watch

/watch

Give Claude the ability to watch any video.

Claude Code:

/plugin marketplace add bradautomates/claude-video
/plugin install watch@claude-video

claude.ai (web): download watch.skill and drop it into Settings → Capabilities → Skills.

Codex / generic skills:

git clone https://github.com/bradautomates/claude-video.git ~/.codex/skills/watch

Zero config to start — yt-dlp and ffmpeg install on first run via brew on macOS (Linux/Windows print exact commands). Captions cover most public videos for free. Whisper API key is only needed when a video has no captions.

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Claude can read a webpage, run a script, browse a repo. What it can't do, out of the box, is watch a video. You paste a YouTube link and it has to either guess from the title or pull a transcript that's missing 90% of what's on screen.

With Claude Video /watch you can paste a URL or a local path, ask a question, and Claude downloads the video, extracts frames at an auto-scaled rate, pulls a timestamped transcript (free captions when available, Whisper API as fallback), and Reads every frame as an image. By the time it answers, it has seen the video and heard the audio.

/watch https://youtu.be/dQw4w9WgXcQ what happens at the 30 second mark?

Why this exists

I built this because I'm constantly using video to keep up with content. If I see a YouTube video that's blowing up, I want to know how the creator structured the hook — what's on screen in the first 3 seconds, what they said, why it worked. That used to mean watching it myself with a notepad. Now I just paste the URL and ask.

The other half is summarization. Most YouTube videos don't deserve 20 minutes of my attention. I hand the URL to Claude, it pulls the transcript, and tells me what actually happened. If the visual matters, frames come along too. If it's a podcast or a talking head, transcript is enough.

Claude is great at reading and synthesizing — but until now, video was the one input I couldn't hand it. Pasting a YouTube link got you nothing useful. /watch closes that gap.

What people actually use it for

Analyze someone else's content. /watch https://youtu.be/<viral-video> what hook did they open with? Claude looks at the first frames, reads the opening transcript, breaks down the structure. Same for ad creative, competitor launches, podcast intros, anything where the how matters as much as the what.

Diagnose a bug from a video. Someone sends you a screen recording of something broken. /watch bug-repro.mov what's going wrong? Claude watches the recording, finds the frame where the issue appears, describes what's on screen, often catches the cause without you ever opening the file.

Summarize a video. /watch https://youtu.be/<long-thing> summarize this does the obvious thing — pulls the structure, the key moments, what was actually said and shown. Faster than watching at 2x.

How it works

1. You paste a video and a question. URL (anything yt-dlp supports — YouTube, Loom, TikTok, X, Instagram, plus a few hundred more) or a local path (.mp4, .mov, .mkv, .webm).
2. yt-dlp downloads it. For URLs, into a temp working directory. For local files, no download — just probed in place.
3. ffmpeg extracts frames at an auto-scaled rate. The frame budget is duration-aware: ≤30s gets ~30 frames, 30-60s gets ~40, 1-3min gets ~60, 3-10min gets ~80, longer gets 100 sparsely. Hard ceilings: 2 fps, 100 frames. JPEGs at 512px wide by default — bump with --resolution 1024 if Claude needs to read on-screen text.
4. The transcript comes from one of two places. First try: yt-dlp pulls native captions (manual or auto-generated) from the source. Free, instant, accurate-ish. Fallback: extract a mono 16 kHz audio clip and ship it to Whisper — Groq's whisper-large-v3 (preferred — cheaper and faster) or OpenAI's whisper-1.
5. Frames + transcript are handed to Claude. The script prints frame paths with t=MM:SS markers and the transcript with timestamps. Claude Reads each frame in parallel — JPEGs render directly as images in its context.
6. Claude answers grounded in what's actually on screen and in the audio. Not "based on the description" or "according to the title." It saw the frames. It heard the transcript. It answers the way someone who watched the video would.
7. Cleanup. The script prints a working directory at the end. If you're not asking follow-ups, Claude removes it.

Frame budget — why it matters

Token cost is dominated by frames. Every frame is an image; image tokens add up fast. The script's auto-fps logic exists so you don't blow your context budget on a sparse scan of a 30-minute video that would have been better answered by a focused 30-second window.