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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.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Give any agent senses (video/audio/image understanding) and OSINT reach (search/capture/monitor) organized around an investigation case. Use when the user wants to analyze a video/audio/image, scan or monitor sources for a target, or ask/brief over accumulated findings. Drives the `overcast` CLI (built on pi + the tinycloud/Cloudglue perception backend); see reference/verbs.md for the full verb surface.
Install and configure overcast for this harness: install the CLI, verify the system ffmpeg, and configure reusable provider profiles. Use once per machine/profile before driving the `overcast` skill.
Turn a case into a visual evidence board — materialize face and object crops, link the same person across clips, connect themes, and render the corkboard as a CSI brief plus a live monitor wall.
Make unreadable footage legible — denoise/upscale a marked moment, re-run OCR and detection on the enhanced output, and record what was recovered with honest provenance (interpolation is a lead, not proof).
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.
Run a standing surveillance watch on public sources for a target — auto-sense new media, auto-suggest matches into a triage queue, and keep a live control-room wall — so new evidence surfaces itself over time.
| 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. |
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.
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
Have the moment (use overcast-pinpoint / overcast-frame-grid): timestamp
T on record REC.
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.
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
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.
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.