Watch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), and hands the result to Claude so it can answer questions about what's in the video.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Watch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), 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, AskUserQuestion
homepage
https://github.com/bradautomates/claude-video
repository
https://github.com/bradautomates/claude-video
author
bradautomates
license
MIT
user-invocable
true
/watch
You don't have a video input; this skill gives you one. A Python script gets captions first, optionally downloads the video, extracts frames as JPEGs (scene-aware, or fast keyframes at efficient detail), 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.
Resolve SKILL_DIR (do this before any command)
Every python3 ... command below runs a bundled script under SKILL_DIR/scripts/. Set SKILL_DIR to the absolute path of the directory containing THIS SKILL.md you just Read — your harness told you that path in the Read result. The scripts are always a direct sibling of this file (SKILL_DIR/scripts/watch.py), in every install layout:
Substitute that literal path for ${SKILL_DIR} in every command. This works on every harness (Claude Code, Codex, Cursor, Gemini CLI, …) without relying on any harness-specific environment variable. Guard once at the start of a run:
SKILL_DIR="<absolute path of the directory containing the SKILL.md you Read>"if [ ! -f "$SKILL_DIR/scripts/watch.py" ]; thenecho"ERROR: scripts/watch.py not found under SKILL_DIR=$SKILL_DIR" >&2
echo"Re-check the directory of the SKILL.md you Read and substitute it as SKILL_DIR." >&2
exit 1
fi
Step 0 — Setup preflight (runs every /watch invocation, silent on success)
Python interpreter: every python3 ... command in this skill is for macOS/Linux. On Windows, substitute python — the python3 command on Windows is the Microsoft Store stub and will not run the script.
On the first /watch invocation in a session, use structured preflight so you can detect first-run setup:
python3 "${SKILL_DIR}/scripts/setup.py" --json
Branch on two fields:
can_proceed: true and first_run: false → setup is already done (the user may have deliberately skipped a Whisper key — that's allowed). Proceed to Step 1 without comment.
first_run: true → genuine first-time setup. Do these in order:
If missing_binaries is non-empty, run the installer first (it auto-installs on macOS / prints commands elsewhere — see below) and confirm the binaries land. Do not skip this and jump to preferences.
Run the installer once more if needed so it scaffolds ~/.config/watch/.env (it only writes the template when the file is absent, so let it create the file before you write any values into it).
Encourage a Whisper API key and ask the watch-preference questions below, then write the selected values into ~/.config/watch/.env and set SETUP_COMPLETE=true.
can_proceed: false and first_run: false → setup was finished before but the environment regressed (e.g. missing_binaries after an OS change). Run the installer to remediate, then proceed. Don't re-ask preferences.
A missing Whisper key is encouraged to fix, not required: on a genuine first run status will read needs_key even when binaries are present — that's your cue to encourage a key, not a blocker.
On follow-up /watch calls in the same session, use the silent check:
python3 "${SKILL_DIR}/scripts/setup.py" --check
This is a <100ms lookup. Exit 0 means /watch can run — this includes a user who finished setup without a Whisper key (keyless is allowed). On exit 0 the script emits nothing — proceed to Step 1 without comment. Do NOT announce "setup is complete" to the user — they don't need a status message on every turn. The only acceptable user-visible output from Step 0 is when remediation is required.
On non-zero exit, follow the table:
Exit
Meaning
Action
2
Missing binaries (ffmpeg / ffprobe / yt-dlp)
Run installer
3
Genuine first run with no Whisper API key
Run installer to scaffold .env, then encourage a key (the user may decline — proceed with --no-whisper)
4
Both missing
Run installer, then encourage a key
Exit 3 only fires before the user has completed setup. Once SETUP_COMPLETE=true is written, a keyless install returns exit 0 and is never nagged again.
The installer is idempotent — safe to re-run:
python3 "${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. It scaffolds ~/.config/watch/.env with commented placeholders and default watch settings at 0600 perms.
If an API key is still missing after install: use AskUserQuestion to ask the user whether they have a Groq API key (preferred — cheaper, faster) or an OpenAI key. Then write it into ~/.config/watch/.env — set the matching GROQ_API_KEY=... or OPENAI_API_KEY=... line. If they don't want to set up Whisper, proceed with --no-whisper and tell them videos without native captions will come back frames-only.
First-run watch preference: after the installer has scaffolded ~/.config/watch/.env, use AskUserQuestion to ask one question:
Default detail (one dial). Present these as AskUserQuestion options in this exact order — lightest to heaviest — and keep (recommended) on balanced even though it is not first (do not reorder to put the recommended option first):
transcript — no frames at all, transcript only (skips video download when captions exist).
token-burner — scene-aware, uncapped (maximum fidelity; high token cost).
Write the answer directly into ~/.config/watch/.env by setting the bare key on its own line — no trailing inline comment (a # note after the value can break parsing):
WATCH_DETAIL=balanced
Use the user's selected value. If they skip the question, keep the recommended default. Once dependencies, the API-key choice, and this preference are handled, write or update SETUP_COMPLETE=true in the same file. Do not ask this preference question again when SETUP_COMPLETE=true.
Structured mode (optional):python3 "${SKILL_DIR}/scripts/setup.py" --json emits {status, can_proceed, first_run, setup_complete, missing_binaries, whisper_backend, has_api_key, config_file, watch_detail, platform} where status is one of ready | needs_install | needs_key | needs_install_and_key. status describes the ideal state (a key is encouraged, so a keyless first run reads needs_key); can_proceed is the operational gate (binaries present AND a key is set OR setup was already completed). Branch on can_proceed/first_run to decide whether to run; use status to decide what to encourage.
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.
Universal rate cap: 2 fps. The script never samples faster than 2 fps, even when a budget or --fps would imply more.
The frame ceiling is set by the detail mode (WATCH_DETAIL in ~/.config/watch/.env, or --detail), not a single global cap:
transcript → no frames
efficient → up to 50 (keyframes)
balanced (default) → up to 100 (scene-aware)
token-burner → uncapped (scene-aware; a soft warning prints past 250 frames)
--max-frames N overrides whichever cap the mode would otherwise use.
Full-video frame budget by duration. Token cost grows with frame count, so the script targets a budget by duration. This budget sets the fps and the uniform-sampling fallback; scene-aware selection can fill up to the detail cap above, whichever is lower:
≤30s → ~12-30 frames
30s-1min → ~40 frames
1-3min → ~60 frames
3-10min → ~80 frames
>10min → up to the detail cap, 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 "${SKILL_DIR}/scripts/watch.py""<source>"
Optional flags:
--detail transcript|efficient|balanced|token-burner — fidelity/speed dial. transcript = no frames (transcript only, skips video download when captions exist); efficient = fast keyframes (cap 50); balanced = scene-aware frames (cap 100); token-burner = scene-aware, uncapped.
--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).
--timestamps T1,T2,… — grab a frame at each of these absolute timestamps (SS, MM:SS, or HH:MM:SS). Use this after reading the transcript to capture deictic moments the presenter flags ("look here", "as you can see", "notice this") that visual selection alone may miss. See "Transcript-cue frames" below.
--max-frames N — override the preset 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)
--no-dedup — keep near-duplicate frames. By default a frame-delta pass drops frames that are visually near-identical to the previous kept one (held slides, static screen recordings, paused video) so the frame budget goes to distinct content; the report's Frames line notes how many were dropped. Pass this only if the user needs every sampled frame (e.g. judging subtle frame-to-frame motion).
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, and still bounded by the detail-mode cap — the counts below assume the default balanced cap of 100; efficient tops out at 50):
≤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 "${SKILL_DIR}/scripts/watch.py" video.mp4 --start 50 --end 60
# Zoom into 2:15 → 2:45 at 2 fps (60 frames)
python3 "${SKILL_DIR}/scripts/watch.py""$URL" --start 2:15 --end 2:45 --fps 2
# From 1h12m to the end of the video
python3 "${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.
This holds for transcript detail too: even with no frames, produce a summary like the other modes — do not paste the full transcript into chat. Synthesize structure, key moments, and spoken content with timestamps; quote only the lines that matter. Offer the raw transcript only if the user explicitly asks for it.
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.
At transcript detail, captions are enough to return a report without downloading video. If captions are missing, the script downloads audio only and tries Whisper. If no transcript can be produced, it reports the limitation clearly; re-run with --detail balanced for frames.
At efficient detail, the script downloads the video and extracts keyframes only (ffmpeg -skip_frame nokey) — a near-instant pass that lands frames on scene cuts. If a clip has fewer than 4 keyframes it falls back to uniform sampling.
At balanced / token-burner detail, the script extracts scene-aware frames: ffmpeg scene-change selection first, falling back to uniform sampling only when the video is effectively static. balanced caps at 100 frames; token-burner is uncapped. Frame report lines include both timestamp and selection reason. Extracted images are clamped to a maximum 1998px height for Claude Read compatibility.
Transcript-cue frames
Visual frame selection (scene/keyframe) can miss the moments a presenter explicitly flags — "look here", "as you can see", "notice this", "watch what happens" — because pointing at a slide is often a low visual change. --timestamps lets you force a frame at those exact moments. You decide which moments matter, by reading the transcript:
Run once at --detail transcript (or any detail) to get the timestamped transcript.
Scan it for deictic cues — phrases where the speaker directs attention to something on screen. This is a judgment call (ignore rhetorical "look, the point is…"); that's why it's done by you, not a regex.
Re-run with --timestamps 4:32,7:10,9:55 (absolute source times). For a URL, point the second run at the downloaded local file in the work dir so it doesn't re-download.
Behavior:
Additive by default. Cue frames (reason=transcript-cue) are merged into whatever --detail already selected, in chronological order.
Pinned and counted first. Cue frames are reserved against the frame cap before the detail engine runs, so they're never evicted by even-sampling.
Honors focus mode. With --start/--end, any cue timestamp outside the window is dropped (reported in the summary). Coordinates are always absolute source time.
Cue-only frames.--detail transcript --timestamps … skips scene/keyframe sampling and returns only the cue frames (it will download the video to do so, since frames need pixels).
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.
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 "${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 or rate limit). Audio over the API's 25 MB upload cap is split into chunks and transcribed automatically, so length alone won't fail it; if some chunks fail the transcript is partial and the dropped chunks are noted on stderr. The report will say "none available" only if every chunk fails. 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) — yt-dlp only ever requests public data
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)
