/watch

name: watch
description: Watch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or local WhisperX / cloud Whisper fallback), and hands the result to the agent so it can answer questions about what's in the video. With a Gemini API key, Google's agentic video model watches the full video instead.
license: MIT
allowed-tools: Bash, Read, AskUserQuestion
metadata:
  version: "0.3.2"

/watch

Run the bundled Python script. With the **gemini** engine (a `GEMINI_API_KEY` is configured) Google's video model watches the video and the report carries its timestamped answer for you to relay. With the **local** engine the script produces timestamped frames and a transcript; view the frames and answer from that evidence. Native captions come first; the chosen local or cloud backend is only a fallback. Transcript-only evidence cannot establish visual facts.

Resolve the skill and interpreter

`SKILL_DIR` is the absolute directory containing this SKILL.md; `scripts/` sits beside it.

Commands below use `python3` for macOS/Linux. On Windows, verify a working Python 3.10+ with `python --version` or `py -3 --version` and use that interpreter. `python3` is not always a Store alias; inspect the actual result. In PowerShell use `$SKILL_DIR` rather than Bash variable syntax, for example:

python "$SKILL_DIR/scripts/setup.py" --json

Use the host's available shell, image viewer, and question tool. `AskUserQuestion` and Bash are examples, not requirements for every host.

First run and setup

On the first invocation in a session:

python3 "${SKILL_DIR}/scripts/setup.py" --json

• `can_proceed` depends on base binaries for the local engine, not optional credentials. If it is false, run `setup.py` and confirm the binaries become available. macOS uses Homebrew; other systems get package commands. Do not use sudo automatically. With the Gemini engine (`engine` is `gemini`, `binaries_required` false), missing `ffmpeg`/`yt-dlp` do not block YouTube URLs but are still needed for other URLs and for `--engine local`; mention `missing_binaries` only when that matters.
• If `first_run` is false, proceed without announcing successful setup or asking preferences again. Existing installations without a backend setting retain `auto` (Groq key first, then OpenAI).
• If `first_run` is true, ask the engine question first, then (local engine only) the two choices below it. The wizard does not inspect RAM, disk, CPU, browser sessions, or other machine state; the user decides from the stated requirements.

**Question 1 — "How should watch view videos?"**

• `gemini` (recommended) — Google's Gemini model watches the whole video, including audio, and answers directly. Needs a free key from https://aistudio.google.com/apikey. YouTube URLs are sent to Google; local or downloaded videos are uploaded to Google, then deleted after the answer.
• `local` — frames + transcript extracted on this machine and read by you. No key needed.

If `gemini`, run:

python3 "${SKILL_DIR}/scripts/setup.py" --engine gemini

Exit 3 means the key is missing; the command has created `~/.config/watch/.env` for it. Point the user to https://aistudio.google.com/apikey for a free key and let them choose how to add it:

• **Paste it in chat.** Write it on the `GEMINI_API_KEY=` line of the config file (add the line if it is missing) with a file-editing tool, preserving the other lines.
• **Add it themselves.** Offer to open the config file in their text editor (`open -t` on macOS, `notepad` on Windows, `xdg-open` on Linux) so they can paste the key after `GEMINI_API_KEY=` and save. This only works when you run on the user's own computer; otherwise give them the file path.

Never print the key or put it in a shell command. Once it is saved, rerun `setup.py --engine gemini`. On exit 0 setup is complete — **skip the detail and transcription questions**; they only apply to the local engine. If `local`: run `setup.py --engine local` (this also installs missing base binaries and scaffolds the private config) and continue with the two questions below. An existing explicit `WATCH_ENGINE` is not asked again.

Ask for the default detail, lightest to heaviest:

1. `transcript`: no frames; skip media download when captions are available. 2. `efficient`: fast keyframe selection, cap 50. 3. `balanced` (recommended): scene-aware frames, cap 100. 4. `token-burner`: scene-aware, uncapped; high image cost.

If the detail question is skipped, retain `balanced`.

Then ask: **For videos without captions, how should watch transcribe?** Present:

1. `whisperx` (recommended): local transcription, no API key; audio stays on the machine. One-time download of about 1.5 GB. Requires 3 GB free disk, 8 GB RAM, and a compatible 64-bit CPU. Apple Silicon macOS is verified; Linux/Windows recipes and Intel macOS are untested. 2. `groq`: fast cloud transcription, needs a Groq key. 3. `openai`: cloud transcription, needs an OpenAI key. 4. `none`: captions only; no speech fallback.

Use an existing explicit backend choice without asking again. Do not treat silence as permission to install local models or upload audio; the no-key/no-install path remains available.

Complete the selected setup using the user's detail value:

python3 "${SKILL_DIR}/scripts/setup.py" --backend whisperx --detail balanced
# Or: --backend groq / openai / none

For WhisperX, relay progress while the installer provisions uv, Python 3.12, pinned dependencies, and both model caches. `setup.py --install-whisperx` reruns this managed installer if needed. It writes the backend, executable, model, and completion marker only after warm-up succeeds. A failed local installation never selects a cloud backend automatically.

For cloud, get the matching key the same way as the Gemini key (pasted in chat, or the user adds it after offering to open the config file), then rerun `setup.py --backen