r/osinttools

Hi everyone, I'm working on SpyBrain, an OSINT desktop tool for Windows, and I'd really appreciate a few people trying it and telling me what works and what doesn't.

It brings username and domain searches, email breach checks and photo geolocation into one interface. Photo geolocation runs locally after the initial model download; the online searches still use external sources. It builds on tools including Sherlock, Maigret and GeoCLIP.

The code and setup instructions are here: https://github.com/skriptkiddy98x/spybrain

I'd especially like feedback on installation, failed searches, false positives and whether the interface is useful. If you find a bug, please leave a comment or open a GitHub issue with your Windows version and steps to reproduce it, without posting anyone's personal data. Please use your own data or data you have permission to test.

It's an early project, so honest criticism is welcome. Thanks to anyone who gives it a try!

Hi r/osinttools,

I’ve been working on a new update for Ferret OSINT, my mobile OSINT app for iPhone and Android.

It’s meant for quick, focused checks on the go: DNS, WHOIS, subdomains, and public email discovery, without extra clutter. Results stay on your device, and the app is built around local storage and mobile-first workflows.

What’s new

• Shodan integration: export one or multiple subdomains to Shodan for deeper infrastructure analysis, and switch between targets from the Shodan screen.
• Export & share: long-press any result to copy or share it through the iOS share sheet.

Core features

• DNS Lookup.
• WHOIS.
• Subdomain Discovery.
• Public Email Discovery.
• Shodan export for follow-up analysis.

This is aimed at lawful security research, digital footprint analysis, and technical investigation using public sources.

Links for download:

• Android

https://play.google.com/store/apps/details?id=com.kssecurity.ferret

• iOS

https://apps.apple.com/us/app/ferret-osint/id6760048642

If anyone tries it, I’d be interested in hearing what works, what feels unnecessary, and what you’d want to see in the next version.

Your digital footprint is bigger than you think.

One email address or one username can tie back to hundreds of accounts across the internet. user-scanner checks over 1,080 platforms in seconds: 200+ email vectors and 880+ username vectors. It pulls avatars, bios, follower counts, UIDs, seller statuses, basically everything that's publicly exposed.

The part that makes it interesting is the pivot engine. Say it finds a secondary handle or an exposed email on one of those 880 platforms. It automatically pivots and runs a new scan on that, chaining results without you doing anything manual.

It also ships with an MCP server, so AI agents (Claude Desktop, Cursor, and others) can run scans autonomously and follow those pivots on their own.

Worth noting: this is strictly for authorized security research and defensive OSINT. The repo makes that clear upfront.

37 contributors, pure Python, and actively maintained with a release just two weeks ago.

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

Metric	Value
Stars	5,046
Forks	--
Last release	v1.5.2 (2026-09-17)
Contributors	37
Languages	Python 100%

This one needs a disclaimer upfront: GhostTrack is a security research and educational tool. Using it to track someone without their knowledge or consent is illegal in most places. Treat it accordingly.

With that out of the way.

GhostTrack is a Python-based OSINT tool that lets you trace phone numbers and gather location data. You feed it a number and it pulls carrier info, approximate location, and related metadata from public sources.

16,000 stars. That is a lot for a tool in this space.

The project is straightforward. Pure Python, small team of about 15 contributors, no formal releases (you clone and run). The codebase is lean and does not try to be more than what it is.

What it is good for: security researchers testing exposure, penetration testers mapping targets during authorized engagements, and people learning how OSINT works hands-on. There is real educational value in seeing what publicly available data can reveal about a phone number.

What it is not: a stalking tool, a spy app, or something you run on your ex's number. The ethical line here is clear and the project's README does address responsible use.

The accuracy depends heavily on the data sources it queries. Some lookups return solid carrier and region data. Others return very rough approximations. Do not expect GPS-level precision from a tool that works through public databases.

https://github.com/HunxByts/GhostTrack

Metric	Value
Stars	16,103
Forks	2,200
Last release	No releases
Contributors	15+
Languages	Python (primary)