GeoInt

An agent skill for finding where any photo, screenshot, video frame or Street View image was taken — the instincts of a strong GeoGuessr player plus the discipline of an OSINT investigation. The agent leads with its own expert read of the scene (a written gut call), then proves, sharpens or overturns it with web research and local tools, down to street or camera level when the evidence allows. Every photo gets its own timestamped archive with a report, evidence images and a scoreboard once the true location is known.

SKILL.md is the method; references/ holds the playbooks and the worldwide clue library; scripts/ holds the tools.

Install

git clone https://github.com/marouane53/GeoInt.git ~/.agents/skills/geoint   # Codex and other agents
ln -s ~/.agents/skills/geoint ~/.claude/skills/geoint                         # Claude Code
uv run ~/.agents/skills/geoint/scripts/doctor.py --network                        # check the setup
uv run ~/.agents/skills/geoint/scripts/doctor.py --models                         # optional: pre-download local models (~6.2 GB)

Requirements: Python 3.10+, uv, curl; exiftool and ffmpeg recommended. Each script declares its own dependencies and runs with uv run. Per-photo archives go to ~/geoint-photos with timestamps in this computer's time zone; set GEOINT_PHOTOS / GEOINT_TZ, or put {"photos_root": "…", "timezone": "…"} in a config.local.json next to SKILL.md (git-ignored), to change that.

What's inside

Area	Tools
Archive and scoring	photo_session.py (start / finish / truth / scoreboard / list), bench.py
One-command local first pass	recon.py → meta.py, intake.py, ocr.py, textgeo.py, detect.py, calib.py, prior.py
Text → place	textgeo.py (scripts, telltale letters, language, phone numbers incl. unreadable digits, domains, postal codes, currency, regional words, ~50k brands, GeoNames places), geodata.py, clues.py (incl. 2,100 regional plate codes across ten countries, with the years each was in use)
Reasoning ledger	board.py — candidates, clues, likelihood ratios capped by evidence type, exclusion rules, rank / next / check / report
Street level	pano.py (every provider plus your own manifests: coverage, list, render, sheet), sweep.py (rank every panorama of whole towns with MegaLoc), gsv.py, refsheet.py (real Street View rows per candidate country, region or town), match.py, baidu_pano.py
Clue → short list	opendata.py (city open-data portals: bus lanes, street trees, hydrants… filtered to candidate points), osm.py addr (house numbers, alone or co-occurring)
Maps and geometry	osm.py, poi.py, gazetteer.py, tiles.py (incl. NASA daily satellite passes), sat_scan.py, terrain.py, sun.py (incl. past weather by day), geo.py, pose.py, evidence.py (incl. colour-coded match sheets)
Local models	StreetCLIP + GeoCLIP world prior (prior.py), OWLv2 object crops (detect.py), GeoCalib camera calibration (calib.py), MegaLoc place recognition (sweep.py, match.py), DINOv2/CLIP matching

The skill works end to end without asking questions: reverse image search, web search, maps and Street View lookups run automatically as part of the flow.

Tests

uv run tests/test_tools.py && uv run tests/test_runtime.py && python3 -m unittest tests/test_photo_session.py

Licences and data

Code: MIT (see LICENSE, which keeps the notice for reused portions). Data: GeoNames (CC BY 4.0), Wikipedia-derived tables (CC BY-SA 4.0), OpenStreetMap name-suggestion-index (downloaded at runtime); details in data/README.md and references/data-sources.md. Model weights are downloaded from their publishers under their own licences (StreetCLIP is non-commercial); see references/models.md.