# watch

watch examines linked or local videos, extracts frames and speech, then answers questions from an index. Use it to review a recording or check your own interface.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 583 installs · ★ 528
- Author: oxbshw
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/oxbshw/watch-skill/tree/HEAD/skills/watch/SKILL.md
- Updated: 2026-10-14
- HTML: https://affpapa.org/en/nevibecode/skills/oxbshw-watch

## What it does

The skill hands video to the `watch-skill` engine, which samples frames around scene changes, reads visible text, and transcribes speech. It checks for captions first and falls back to local Whisper. Results stay indexed, so follow-up questions can use the existing analysis instead of processing the video again. You can focus on a time range or request transcript-only processing.

For interface checks, the loop returns timestamped issues against your criteria; the agent makes the code changes. In Claude Code, install with `npx skills add oxbshw/watch-skill --skill watch -a claude-code`; the skill file is `~/.claude/skills/watch/SKILL.md`. If `watch-skill` is missing from PATH, install it with `pip install watch-skill` or `uv tool install watch-skill`. The CLI must be available for video processing to run. Transcription needs no API key, while cloud speech processing requires the explicit `--cloud-stt` flag.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add oxbshw/watch-skill --skill watch -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/watch/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/watch/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add oxbshw/watch-skill --skill watch -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/watch/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add oxbshw/watch-skill --skill watch -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/watch/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install oxbshw/watch-skill watch
```

2. **Manually: put the files in place**
- File: `.github/skills/watch/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add oxbshw/watch-skill --skill watch -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/oxbshw/watch-skill --path skills/watch
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/watch/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add oxbshw/watch-skill --skill watch -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/oxbshw/watch-skill/tree/HEAD/skills/watch/SKILL.md

## When to use it
- review a video from a link or local file
- locate a moment by timestamp
- read on-screen text and transcribe speech
- ask follow-up questions about an indexed video
- check an interface against stated criteria

## Example prompts
- Review this video and summarize its key moments with timestamps.
- What happens at 2:30 in this recording?
- Check this interface against the criteria and suggest fixes.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 3a49b35beb8484cc.

BEGIN THIRD-PARTY FILE 3a49b35beb8484cc
````text
---
name: watch
version: "1.4.3"
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)

```bash
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:

```bash
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).
````
END THIRD-PARTY FILE 3a49b35beb8484cc

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