market-research

user-scanner: Extract Deep Data

Name: user-scanner: Extract Deep Data
Author: kaifcodec

Get deep data extraction for security research with user-scanner, a tool for founders and cybersecurity professionals, backed by 2.7k+ GitHub stars.

3,248 stars380 forksPythonHealth Score 9/10Updated 8/19/2026100% free · open source

What it does

User-scanner is a tool that extracts deep data for security research by analyzing 310+ scan vectors for email and username OSINT, providing insights for digital footprinting and investigations.

Install / run

git clone https://github.com/kaifcodec/user-scanner.git

When to use it

•When conducting security research on a specific target, such as a company or individual
•When performing digital footprinting to identify potential security vulnerabilities
•When investigating potential security threats and needing detailed information on a username or email

Quick start

1Navigate to the cloned repository using 'cd user-scanner'
2Run the tool using 'python user_scanner.py' to start the scanner
3Configure the scanner by editing the 'config.json' file to specify target email or username
4Use the '-h' flag to view help options, e.g., 'python user_scanner.py -h'
5Review the 'README.md' file for detailed usage and configuration instructions

Ready-to-paste prompt

python user_scanner.py -e example@example.com -u exampleusername

Heads up: The tool requires Python to be installed on the system, and the user must have the necessary permissions to run the script and access the required resources

Saves to your device

Topics

cybersecurity

cybersecurity-tools

email-osint

enumeration

ethical-hacking

ethical-hacking-tools

osint

osint-email

osint-tool

osint-tools

osint-username

python

redteam-tools

redteaming

threat-intelligence

username-osint

How user-scanner: Extract Deep Data works

Codeflow

Free to inspect

User-scanner: Extract Deep Data is a large Python project (~33k lines across 512 code files, plus 19 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, MIT license, has a test suite.

Size

Large codebase

~33k lines · 512 code files · ~5 h to skim

Setup

Developer setup

A real software project. Use it via its install path; don't expect to read it all.

Runs on

Python

No API keys detected

Python 100%

Where to start reading