high-fit candidates

Quick Setup Guide
👉 Demo & Setup Video
👉 Sheet Template
👉 Course

Nodes of Interest You Can Edit

Trigger — On Form Submission
Change webhook URL
Modify accepted form fields
Add metadata capture (job_id, source)

Resume Extraction (Extract from File)
Enable OCR fallback
Adjust encoding/charset handling
Replace with third-party resume parser

HTTP Request (Fetch Job Description)
Configure timeouts
Add retry policy
Set headers
Restrict allowed domains

Job Description Extractor (Information Extractor1)
Modify extractor prompt/schema
Add fields like must_have and nice_to_have

Set Resume (Prepare Resume)
Strip headers/footers
Normalize dates
Split resume sections

Merge / Aggregate
Modify payload structure
Add context fields (job_id, recruiter_notes, source_platform)

Recruiter Agent (LangChain Agent)
Edit system/user prompts
Adjust model temperature
Modify token limits
Switch LLM provider

Structured Output Parser
Update JSON schema
Add fields like:
experience_years
certifications
notice_period

Contact Info Extractor
Add:
Phone
LinkedIn
Location

Append to Google Sheets
Modify column mapping
Add fields like:
workflow_run_id
resume_link

What You’ll Need (Credentials)

Google Sheets API credentials (OAuth or Service Account)
Google Drive / Storage credentials (if resumes are stored there)
LLM provider credentials (e.g., Google Gemini API key/service account)
(Optional) OCR / Vision API credentials for scanned PDFs
(Optional) Email / Slack / Teams webhook or SMTP credentials
Access to public JD URLs (or credentials if behind authentication)

Recommended Settings & Best Practices

LLM temperature:** 0.0–0.3 for consistent output
Max tokens:** 800–1200 for justification (with enforced limits)
Strict JSON schema:** Fail fast on invalid structure
Retries & timeouts:**
~10s HTTP timeout
2 retries with exponential backoff
Rate limiting:** Protect LLM quotas
Deduplication:** Check existing email or resume hash
Least privilege:** Scope Google service account to target sheet only
PII handling:** Limit exposed fields; encrypt sensitive data if needed
Schema versioning:** Add schema_version column
Error logging:** Use Catch node with workflow_run_id
Human review gate:** Route borderline scores (6–7) for manual review

Customization Ideas

Conditional alerts (overall_fit_rating >= 8)
Multi-model scoring (Gemini + alternative model)
Automated outreach emails
ATS integration (Greenhouse, Lever, etc.)
JD template library
Multi-language resume routing
Skill-level mapping (e.g., python: 4/5)
Candidate scoring dashboard
Resume storage with secure links

Troubleshooting — Quick Tips

Resume Extraction Issues
Validate binary input
Enable OCR for scanned PDFs
Check encoding and file type

JD Fetch Failure
Validate URL reachability
Add headers (User-Agent)
Increase timeout
Provide auth if needed

LLM JSON Errors
Lower temperature (0–0.2)
Enforce strict JSON prompt
Add retry with "fix-json" prompt
Inspect raw LLM output

Google Sheets Append Fails
Check credential expiry
Confirm sheet ID and gid
Validate column mapping
Monitor API quota

Duplicate Rows
Add email-based dedupe logic
Hash resume content

PII Exposure
Audit sheet sharing settings
Use restricted service accounts

Tags / Suggested Listing Fields

recruiting
resume-parser
ai-screening
langchain
google-gemini
google-sheets
n8n
ats-integration
pii-sensitive
automation