A Python-based GUI tool to perform LinkedIn recon and generate company email addresses from employee name lists. Ideal for red teams, OSINT professionals, and penetration testers.
- Extracts employee names from a LinkedIn company "People" page
- Supports common corporate email formats
- Builds a list of likely email addresses (e.g.
john.doe@example.com) using scraped names - Lets you review results, preview email structure, and export to CSV
- Fully manual login and scrolling for stealth and realism
- ✅ Manual login through real browser window (no API, no automation bans)
- ✅ Extracts names of employees from LinkedIn
- ✅ Supports formats:
firstname.lastname,firstnamelastname,j.doe, and more - ✅ Domain input (e.g.
@example.com) - ✅ Live email preview and name selection
- ✅ Export to
.csvfor phishing, enumeration, or red teaming - ✅ Built with tkinter GUI for portability and ease of use
git clone https://github.com/intelligencegroup-io/linkedin-email-recon.git
cd linkedin-email-recon
python3 -m venv venv
source venv/bin/activate
pip install selenium pandas
sudo apt install chromium-driverpython3 linkedin-email-recon.py- Paste the LinkedIn "People" page URL (e.g.
https://www.linkedin.com/company/example/people/) - Launch browser → Log in manually → Scroll to load all names
- Return to GUI and click Scrape Names
- Select valid names from the list
- Enter a domain (e.g.
example.com) and choose a format - Preview the generated emails
- Export the final list to CSV
John Doe <john.doe@example.com>
Jane Doe <jane.doe@example.com>
J Doe <j.doe@example.com>
- Red team email harvesting
- OSINT and LinkedIn-based recon
- Pretexting and phishing simulation
- Username generation for login portals
- Credential stuffing or validation prep
This tool is intended strictly for authorised security assessments. Do not use it against any targets without written permission.
MIT License — use freely for ethical hacking and training purposes.