Watch a video from YouTube, Instagram, X/Twitter, Vimeo, TikTok or any of ~1800 yt-dlp sites (or a local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or local mlx-whisper fallback, no API key), and hands the result to Claude so it can answer questions about what's in the video.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Watch a video from YouTube, Instagram, X/Twitter, Vimeo, TikTok or any of ~1800 yt-dlp sites (or a local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or local mlx-whisper fallback, no API key), and hands the result to Claude so it can answer questions about what's in the video.
argument-hint
<video-url-or-path> [question]
allowed-tools
Bash, Read
homepage
https://github.com/mathiaschu/claude-video
repository
https://github.com/mathiaschu/claude-video
author
Mathias Schusterman (fork of bradautomates/claude-video)
license
MIT
user-invocable
true
/watch — Claude watches a video
You don't have a video input; this skill gives you one. A Python script downloads the video, extracts frames as JPEGs, gets a timestamped transcript (native captions first, then local mlx-whisper as fallback — runs on-device, no API and no key), and prints frame paths. You then Read each frame path to see the images and combine them with the transcript to answer the user.
Step 0 — Setup preflight (runs every /watch invocation, silent on success)
Python interpreter: every python3 ... command in this skill is for macOS/Linux. On Windows, substitute python — the python3 command on Windows is the Microsoft Store stub and will not run the script.
Before every /watch run, verify that dependencies are in place:
This is a <100ms lookup. On exit 0, the script emits nothing — proceed to Step 1 without comment. Do NOT announce "setup is complete" to the user — they don't need a status message on every turn. The only acceptable user-visible output from Step 0 is when remediation is required.
On non-zero exit, follow the table:
Exit
Meaning
Action
2
Missing binaries (ffmpeg / ffprobe / yt-dlp)
Run installer
3
No local whisper engine (mlx-whisper / openai-whisper)
Run installer, then tell user the pip3 command it prints
4
Both missing
Run installer
The installer is idempotent — safe to re-run:
python3 "${CLAUDE_SKILL_DIR}/scripts/setup.py"
On macOS with Homebrew, it auto-installs ffmpeg and yt-dlp. On Linux/Windows, it prints the exact install commands for the user to run. For transcription it checks for a local whisper engine ( preferred on Apple Silicon, as a CPU fallback) and prints the command if neither is present.
mlx-whisper
openai-whisper
pip3 install
No API key, no config file, no .env — transcription runs entirely on-device.
If no whisper engine is installed: run the installer and relay the exact pip3 install … command it prints (mlx-whisper on Apple Silicon, openai-whisper on Windows/Linux/Intel Macs — do not assume mlx, it only installs on Apple Silicon). If they don't want to install it, proceed with --no-whisper and tell them videos without native captions will come back frames-only.
Structured mode (optional):python3 "${CLAUDE_SKILL_DIR}/scripts/setup.py" --json emits {status, missing_binaries, whisper_backend, has_whisper, platform} where status is one of ready | needs_install | needs_whisper | needs_install_and_whisper.
Within a single session, you can skip Step 0 on follow-up /watch calls — once --check returned 0, nothing about the environment changes between turns.
When to use
User pastes a video URL (YouTube, Vimeo, X, TikTok, Twitch clip, most yt-dlp-supported sites) and asks about it.
User points at a local video file (.mp4, .mov, .mkv, .webm, etc.) and asks about it.
User types /watch <url-or-path> [question].
Recommended limits
Best accuracy: videos under 10 minutes. Frame coverage scales inversely with duration.
Hard caps: 100 frames total and 2 fps. Token cost grows with frame count, so the script targets a frame budget by duration (and never exceeds 2 fps even when the budget would imply more):
If the user hands you a long video, consider asking whether they want a specific section before burning tokens on a sparse scan.
How to invoke
Step 1 — parse the user input. Separate the video source (URL or path) from any question the user asked. Example: /watch https://youtu.be/abc what language is this in? → source = https://youtu.be/abc, question = what language is this in?.
Step 2 — run the watch script. Pass the source verbatim. Do not shell-escape it yourself beyond normal quoting:
--start T / --end T — focus on a section. Accepts SS, MM:SS, or HH:MM:SS. When either is set, fps auto-scales denser (see "Focusing on a section" below).
--max-frames N — lower the cap for tighter token budget (e.g. --max-frames 40)
--resolution W — change frame width in px (default 512; bump to 1024 only if the user needs to read on-screen text)
--fps F — override auto-fps (clamped to 2 fps max)
--out-dir DIR — keep working files somewhere specific (default: an auto-generated tmp dir)
--cookies-from-browser B — read cookies from a local browser (chrome, firefox, safari, edge, brave, …) for login-gated sources
--cookies FILE — path to a Netscape-format cookies.txt (alternative to --cookies-from-browser)
--whisper mlx|openai-whisper — force a specific local Whisper engine (default: prefer mlx-whisper, fall back to openai-whisper)
--no-whisper — disable the local Whisper fallback entirely (frames-only if no captions)
Public videos (most of YouTube, Vimeo, TikTok, Loom, etc.) download with no auth. But some sources gate the download behind a login: Instagram, X/Twitter, age-restricted or private/unlisted YouTube, members-only content. Those need the user's own cookies.
Do NOT pass cookies pre-emptively. Always try the plain download first. Only reach for cookies when it fails with a login / private / 403 / "login required" / "rate-limit" error. The user never types the flag themselves — you add it and re-run. When that happens, walk the user through it (these are sub-steps of the main Step 2, not the main flow):
(a) Ask which browser they're logged into. "To grab this Instagram video I need to borrow the cookies from a browser where you're logged into Instagram. Which one are you logged in on — Chrome, Safari, Firefox, Edge, or Brave?" Supported values: chrome, firefox, safari, edge, brave, chromium, opera, vivaldi.
(b) Re-run with that browser (on Windows use python, not python3 — see Step 0):
(c) Handle the common per-browser snags (tell the user the specific fix, don't just retry):
Chrome on macOS locks its cookie DB while open and its cookies are encrypted. Two things may happen: (1) extraction fails with "could not copy/open the cookie database" → tell the user to fully quit Chrome (Cmd-Q, not just close the window) and retry; (2) a macOS Keychain prompt pops up ("… wants to use your confidential information stored in Chrome Safe Storage") → tell the user to click Always Allow. If Chrome keeps fighting it, suggest they switch to Safari or Firefox.
Chrome on Windows also locks the DB while running → tell the user to fully close it (check the system tray) and retry.
Safari on macOS needs the app running this (the terminal / Claude Code) to have Full Disk Access (System Settings → Privacy & Security → Full Disk Access). If Safari extraction fails, point them there, or fall back to another browser.
Firefox usually works without closing it — good fallback on any OS when Chrome is stubborn.
(d) Manual fallback if browser extraction just won't cooperate (most reliable, works on macOS / Windows / Linux): guide the user to export a cookies.txt and pass it with --cookies:
Install a cookies-export extension — "Get cookies.txt LOCALLY" (open-source, exports Netscape format) for Chrome/Edge, or "cookies.txt" for Firefox.
Open and log into the site (e.g. instagram.com).
Click the extension → Export → save the file (e.g. ~/Downloads/cookies.txt).
Privacy note to reassure the user: cookies are read live from their own machine and piped straight into the yt-dlp subprocess. The skill never copies, stores, logs, or transmits them anywhere. The cookies.txt file (if they used the manual fallback) stays on their disk — they can delete it after.
Focusing on a section (higher frame rate)
When the user asks about a specific moment — "what happens at the 2 minute mark?", "zoom into 0:45 to 1:00", "the first 10 seconds" — pass --start and/or --end. The script switches to focused-mode budgets, which are denser than full-video budgets (still capped at 2 fps):
≤5s → 2 fps (up to 10 frames)
5-15s → 2 fps (up to 30 frames)
15-30s → ~2 fps (up to 60 frames)
30-60s → ~1.3 fps (up to 80 frames)
60-180s → ~0.6 fps (100 frames, capped)
Focused mode is the right call for:
Any moment/range the user names explicitly ("around 2:30", "the intro", "the last 30 seconds").
Any video longer than ~10 minutes where the user's question is about a specific part — running focused on the relevant section is far more useful than a sparse scan of the whole thing.
Re-runs after a full scan didn't have enough detail in some region.
Transcript is auto-filtered to the same range. Frame timestamps are absolute (real video timeline, not offset-from-start).
Examples:
# Last 10 seconds of a 1 minute video
python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py" video.mp4 --start 50 --end 60
# Zoom into 2:15 → 2:45 at 3 fps (90 frames)
python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py""$URL" --start 2:15 --end 2:45 --fps 3
# From 1h12m to the end of the video
python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py""$URL" --start 1:12:00
Step 3 — Read every frame path the script lists. The Read tool renders JPEGs directly as images for you. Read all frames in a single message (parallel tool calls) so you see them together. The frames are in chronological order with a t=MM:SS timestamp so you can align them to the transcript.
Step 4 — answer the user. You now have two streams of evidence:
Frames — what's on screen at each timestamp
Transcript — what's said at each timestamp. The report's header shows the source (captions = yt-dlp pulled native subs; whisper (mlx) or whisper (openai-whisper) = transcribed locally on-device).
If the user asked a specific question, answer it directly citing timestamps. If they didn't ask anything, summarize what happens in the video — structure, key moments, notable visuals, spoken content.
Step 5 — clean up. The script prints a working directory at the end. If the user isn't going to ask follow-ups about this video, delete it with rm -rf <dir>. If they might, leave it in place.
Transcription
The script gets a timestamped transcript in one of two ways:
Native captions (free, preferred). yt-dlp pulls manual or auto-generated subtitles from the source platform if available.
Local Whisper fallback (on-device, no API, no key). If no captions came back (or the source is a local file), the script extracts audio (ffmpeg -vn -ac 1 -ar 16000 -b:a 64k, ~0.5 MB/min) and transcribes it locally:
mlx-whisper — mlx-community/whisper-large-v3-turbo. Preferred on Apple Silicon: fast, runs on the GPU/Neural Engine. Same engine ig-scraper uses. Install: pip3 install mlx-whisper.
openai-whisper — base model on CPU. Cross-platform fallback when mlx isn't available. Install: pip3 install openai-whisper.
The audio never leaves the machine. The script prefers mlx-whisper; override with --whisper openai-whisper. Language is auto-detected. Use --no-whisper to skip the fallback entirely.
Failure modes and handling
Setup preflight failed → run python3 "${CLAUDE_SKILL_DIR}/scripts/setup.py" (auto-installs ffmpeg/yt-dlp via brew on macOS; prints exact commands on Windows/Linux). If it reports no whisper engine, relay the exact pip3 install … command it printed (mlx-whisper only on Apple Silicon, openai-whisper elsewhere).
No transcript available → captions missing AND (no local whisper engine OR --no-whisper set OR transcription failed). Script prints a hint pointing to setup. Proceed frames-only and tell the user.
Long video warning printed → acknowledge it in your answer. Offer to re-run focused on a specific section via --start/--end rather than a sparse full-video scan.
Download fails (login/private/403) → the source needs auth (common on Instagram, X, age-restricted or private videos). Re-run with --cookies-from-browser <browser> using a browser the user is logged into (see "Login-gated sources" above). If it's region-locked or genuinely unavailable, tell the user plainly; do not keep retrying.
Whisper fails → the error is printed to stderr (likely: engine not installed, or a video with no audio track). The report will say "none available" for transcript. The first mlx run also downloads the model (~1.5 GB) once, then caches it.
Token efficiency
This skill burns tokens primarily on frames. Order of magnitude:
80 frames at 512px wide is roughly 50-80k image tokens depending on aspect ratio.
The transcript is cheap (a few thousand tokens at most for a 10-minute video).
Bumping --resolution to 1024 roughly quadruples the image tokens per frame. Only do it when necessary.
If you already watched a video this session and the user asks a follow-up, do not re-run the script — you already have the frames and transcript in context. Just answer from what you have.
Security & Permissions
What this skill does:
Runs yt-dlp locally to download the video and pull native captions when the source supports them (public data; the request goes directly to whatever host the URL points at)
Runs ffmpeg / ffprobe locally to extract frames as JPEGs and, when Whisper is needed, a mono 16 kHz audio clip
Transcribes the audio clip locally on-device with mlx-whisper (or openai-whisper as a CPU fallback) — no network call, no API, no key
Writes the downloaded video, frames, audio, and an intermediate transcript to a working directory under the system temp dir (or --out-dir if specified) so Claude can Read them
On first mlx run, downloads the whisper model (~1.5 GB) from Hugging Face once and caches it under ~/.cache/huggingface
What this skill does NOT do:
Does not upload the video OR the audio to any API — transcription is fully local. The only outbound traffic is yt-dlp fetching the video/captions from the source URL (and the one-time model download)
Does not log into or post to any account. It only reads browser cookies when the user explicitly passes --cookies-from-browser / --cookies for a login-gated source, and only to authenticate the yt-dlp download. Those cookies are read live and never copied, stored, logged, or transmitted by the skill
Does not use, store, or require any API key — there is no .env, no config file, no secrets
Does not persist anything outside the working directory and the Hugging Face model cache — clean up the working directory when you're done (Step 5)
