Watch

Community

oxbshw

watch

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.

Overview

Publisher	oxbshw
Repository	`watch-skill`
Skill name	`watch`
Stars	518
Forks	72
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by oxbshw on GitHub. Read the source before you install it.

Installation

Install the Watch AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/oxbshw/watch-skill/tree/main/skills/watch
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/oxbshw/watch-skill.git /tmp/watch-skill
mkdir -p .claude/skills
cp -r /tmp/watch-skill/skills/watch .claude/skills/watch

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Watch in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Watch on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Watch is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/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).

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:

bash
watch-skill ask <video_id> "<question>"      # self-healing answer + evidence
watch-skill search "<phrase>"                 # across every video ever watched

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):

bash
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:

bash
watch-skill loop start "<url-or-screen:-or-file>" "<pass criteria>" [--script '<json steps>']
# ... you apply the suggested fixes ...
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).

Frequently asked questions

What does the Watch AI skill do?

Why use Watch on TypingMind?

Because you install it once and use it with any model. Watch is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Watch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oxbshw/watch-skill/tree/main/skills/watch. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Watch?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Watch?

As many as you like. As long as a model supports skills, you can use Watch with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Watch AI skill free?

Yes. It is published on GitHub by oxbshw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

/watch (Watch Skill)

Step 0 — Preflight (first invocation per session)

Step 1 — Watch

Step 2 — Read the frames

Step 3 — Answer

Follow-ups — use the index, not re-processing

THE LOOP — iterate on your own output

Security posture

Frequently asked questions

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