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 wordswatch_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.
1---2name: watch-video3description: 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.4---56# watch-video — let Claude actually watch a video78Most 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.910## How to use it1112When the user gives a video URL:13141. Run the helper (it downloads, samples frames, and transcribes):15 ```16 python scripts/watch.py "<VIDEO_URL>" --out watch_out17 ```18 Useful flags: `--max-frames 60` (cap frames), `--no-whisper` (captions only, fully free/offline), `--every 0` (auto-pace by duration).19202. It writes:21 - `watch_out/frames/f000.jpg …` — sampled frames (named by index)22 - `watch_out/transcript.txt` — the spoken words23 - `watch_out/index.json` — `[{frame, t, file}]` so each frame maps to its timestamp24253. **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.2627## Analyze — the full breakdown2829Once `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:3031- **Hook (first ~3s)** — exactly what's on screen and said in the opening, and *why* it stops the scroll.32- **Format** — the style (talking-head, faceless motion-graphics, screen-record, split-screen…) and how captions / on-screen text are used.33- **Storytelling structure** — beat by beat: each scene or idea, its timestamp, and what changes on screen at that moment.34- **Pacing** — average beat length, cut rhythm, where it slows or accelerates.35- **Visual layout** — framing, where text sits, the graphics technique, b-roll vs. demo.36- **Transcript** — the full spoken words (from `transcript.txt`).3738That's the same breakdown you'd want before modeling a video: hook → format → structure → pacing → layout, all evidence-backed.3940## Batch — analyze many at once, find the patterns4142Pass several URLs to watch them all in one run:43```44python scripts/watch.py "<url1>" "<url2>" "<url3>" --out batch_out45```46It 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.4748## Notes49- 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.50- For long videos, frames are sampled sparsely (duration-aware) so the frame count stays manageable for vision.
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