🕵️‍♂️ SteamReveal 🕵️‍♀️

April 13, 2026 · View on GitHub

- :wave: Introduction

This repository contains the code for SteamReveal, an advanced OSINT (Open Source Intelligence) tool developed with Next.js and TypeScript. SteamReveal is designed for the Steam community at large, allowing users to uncover hidden profile data, such as a player's possible location and their Close Friends network. For Counter-Strike (CS) players, it also offers a specialized Cheater Probability analysis.

Figure 1 homepage

This repository is divided into parts:

  • Features
  • Technologies Used
  • How it Works
  • Privacy
  • Contact

- :video_game: Features

  • Geographic Triangulation (General Steam): Discovers any player's most likely location by analyzing the public location data of their closest social circle (mutual friend density).
  • Social Graph Analysis (General Steam): Maps the top 20 "Close Friends" based on mutual connection weight rather than just a simple friends list.
  • AI Cheater Probability (CS Exclusive): Calculates the likelihood of a user being a cheater using a machine learning model that analyzes profile comments sentiment, friend ban proximity, account investment, and specific Counter-Strike stats.
  • User-Friendly Interface: Developed with React and Framer Motion to ensure a fluid, modern, and responsive experience.
  • Multilingual Support: Full support for English and Portuguese via next-intl.

To access it, click on the link: SteamReveal

👩‍💻 Technologies Used

In the development of SteamReveal, we used a modern stack to ensure performance and intelligence:

  • TypeScript: For static typing and code reliability.
  • React & Next.js 14: Core framework for server-side rendering and routing.
  • Tailwind CSS & Framer Motion: For utility-first styling and smooth UI animations.
  • SteamAPI & Cheerio: Used to collect official data and scrape public profile comments for sentiment analysis.
  • AI/ML Backend: Integration with a Flask-based prediction model for CS cheater probability.
  • next-intl: Internationalization for global users.

- :grey_question: How it Works

  1. Input: Enter a Steam URL, Custom ID, or SteamID64.
  2. Data Collection: The system fetches the target's friends and public profile data. If the target is a CS player, it also collects game-specific stats and comments.
  3. Triangulation: It identifies "Close Friends" by mutual count and aggregates their public locations to find the target's geographic hub.
  4. AI Prediction (for CS): A set of features (ban history, comment sentiment, account age/value) is sent to a machine learning model to estimate cheater probability.
  5. Output: Displays possible locations, the social graph, and the specialized Cheater Report when applicable.

Figure 2 results

- :lock: Privacy

If you do not wish for your information to be publicly available, please visit the Steam Privacy section to adjust your profile settings.

- :telephone_receiver: Contact

E-mail: walterfelipeberchez@outlook.com