watch-video

watch-video — let Claude actually watch a video

Most tools only read a transcript and miss everything visual. This skill gives Claude the frames + the words, aligned on one timeline, so it can reason about cuts, on-screen text, demos, and pacing — not just the audio.

How to use it

When the user gives a video URL:

1. Run the helper (it downloads, samples frames, and transcribes):

   python scripts/watch.py "<VIDEO_URL>" --out watch_out
   

   Useful flags: --max-frames 60 (cap frames), --no-whisper (captions only, fully free/offline), --every 0 (auto-pace by duration).

2. It writes:

   • watch_out/frames/f000.jpg … — sampled frames (named by index)
   • watch_out/transcript.txt — the spoken words
   • watch_out/index.json — [{frame, t, file}] so each frame maps to its timestamp

1. Read the frames as images (use the Read tool on the PNG/JPGs) alongside transcript.txt, lining each frame up with what's being said at its t. Then answer the user's request (summary, breakdown, hook analysis, study notes, a fresh script, etc.) grounded in BOTH the visuals and the audio.

Analyze — the full breakdown

Once watch.py has run, read the frames + transcript.txt aligned by index.json, then write a structured breakdown. Ground every point in a specific frame t and/or transcript line:

• Hook (first ~3s) — exactly what's on screen and said in the opening, and why it stops the scroll.
• Format — the style (talking-head, faceless motion-graphics, screen-record, split-screen…) and how captions / on-screen text are used.
• Storytelling structure — beat by beat: each scene or idea, its timestamp, and what changes on screen at that moment.
• Pacing — average beat length, cut rhythm, where it slows or accelerates.
• Visual layout — framing, where text sits, the graphics technique, b-roll vs. demo.
• Transcript — the full spoken words (from transcript.txt).

That's the same breakdown you'd want before modeling a video: hook → format → structure → pacing → layout, all evidence-backed.

Batch — analyze many at once, find the patterns

Pass several URLs to watch them all in one run:

python scripts/watch.py "<url1>" "<url2>" "<url3>" --out batch_out

It writes each video to batch_out/v01, v02, … (frames + transcript + index per video) plus a batch_out/batch.json manifest. Analyze each (above), then compare across all of them to surface what the strong ones share — repeated hook shapes, common beat lengths / structure, recurring visual moves: the trends, insights and patterns you'd never spot watching one at a time.

Notes

• Captions are used when the platform has them (free). Otherwise it falls back to local faster-whisper (also free, runs on CPU). Pass --no-whisper to skip transcription entirely.
• For long videos, frames are sampled sparsely (duration-aware) so the frame count stays manageable for vision.