Watch

/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:

python3 "${CLAUDE_SKILL_DIR}/scripts/setup.py" --check

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:

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 (mlx-whisper preferred on Apple Silicon, openai-whisper as a CPU fallback) and prints the pip3 install command if neither is present. 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):
  • ≤30s → ~1-2 fps (up to 30 frames)
  • 30s-1min → ~40 frames
  • 1-3min → ~60 frames
  • 3-10min → ~80 frames
  • >10min → 100 frames, sparsely spaced (warning printed)
• 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:

python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py" "<source>"

Optional flags:

• --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)

Login-gated sources (Instagram, X, private/age-restricted videos)

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):

python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py" "https://www.instagram.com/reel/XXXX/" --cookies-from-browser chrome

(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:

1. Install a cookies-export extension — "Get cookies.txt LOCALLY" (open-source, exports Netscape format) for Chrome/Edge, or "cookies.txt" for Firefox.
2. Open and log into the site (e.g. instagram.com).
3. Click the extension → Export → save the file (e.g. ~/Downloads/cookies.txt).
4. Re-run: python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py" "<url>" --cookies ~/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:

1. Native captions (free, preferred). yt-dlp pulls manual or auto-generated subtitles from the source platform if available.
2. 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)

Bundled scripts: scripts/watch.py (entry point), scripts/download.py (yt-dlp wrapper), scripts/frames.py (ffmpeg frame extraction), scripts/transcribe.py (caption parsing), scripts/whisper.py (local mlx/openai-whisper transcription), scripts/setup.py (preflight + installer)

Review scripts before first use to verify behavior.