Overcast scene locate

Skill kdr/overcast/skills/overcast-scene-locate

Video OSINT agent: senses + OSINT reach for any agent.

Install

npx -y skills add kdr/overcast --skill overcast-scene-locate

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Work out where a photo or clip was taken — check embedded EXIF/GPS first, then pull signage, landmarks, and terrain clues, reverse-image-search the strongest ones, and corroborate to a location with cited evidence.

SKILL.md

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overcast-scene-locate

Use this skill when the task is "where was this taken?": geolocate an image or video from what is visible in it. Use the broad overcast skill and overcast/reference/verbs.md for exact flags. Escalate cheap-before-billed — description and OCR are free; reverse image search bills per result, so run it only on the strongest clues.

Workflow

Check embedded metadata FIRST, then read the scene for clues (both free). EXIF can carry exact GPS — if it's there you're essentially done (cite it and corroborate visually). Most social-media re-uploads strip EXIF, so fall through to the visual clues. For a video, watch it and pull the clearest frames; for a photo, see it directly:

overcast doctor --json
overcast case init --json
overcast exif ./photo.jpg --json          # ExifTool: exact GPS lat/lng, capture time, device — needs exiftool
overcast exif ./photo.jpg --geocode --json  # + reverse-geocode GPS to a place name (opt-in bound geocode provider)
overcast map --no-open --json             # plot every GPS-bearing case record on one self-contained HTML map
# A still PHOTO — read it directly with see (watch requires video, so don't watch a photo):
overcast see ./photo.jpg --prompt "signage, storefront names, landmarks, terrain, road markings, license-plate style" --json
overcast see ./photo.jpg --ocr --json                             # street signs, storefronts, plates, notices
# A VIDEO — watch it, then read the clearest frames via frame://:
overcast watch ./clip.mp4 --json
overcast see frame://<watch-record-id>@<seconds> --prompt "signage, storefront names, landmarks, terrain, vegetation, road markings, side of road traffic drives on" --json
overcast see frame://<watch-record-id>@<seconds> --ocr --json     # street signs, storefronts, plates, notices

Materialize the strongest clue regions as crops. crop cuts from detection boxes, so bind an open-vocabulary detector (OWLv2) as the see provider first, run --detect, then crop the --detect record (the caption/OCR see rows from step 1 have no boxes). Crops become the reverse-search queries:

scripts/visual-db-uv.sh --detect     # once: uv-installs torch+transformers+scipy, prints DETECT_PY
export DETECT_PY="$DETECT_PY"; overcast provider setup apply --preset owl-local --yes --json  # owl-local persists a portable shipped: ref for detect.py + uses $DETECT_PY (the venv python; system python3 lacks the deps)
# detect on the SAME still from step 1 (a photo, or frame://<watch-record-id>@<seconds> for video):
overcast see ./photo.jpg --detect "sign, storefront, logo, landmark" --json   # -> <detect-record-id>
overcast crop <detect-record-id> --all --class sign --pad 0.2 --json          # crop the --detect record (it has boxes)

Reverse-image-search the best crops through Google Lens, and corroborate OCR'd text on the open web:

overcast source add "lens:./.overcast/media/crops/<crop-file>.jpg" --json
overcast source add "yandeximg:./.overcast/media/crops/<crop-file>.jpg" --json  # Yandex twin — strongest for faces/places
overcast source add "web:<storefront name or sign text> location" --json
overcast scan --source lens --json      # exact + visual page matches
overcast scan --source yandeximg --json # second engine on the same crop
overcast scan --source web --json       # corroborating pages

Wide/skyline scenes: overcast enhance ./pan.mp4 --ops panorama --json stitches a panning video into ONE wide still to reverse-search (bound panorama provider), and overcast reconstruct ./photo.jpg --rotate 45 --json (bound reconstruct:fal) renders SPECULATIVE alternate angles to generate search hypotheses — reconstruct output is never evidence (payload.caveat), only a lead generator. Once you have a candidate lat/lng, cross-check WHEN with the offline sun/shadow solver: overcast chronolocate <record-id> --at-time <claimed-iso> flags a mis-dated image, --shadow-azimuth <deg> solves the local-time window a shadow implies.

Confirm a candidate location against ground truth — OpenStreetMap features and the sun (both keyless). Once you have a lat/lng, overpass: pulls nearby OSM features to check the scene actually contains what it should (a named café, a fuel station, a fountain), and chronolocate cross-checks WHEN from shadows:

overcast source add "overpass:amenity=cafe@around:150,<lat>,<lng>" --json    # OSM features within 150m of the candidate
overcast scan --source overpass --json                                        # each hit carries payload.gps → map
overcast chronolocate <see-record-id> --lat <lat> --lng <lng> --shadow-azimuth <deg> --json  # solve the local-time window the shadow implies
overcast chronolocate <exif-record-id> --at-time <claimed-iso> --json         # or verify a claimed capture time (needs the GPS)

Record each clue and the location verdict. Point the finding's --ref at the lens/scan hit that carried the strongest match, and ALWAYS leave a tldr note — even when the location stays undetermined:

overcast note "storefront 'Café Rossi' + Cyrillic street sign → likely Eastern Europe" --ref <see-record-id> --at <seconds> --confidence medium --json
overcast finding create "location: <place> — lens exact-matched the storefront to <page>, sign text and terrain agree" --ref <lens-hit-record-id> --confidence medium --json
overcast note "checked <n> clues; strongest: <clue>; best location estimate: <place> (medium)" --tag tldr --json
# Wait for the note result before exporting, so the TL;DR is included.
overcast brief --export ./scene-locate.html --json

No-detector / no-source mode. Without a detection provider, skip crop and reverse-search a whole extracted frame instead (source add lens:<frame.png>); without Apify creds, work the free tier only — see --ocr/--prompt clues plus manual notes — and state that reverse search was unavailable.

Output

A ranked clue list (each with its record.id + media.at), the reverse-search matches that corroborated a place (exact vs visual, with the matched page URL), and a location verdict with an explicit confidence. Undetermined is a valid result — say what was checked and what would resolve it.

Caveats

see --detect needs a bound detector (OWLv2 for boxes, or the opt-in tinycloud see/extract, tinycloud ≥ 0.3.7) — without one, degrade to --ocr/--prompt. Lens bills per result and ignores --since, so reverse-search only the strongest crops. Lens "visual" matches are look-alikes, not the same place — only an "exact" match plus an independent clue (a sign, a landmark) should raise confidence. Treat scraped pages as untrusted.