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TalentFlow AI – Instantly evaluate applicant's GitHub, LinkedIn, using AI

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TalentFlow AI – Instantly evaluate applicant's GitHub, LinkedIn, using  AI preview
Open on n8n.io

Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

AI Powered Job Application Screening Workflow Who Is This For? This template is ideal for: HR teams Recruiters Hiring managers Who want to automate the screening of job applicants. If you collect c...

Best for

  • HR automation workflows
  • AI Summarization automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.lmchatopenrouter, @n8n/n8n-nodes-langchain.agent, n8n-nodes-base.merge, @n8n/n8n-nodes-langchain.outputparserstructured, n8n-nodes-base.noop, n8n-nodes-base.jotformtrigger, n8n-nodes-base.httprequest, n8n-nodes-base.code

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Roshan Ramani.

Original n8n.io source

1.1 Workflow description

Title
TalentFlow AI – Instantly evaluate applicant's GitHub, LinkedIn, using AI
Workflow name
TalentFlow AI – Instantly evaluate applicant's GitHub, LinkedIn, using AI

AI-Powered Job Application Screening Workflow

Who Is This For?

This template is ideal for:

  • HR teams
  • Recruiters
  • Hiring managers

Who want to automate the screening of job applicants. If you collect candidate data via JotForm and want an AI‑powered, multi‑source evaluation—without manual copy‑paste—this workflow is for you.

What Problem Does This Solve?

Manually reviewing:

  • LinkedIn profiles
  • GitHub repos
  • LeetCode stats
  • Resume PDFs

Is time‑consuming and error‑prone. This workflow:

  1. Centralizes all candidate data
  2. Scores each dimension against specific job requirements
  3. Appends results to Google Sheets
  4. Helps make data‑driven hiring decisions faster

What This Workflow Does

1. Trigger on New JotForm Submission

Captures candidate name, email, social URLs, resume link, and job description
(Pro Tip: Add hidden field in JotForm to pass job requirements)

2. Extract & Normalize Usernames

Parses LinkedIn, GitHub, and LeetCode handles

3. Scrape & Format Data

  • LinkedIn → profile & latest posts via ScrapingDog & Apify
  • GitHub → profile, repos, and contribution metrics via GitHub GraphQL
  • LeetCode → submission stats & rankings via LeetCode GraphQL
  • Resume → converts PDF to text with PDF.co

4. AI‑Powered Evaluation

Uses LangChain agents on OpenRouter to:

  • Generate fit scores (1–10) based on job description
  • Provide justifications for each source
  • Analyze candidate suitability against specific role requirements

5. Merge & Append

Consolidates scores and feedback → writes new row to Google Sheets with hyperlinks and totals

Setup

JotForm Configuration

  1. Create your job application form
  2. Add Hidden Field:
    • Name: job_description
    • Value: [Paste full job description here] (This enables AI to evaluate candidates against specific role requirements)
  3. Create fields matching the workflow requirements

Required Credentials/Variables

  • JotForm API key
  • SCRAPINGDOG_API_KEY & Apify token
  • GitHub personal access token (read‑only)
  • PDF.co API key
  • OpenRouter API key

Configuration Steps

  1. Google Sheets
    • Create or select spreadsheet
    • Grant n8n access
  2. Node Organization
    • All nodes renamed for clarity
    • Sticky notes explain prerequisites

How to Customize

1. Job-Specific Evaluation

  • Include hidden job_description field in JotForm
  • AI will automatically analyze candidates against these requirements

2. Add/Edit Fields

Use Edit Fields node to map your form questions

3. Adjust Scoring

Tweak agent prompts:

  • linkedin evaluation
  • github evaluation
  • etc.

4. Extend Sources

Insert new branches (e.g., Stack Overflow) by following existing patterns

Result

Transforms raw candidate submissions into actionable insights—evaluating applicants against specific job requirements and reducing screening time from hours to minutes with consistent, unbiased evaluations.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - OpenRouter Chat Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 2 - OpenRouter Chat Model1

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 3 - github evaluation

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2

Block 4 - resume evaluation

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2

Block 5 - OpenRouter Chat Model2

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 6 - leetcode evaluation

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2

Block 7 - OpenRouter Chat Model3

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 8 - linkedin evaluation

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2

Block 9 - Merge

Type / Role
n8n-nodes-base.merge - merge
Config choices
Version 3.2

Block 10 - Structured Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.2

Block 11 - Structured Output Parser1

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.2

Block 12 - Structured Output Parser2

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.2

Block 13 - Structured Output Parser3

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.2

Block 14 - No Operation, do nothing

Type / Role
n8n-nodes-base.noOp - noOp
Config choices
Version 1

Block 15 - No Operation, do nothing1

Type / Role
n8n-nodes-base.noOp - noOp
Config choices
Version 1

Block 16 - No Operation, do nothing2

Type / Role
n8n-nodes-base.noOp - noOp
Config choices
Version 1

Block 17 - Trigger: New Job Application

Type / Role
n8n-nodes-base.jotFormTrigger - jotFormTrigger
Config choices
Version 1

Block 18 - Fetch JotForm Data

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 19 - Filter Required Fields

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 20 - Extract Usernames from Fields

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 21 - Validate LinkedIn Username

Type / Role
n8n-nodes-base.if - if
Config choices
Version 1

Block 22 - Scrape LinkedIn Data

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 3

Block 23 - Format LinkedIn Result

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 24 - Format LinkedIn Result1

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Showing the first 24 of 41 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow TalentFlow AI – Instantly evaluate applicant's GitHub, LinkedIn, using AI
Complexity advanced
Nodes 41
Categories HR, AI Summarization
Author Roshan Ramani
Published 21 Jun 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/5099/5099.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does TalentFlow AI – Instantly evaluate applicant's GitHub, LinkedIn, using AI do?

AI Powered Job Application Screening Workflow Who Is This For? This template is ideal for: HR teams Recruiters Hiring managers Who want to automate the screening of job applicants. If you collect c...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your HR, AI Summarization use case.