TL;DR: This project implements an agent skill via a SKILL.md file and accompanying Python scripts that interact with mapping data and computer vision tools. Instead of relying purely on visual recognition, it uses single-purpose scripts to query OpenStreetMap, render elevation skylines, and match satellite tiles.
Best for: Developers and OSINT researchers who want to automate photo geolocation using AI coding agents.
Repository snapshot: ⭐ 6 349 stars · 🍴 784 forks · Python · MIT
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What it is
geo-sleuth is an open-source Python project that extends AI coding agents with geolocation capabilities. Instead of relying solely on visual pattern matching, it provides structured scripts that query OpenStreetMap, elevation data, and satellite imagery to determine where a photo was taken.
How it works
The project implements an agent skill via a SKILL.md file and accompanying Python scripts. When provided with an image, the AI model orchestrates single-purpose scripts to query map geometry, render 360-degree horizon skylines from candidate positions, and cross-reference satellite tiles to filter candidates and output a final coordinate estimate with an error radius.
Key features
- Query OpenStreetMap and elevation data for terrain matching
- Render 360-degree horizon skylines from candidate camera positions
- Cross-reference satellite imagery and street view tiles
- Output coordinates with an error radius, heading, and evidence image
- Compatible with Claude Code, Codex, Cursor, Gemini CLI, OpenCode, and GitHub Copilot
Getting started
To use geo-sleuth with your coding agent, clone the repository and ensure you have Python installed along with the required mapping and vision dependencies.
git clone https://github.com/Oldcircle/geo-sleuth.git
cd geo-sleuth
Configure your agent environment to read the SKILL.md instructions and execute the underlying scripts as needed during your workflow.
When not to use it
This tool is not suited for real-time video tracking or environments without access to reliable mapping and elevation data. Run times depend heavily on the compute resources and AI model you use.
Discuss
Which agent framework do you use for running local skills?
Curated from the repository README and metadata. No hands-on test is claimed; evaluate it in a sandbox before production use.
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