| name | watch |
| version | 1.3.0rc2 |
| description | Watch any video (URL, stream, or local path) via Watch Skill. Downloads, extracts scene-aware deduped frames, OCRs them, transcribes (captions first, then local Whisper — offline by default), indexes everything, and hands the result to the agent. Follow-up questions are answered from the persistent index without re-processing. |
| argument-hint | <video-url-or-path> [question] |
| allowed-tools | Bash, Read, AskUserQuestion |
| license | MIT |
| user-invocable | true |
/watch (Watch Skill)
You don't have a video input; this skill gives you one. It is a thin wrapper
around the watch-skill CLI — all logic lives in the engine, so this skill
works identically on every harness (Claude Code, Codex, Cursor, ...).
This is a drop-in upgrade of the classic claude-video /watch skill:
same invocation shape, plus a persistent index (ask answers follow-ups
without re-processing), OCR on frames, scene-aware sampling with perceptual
dedup, local Whisper (offline by default, no API key needed), and THE LOOP
(capture -> critique -> fix -> re-capture) for iterating on your own output.
Step 0 — Preflight (first invocation per session)
watch-skill doctor --json
- Exit 0 → proceed silently. Do NOT announce that setup is fine.
- Non-zero → the JSON lists each failing check with a
fix. doctor
auto-bootstraps ffmpeg and yt-dlp into a managed bin dir on Windows/macOS/
Linux; re-run once after it reports fixes. Only involve the user when a
check still fails after remediation.
- If
watch-skill itself is not on PATH: pip install watch-skill (or
uv tool install watch-skill), then re-run the doctor.
No API key is required for acquisition, transcription, OCR, indexing, or
search: transcription falls back to local faster-whisper. Visual synthesis
and verification can use the user's existing Anthropic, OpenAI, Gemini, or
OpenRouter key, or an optional local Ollama model. The agent and provider are
independent; see the configuring-vision skill. Cloud STT is opt-in
(--cloud-stt) and only ever uploads extracted mono audio — the video file
never leaves the machine.
Step 1 — Watch
Parse the user input into source + optional question, then:
watch-skill watch "<source>" [--start T --end T] [--max-frames N] [--transcript-only]
- Any yt-dlp-supported site (1800+), direct media URLs, HLS/DASH manifests
(
--duration 60 bounds live streams), and local files all work.
--start / --end (SS, MM:SS, HH:MM:SS) switch to dense focused
sampling of that window — use for "what happens at 2:30?" questions and for
any video over ~10 minutes when the user cares about one section.--timestamps T1,T2,... pins frames at transcript-flagged moments
("look here", "as you can see") that visual selection may miss.--transcript-only skips frames entirely (fastest; no video download when
captions exist).--max-frames N tightens the token budget (default: duration-tiered,
hard cap 100, max 2 fps).
The report prints an Indexed: video_id ... line, frames with t=MM:SS
timestamps, OCR text, and the transcript.
Step 2 — Read the frames
Read every frame path the report lists, in a single message (parallel Read
calls), so you see them together in chronological order.
Step 3 — Answer
Answer from frames + OCR + transcript, citing timestamps. No question →
summarize structure, key moments, notable visuals, spoken content.
Follow-ups — use the index, not re-processing
The video is already indexed. For any follow-up question in this or a LATER
session:
watch-skill ask <video_id> "<question>"
watch-skill search "<phrase>"
ask (v0.6) answers text-first with timestamped evidence, a confidence
score, and a ~N tokens saved line. It escalates on its own when unsure
(dense re-sampling, zoom-crop re-OCR) and states plainly when the video
does not clearly show the answer — trust that refusal; do NOT invent an
answer past it. Frame paths are listed only when the engine wants you to
look yourself (or pass --frames); Read them then. Never re-run watch
for a follow-up on an already-indexed video.
If the user corrects one of your video answers, report it so the system
learns (locally):
watch-skill lessons add <video_id> "<question>" "<your wrong answer>" "<the correction>"
THE LOOP — iterate on your own output
When the user asks you to fix UI/visual output and verify the fix:
watch-skill loop start "<url-or-screen:-or-file>" "<pass criteria>" [--script '<json steps>']
watch-skill loop iterate <loop_id>
The critique returns structured issues with timestamps and suggested fixes.
YOU change the code; the loop only observes. On pass it renders a
before/after MP4+GIF proof. watch-skill capture "<target>" records without
critiquing.
Security posture
- The video file itself NEVER leaves the machine. Only extracted mono-16kHz
audio may go to a cloud STT API, and only with explicit
--cloud-stt.
- No cookies, no logins — only public data is requested.
- API keys live in env vars /
.env; they are never logged or echoed.
- Downloads are cached under
~/.watch-skill/cache (LRU, size-capped).
