thalha-a9

thalha-a9 / helix

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Helix by thalha-a9 — An advanced asynchronous OSINT identity mapper and relation tool.

github.comthalha-a9helix d3js-graph footprinting holehe identity-mapping osint
13
1
69% credibility
Found Jun 01, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Helix is an open-source intelligence tool that maps a person's digital footprint by searching hundreds of websites for matching usernames or email addresses. It combines results into an interactive visual map showing how platforms connect through bio links, supports deep email scanning and username variation testing, and exports findings in multiple formats for investigations or reports.

How It Works

1
🔍 You enter a username or email

You type in someone's username or email address that you want to investigate, like finding all the places a person appears online.

2
🌐 The tool searches hundreds of websites automatically

Behind the scenes, the tool checks your target across 70 or more popular websites simultaneously, looking for matching profiles.

3
🕸️ A visual map appears showing all connections

Instead of a boring list, you see an interactive map where each platform is a dot, and lines connect profiles that share links in their bios — showing how the person links their online identities together.

4
You can dig deeper in different ways
📧
Email deep-dive

Check if an email address shows up on 120+ platforms using a special email scanner

🔢
Username variations

Try common variations like adding numbers or changing separators to find hidden accounts

📡
Expand platform search

Load 400+ additional platforms via Sherlock for even broader coverage

5
📊 You get your results in multiple formats

Results are saved as a graph you can open in any browser, plus reports in text, spreadsheet, and data formats so you can share or archive your findings.

✅ You have a complete picture of someone's online presence

You now know every platform where this person has an account, how their profiles link to each other, and can export everything for your investigation or report.

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Star Growth

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AI-Generated Review

What is Helix?

Helix is a Python OSINT tool that maps someone's entire digital footprint from a single username or email address. It asynchronously checks 70+ platforms out of the box, with optional integration for 400+ more via Sherlock. The real hook is the interactive D3.js graph that visualizes found profiles and extracts cross-links from bios, showing which platforms connect to each other. You get HTML graphs, JSON, CSV, and TXT reports. Run `python helix.py -u johndoe` and within seconds you have a map of where that person exists online.

Why is it gaining traction?

The false positive prevention sets this apart. Rather than naive HTTP 200 checks, Helix uses platform-specific detection: Reddit hits the JSON API, Chess.com and Lichess use their public APIs, GitHub parses og:meta tags, and Twitter requires TLS impersonation via curl_cffi. The permutation scanner generates username variations like johndoe1, john.doe, and realjohndoe, catching people who use slight variations across platforms. The D3.js graph is genuinely useful, with bright edges showing bio-extracted cross-links between profiles. It's the difference between a list of URLs and actual intelligence.

Who should use this?

Security researchers conducting reconnaissance, investigators building dossiers on targets, and developers auditing their own online presence will get the most value. If you're doing legitimate OSINT work—penetration testing, brand monitoring, or fraud investigation—this replaces a pile of browser tabs and manual checks. Hobbyists curious about their digital footprint will enjoy the graph visualization.

Verdict

Helix is a well-architected tool with smart detection logic and a genuinely useful visualization layer. The credibility score of 0.699999988079071% reflects the reality: 13 stars and a single-author project mean this is early-stage software with limited community validation. Install it, read the code, and use it for your own OSINT work—but don't depend on it for mission-critical investigations without testing it against your specific use cases first. The foundation is solid; the maturity needs time.

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