Watch a video

Read a video through its captions, or a local transcript of its audio, on disk, not through the context window. Produce an answer, summary or extraction with [mm:ss] stamps from a transcript the model never pasted whole. Avoid fetching audio unless a video has no captions, printing a transcript into the session, and installing anything by hand.

When to use

• A video link or YouTube id comes with a question, a summary request or an extraction request.
• Several videos to compare or merge.
• Not for a web page: the web tools own it.

A profile, channel or playlist url

Given a TikTok, YouTube, Instagram or X profile, a YouTube channel or a playlist url, never refuse it as "no listing mode".

1. Run node "${CLAUDE_SKILL_DIR}/scripts/list.mjs" --url <url> [--limit <n>]. It prints one line per video (url, title, date, duration, views, each when known) and a summary line, downloads nothing, and lists 20 by default.
2. Pick the videos the question needs from the titles and dates, or the newest few when it names none.
3. Run the loop below once for all picked videos in one batch call: --from with the list.mjs output saved to a file, or --from - on stdin, or many --url.
4. On exit 1, missing tool follows the loop's missing tool rule; any other line says why nothing was listed, so relay it.

The loop

1. Run node "${CLAUDE_SKILL_DIR}/scripts/transcript.mjs" --url <url> [--url <url> ...] with every video in this one call, or --from <file> or --from - for the list.mjs output. It prints one line per video and writes the transcript under .exo/watch/<id>/.
   • Pass all videos in one batch call, which runs them one by one and skips the finished ones (--force redoes them); do not spread watch over parallel agents or parallel Bash calls.
   • For a list too long for one call, run it through lib/map.mjs mapItems with a spend cap.
   • Add --lang <code> only when the user names a language, because it picks a listed track, not a machine translation, and names the spoken language on the speech route.
   • Add --model <ggml file> only when the user names a whisper model.
2. The script fetches yt-dlp, and for a video with no captions whisper.node, a ggml model and a decoder, itself. When a line says missing tool for yt-dlp after a failed fetch, relay its install command and ask the user before installing anything; relay any other failure line as it is.
3. Exit 1 means every video failed; a partly failed batch exits 0 and ends batch done=N skipped=N failed=N | <failure> (<ids>).
   • When a line says no transcript, tell the user that video has no transcript and why, as the line says; offer no other route for it.
   • When a line lists available:, ask which language and rerun with --lang.
   • When the line ends rate-limited, retry later, wait a few minutes and rerun only that url; private/removed needs no rerun.
4. When the line shows frames=<dir> (6 files), read those images, or pass the folder to the lookup delegates, for what is on screen.
5. When the line shows tokens under 10000 and one video, read transcript yourself and answer.
6. Else give each video, or each file under its chunks= folder, to one general-purpose delegate whose prompt opens with exo-kind: lookup.
   • Start them all in one message, at most 8 per message; more files → the next message after the first returns.
   • Brief after the label: the user's question, Reads: <file path> and Worktree: <abs path> on lines of their own, and "return at most 15 lines of notes, each opening with its [mm:ss] stamp, plus a one-line gist".
   • Merge the notes yourself; read no raw transcript.
7. Cite every claim with its stamp, copied whole from the file: on YouTube and Vimeo a stamp is a link, [mm:ss](url), that opens at that moment, so keep the link and the notes' stamps intact. A stamp with no link (TikTok, X, a local file) stays as [mm:ss] or [h:mm:ss] with the video id.

Speakers

• A line Name (guessed): appears only when the captions carry speaker turns (WebVTT <v Name> tags or [SPEAKER_00]: prefixes). Quote it as a guess, not a fact.
• A transcript with no such lines has no speaker data: say so, and infer no speaker from the words.
• Diarization is opt-in. When the user asks who said what, tell them it needs a separate speaker model download (such as whisperx --diarize) and run it only after they agree; the skill downloads none by default.

Judgment

• Say when the source was auto or speech:: names and numbers may be misheard.
• What is on screen (slides, code, diagrams) comes only from frames= images; without them say it is not in the transcript and answer from the words.