overcast-where

name	overcast-where
description	Locate WHERE in a frame a target is — open-vocab detect, crop the box, then VLM-verify the crop so hallucinated boxes don't survive.

overcast-where

Use this skill to turn a moment into spatial evidence: a bounding box on the target plus a verified crop. It follows the detector-proposes / VLM-verifies pattern (open-vocab detection like OWLv2, then confirm the crop) — because chat VLMs are unreliable at emitting raw coordinates, so a real detector draws the box and the VLM only judges the crop. Use the broad overcast skill and overcast/reference/verbs.md for exact flags.

Setup

see --detect needs a detection provider bound (boxes come from OWLv2, not the brain LLM):

scripts/visual-db-uv.sh --detect     # once: prints DETECT_PY (the venv python)
export DETECT_PY="$DETECT_PY"; overcast provider setup apply --preset owl-local --yes --json  # persists a portable shipped: ref for detect.py + the venv python

Workflow

1. Have the moment (use overcast-pinpoint / overcast-frame-grid): timestamp T on record REC.

2. Detect the target in that frame, then materialize + verify the box:

overcast see frame://REC@T --detect "<target phrase>" --json      # -> see record with detections[]
overcast crop <see-record-id> --all --class "<target phrase>" --pad 0.15 --json
overcast see <crop-path> --prompt "Does this crop show <target>? yes/no + describe" --json

The re-see of each crop is what kills false positives — open-vocab detectors emit confident boxes for almost any phrase at low thresholds.

3. Optionally sharpen the exhibit and record the finding:

overcast enhance <crop-path> --ops upscale,denoise --json
overcast finding create "<target> located at T" --ref <see-record-id> --confidence medium --json
overcast brief --export ./where.md --json

Output

Per confirmed target: the timestamp, the box (from the see --detect record), the crop path, and the verification verdict. Cite the see detection record.id + media.at; note the crop is the durable, memory-friendly evidence artifact.

Caveats

Never ask the brain LLM for coordinates directly — bind a detector and verify crops. Detector confidence is not calibrated across free-form phrases: a high score on a rare phrase can still be wrong, so the crop re-check decides. For a specific PERSON, use face --match instead of --detect. Boxes are per sampled frame, not tracks.