name

watch-video

description

Give Claude eyes and ears on any online video, then analyze it. Downloads a video from a URL (YouTube, Instagram, X, Vimeo, and 1000+ yt-dlp sites), extracts key frames, and gets a transcript (native captions, else local Whisper), then hands the frames + timestamped transcript to Claude so it can produce a full breakdown — hook, format, storytelling structure, pacing and visual layout — grounded in what's actually on screen AND said. Pass several URLs to batch-analyze dozens at once and surface the patterns the best ones share. Use whenever the user shares a video link (or many) and wants it watched, analyzed, broken down, summarized, or turned into a script/notes.

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:

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).
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
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.

watch-video — let Claude actually watch a video

How to use it

When the user gives a video URL:

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).

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

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

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.