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.

5,340 stars+175★ this week534 forksPythonGuide quality 9/10 · reviewed Jul 14Updated 10/9/2026100% 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.

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

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

Use with Claude

New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install

Claude Code

mkdir -p ~/.claude/skills/user-scanner && curl -fsSL https://workflowstacks.com/api/skills/user-scanner/claude-skill -o ~/.claude/skills/user-scanner/SKILL.md

Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)

claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How user-scanner: Extract Deep Data works

Codeflow

Free to inspect

User-scanner: Extract Deep Data is a very large Python project (~93k lines across 1260 code files, plus 20 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

Very large codebase

~93k lines · 1260 code files · days to read — use, don't read

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

1
README.md
Start here — what it does and how to install it
2
AGENTS.md
The instructions the AI actually follows
3
user_scanner/__main__.py
Where the program starts running
4
pyproject.toml
Dependencies and the commands it exposes
5
user_scanner/__init__.py
Inside user_scanner/ — the main logic begins here

What's in each folder

user_scanner/Core code — the actual logic1248 files

docs/Documentation5 files

abandoned/Folder14 files

tests/Tests — proof it works20 files

.github/CI / automation (GitHub Actions)5 files

READMEHas testsDocumentedCI checksMIT licenseUpdated this month