Observe actual video and sound over the whole file or named ranges, with timestamped evidence and honest coverage. Use complementary playback, listening and targeted frames; return observations without edits or verdicts.
Install gooseworks: https://gooseworks.ai/install.md # then, in Claude Code, Cursor, or Codex: /gooseworks use the watch skill
Report what the actual file shows and sounds like, with timestamps. Keep sampled frames, moving-picture review, listening and transcription separate. Missing a capability stays explicit; observations are not an approval verdict.
Accept the video, optional timestamp ranges, sampling rate, frame budget, resolution, audio dimensions and focus. Defaults describe whole-file scope, not completed review. Read the parameter reference for how to partition work without omitting intervals.
Before reporting, resolve exact source/version, checksum, duration and actual reviewer capabilities. Returned audio bytes or transcription do not establish perception. Use complete normal-speed moving-picture and sound review for a full audiovisual observation. Add sampled frames for scene/text evidence and suitably dense, original-resolution motion/mouth inspection for suspicious intervals. Sparse frames cannot certify action continuity or fine lip-sync. There is no universal two-fps ceiling for targeted inspection; remain within source rate and available tools.
Return readable observation report, sampled frames and their source times, optional transcript/word timing, available audio artifacts and manifest. The manifest records source identity/checksum/duration, requested and observed ranges, reviewer capability, separate frame/motion/listening/transcription coverage, sampling/resolution, file paths and remaining gaps. Do not rename frames plus transcript as frames-and-audio.
A full-file request with partial coverage remains partial. Audio disabled intentionally is frames/motion-only; audio enabled without perception is unverified. A missing backend does not justify an invented transcript. The downstream judge compares these observations with the creative contract and makes its own evidence-bound verdict.
Read parameters for observation controls and coverage.
Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.