🧭 geo-sleuth

An agent skill that finds where a photo was taken — and shows its work.

No text. No plates. No landmarks. One bridge, one mountain. Located to within 2 m.

Quick start

npx skills add Oldcircle/geo-sleuth

Pick your agents when prompted. Then hand your agent a photo and say:

find where this photo was taken

On first use, ask the agent to run doctor.py from the installed skill’s scripts/ folder and address any failed checks (see Requirements and setup). That is the whole interface. The agent reads SKILL.md, runs the scripts, and comes back with the camera position, the direction it was facing and a satellite evidence image. Prefer to copy the folder yourself? See Installation.

Why geo-sleuth

• One photo, one sentence. Give your agent a photo and say find where this photo was taken. You get back the camera position, the direction it was facing, and a satellite evidence image.
• It works when there is nothing to read. No sign, no plate, no landmark: OpenStreetMap geometry, elevation data, satellite tiles and street view carry the search on their own.
• Geometry instead of guesswork. Pier spacing becomes a distance ruler, shadows become a bearing, a ridge line becomes a fingerprint that elevation data can be matched against.
• Every claim points at a file. A conclusion has to name the command that ran in the session and the file it produced. Population and fame are not evidence.
• Scripts rank, the model judges. Twenty single-purpose scripts search, score and sort; the model only picks among the top few.
• One skill, every agent. A standard Agent Skill — SKILL.md plus plain Python scripts — so the same folder runs in Claude Code, Codex, Cursor, Gemini CLI, OpenCode and GitHub Copilot.
• Answers carry an error radius. Coordinates ± radius, the camera heading, an evidence image and a graded confidence.

Cases

None of these photos had GPS data. Each case lists what the skill read from the photo, how it narrowed the search, and how far the result landed from the confirmed camera position.

Each case is a record of one run by one model. The skill tells the agent how to work; what to query, how to filter and how many top results to check are the model's own decisions, so a different model or different decisions may not reach the same result. Under each case, Reproduction conditions and checkpoints lists the input, the hint, the key parameters and what each stage should still cover, so you can find the step where your own run diverged.

1. A rice paddy and a viaduct

A phone photo with the EXIF stripped: a white oven at the edge of a harvested rice paddy, a long viaduct in the distance, a steep mountain on the right. Not a single character in the frame. One message to an agent with this skill installed, and it came back with the camera position and the direction the camera was facing.

photo → 27,335 → 171 → 14,372 → 22 → 3 → 1 → ±2 m

• What this record is: the table is the first time this method solved the photo (Claude Opus 5.0), not a blind re-run; its steps were later written into the skill.
• Input: a 1280×1707 phone photo with EXIF stripped. The images in this README are display versions and are not suitable as input.
• Candidates: all 27,335 OSM railway-bridge segments in the region, excluding only those tagged electrified=no (as rail_mtn_scan.py did then and terrain.py scan does by default now), with no highspeed filter. Catenary masts only show that the line is electrified; the correct line, the Guangzhou–Qingyu