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 Whisper API as fallback), 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 and an API key are in place:

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

This is a [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" ""

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)
• --whisper groq|openai: force a specific Whisper backend (default: prefer Groq if both keys exist)
• --no-whisper: disable the Whisper fallback entirely (frames-only if no captions)

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 (groq) or whisper (openai) = transcribed by API).

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 . 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. Whisper API fallback. 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 uploads it to whichever Whisper API has a key configured:

• Groq: whisper-large-v3. Preferred default: cheaper, faster. Get a key at console.groq.com/keys.
• OpenAI: whisper-1. Fallback. Get a key at platform.openai.com/api-keys.

Both keys live in ~/.config/watch/.env. The script prefers Groq when both are set; override with --whisper openai to force OpenAI. 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, scaffolds the .env). For API key, ask the user via AskUserQuestion and write it to ~/.config/watch/.env.
• No transcript available → captions missing AND (no Whisper key OR Whisper API 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 → yt-dlp's error goes to stderr. If it's a login-required or region-locked video, tell the user plainly; do not keep retrying.
• Whisper request fails → the error is printed to stderr (likely: invalid key, rate limit, or 25 MB upload limit on a very long video). The report will say "none available" for transcript. You can retry with --whisper openai if Groq failed (or vice versa).

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
• Sends the extracted audio clip to Groq's Whisper API (api.groq.com/openai/v1/audio/transcriptions) when GROQ_API_KEY is set (preferred: cheaper, faster)
• Sends the extracted audio clip to OpenAI's audio transcription API (api.openai.com/v1/audio/transcriptions) when OPENAI_API_KEY is set and Groq is not, or when --whisper openai is forced
• 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
• Reads / creates ~/.config/watch/.env (mode 0600) to store the Whisper API key(s) and a SETUP_COMPLETE marker. As a fallback, also reads .env in the current working directory

What this skill does NOT do:

• Does not upload the video itself to any API. Only the extracted audio goes out, and only when native captions are missing AND Whisper is not disabled with --no-whisper
• Does not access any platform account (no login, no session cookies, no posting)
• Does not share API keys between providers (Groq key only goes to api.groq.com, OpenAI key only goes to api.openai.com)
• Does not log, cache, or write API keys to stdout, stderr, or output files
• Does not persist anything outside the working directory and ~/.config/watch/.env. 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 selection + Whisper orchestration), scripts/whisper.py (Groq / OpenAI clients), scripts/setup.py (preflight + installer)

Review scripts before first use to verify behavior.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: NulightJens
• Source: NulightJens/jensai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.