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overcast-pinpoint
Pinpoint WHEN a specific thing happens in a video — funnel from cheap coarse candidates (shots / CLIP / grid) to a frame-verified time window.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Pinpoint WHEN a specific thing happens in a video — funnel from cheap coarse candidates (shots / CLIP / grid) to a frame-verified time window.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
| name | overcast-pinpoint |
| description | Pinpoint WHEN a specific thing happens in a video — funnel from cheap coarse candidates (shots / CLIP / grid) to a frame-verified time window. |
Use this skill to answer "exactly when does X happen?" in a clip and back it
with evidence the model actually looked at. It mirrors the temporal-search
pattern from the VLM video literature (T* / VideoAgent): score cheaply over the
whole clip, then spend expensive VLM calls only on a few candidate frames and
zoom in. Use the broad overcast skill and overcast/reference/verbs.md for
exact flags.
Two rules that make the answer trustworthy:
[t1-t2] plus one verified keyframe.see-verified. Never emit a
time the model merely guessed — models answer correctly while grounding on the
wrong moment, so confirm by looking at the frame.see frame:// needs media on disk —
capture a remote clip first). watch also gives per-shot timestamped content
to search:overcast doctor --json
overcast case init --json
overcast watch ./clip.mp4 --json # -> video.analysis record id (REC)
overcast ask "moments where <X> happens, with timestamps" --json # over watch shots/notes
overcast grid ./clip.mp4 --count 16 --json # one contact sheet ...
overcast see <montage-path> --prompt "which numbered cells show <X>? give cell numbers" --json
overcast similar search "<X>" --index <basic-clip-id> --json # if a local CLIP index exists
overcast ask "moments <X> happens" --index <media-descriptions-id> --probe --json # remote index
For grid, translate the chosen cell number to a time via the grid record's
payload.cells[n].at (don't trust a model-guessed time). CLIP/shots only
SHORTLIST — CLIP is weak on actions/order — so verify next.
overcast see frame://REC@T --prompt "Is <X> happening here? answer yes/no and what you see" --json
# refine: sample T-d and T+d, halve d each round until adjacent frames flip yes<->no
overcast see frame://REC@<T-2> --prompt "Is <X> happening?" --json
overcast see frame://REC@<T+2> --prompt "Is <X> happening?" --json
overcast note "<X> occurs" --ref REC --at <t1-t2> --confidence medium --json
overcast view REC --at <t1-t2> --json
overcast brief --export ./pinpoint.md --json
The tightest [t1-t2] window, the single see keyframe that confirms it (its
record.id), and how it was found (shots / grid / CLIP / probe). Cite
record.id + media.at for every claim; state the window, not a false-precise
single frame.
Needs the video local (a URL-only watch record can't extract frames). CLIP and
shot text shortlist but don't decide — the see frame check does. If X recurs,
pinpoint each occurrence separately; don't collapse them into one window.
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.