| name | watch-video |
| description | Use ONLY when the user explicitly asks to "Watch Video", "watch-video", or run the Watch Video skill. Builds a multimodal evidence bundle from a video URL or local media file by extracting transcript/subtitles plus ffmpeg screenshots at timed intervals so the AI can inspect what was visible on screen. |
| metadata | {"short-description":"Watch videos with transcript plus timed screenshots"} |
Watch Video
Use this skill only when the user explicitly says Watch Video or names watch-video.
This is different from a transcript job. It creates a bundle with:
- source media from a URL or local video file
- subtitles/transcript, using local WhisperX/Whisper if captions are unavailable
- screenshots sampled with
ffmpeg
watch_context.md, pairing each sampled frame timestamp with nearby transcript textmanifest.json, the machine-readable version of the same context
Command
From the installed skill folder:
python3 "$HOME/.claude/skills/watch-video/scripts/watch_video.py" "VIDEO_URL_OR_FILE" --output-dir "DEST_DIR" --rate 1s
Default rate is 1s, one frame every second.
Supported presets:
4fps
2fps
1fps
1s
3s
5s
10s
Numeric seconds also work:
--rate 15
--rate 0.5
Rate Selection
- Default to
1s.
- Use
4fps or 2fps only when the user needs detailed UI/action analysis or the clip is short.
- Use
3s, 5s, or 10s for long videos, lectures, podcasts, or low-motion screen recordings.
- If the user asks for a specific cadence, use it directly.
Workflow
- Run the script with the requested source and rate.
- Open
watch_context.md and inspect relevant frame links plus transcript context.
- For detailed visual questions, use local image inspection on specific frames rather than relying only on the markdown.
- Answer from both sources: what was said and what was visible.
Dependencies
The script expects:
yt-dlpffmpeg- local
whisperx or whisper for caption fallback
It checks PATH first, then common local install paths:
$HOME/.local/bin/whisperx
$HOME/.local/bin/whisper
$HOME/.buttercut/venv/bin/whisperx
$HOME/.perfect-cuts/venv/bin/whisperx
$HOME/GitHub/Marketing Assets and Skills/.venv-whisperx/bin/whisperx
$HOME/Library/Python/3.9/bin/whisperx
$HOME/Library/Python/3.9/bin/whisper
Optional overrides:
YTDLP_BIN=/path/to/yt-dlp
WHISPERX_BIN=/path/to/whisperx
WHISPER_BIN=/path/to/whisper
WHISPER_MODEL=base
WHISPER_DEVICE=cpu
WHISPER_COMPUTE_TYPE=int8
The script detects yt-dlp in this order:
$YTDLP_BIN
python3 -m yt_dlp
yt-dlp on PATH
