Watch Skill

Name: Watch Skill
Author: oxbshw

by oxbshwversion 1.4.3i★ –iRuns on your computer

Registry nameio.github.oxbshw/watch-skill

Install

Overview

Description from the maker (original English)

Watch video and live sessions, keep timestamped evidence, and verify an agent's own work.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Design, images & audio

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "watch-skill": {
      "command": "uvx",
      "args": [
        "watch-skill",
        "serve"
      ]
    }
  }
}

Run this in your terminal

claude mcp add watch-skill -- uvx watch-skill serve

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "watch-skill": {
      "command": "uvx",
      "args": [
        "watch-skill",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "watch-skill": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "watch-skill",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account oxbshw, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Design, images & audioi

More stars than 0% of the other 2202 servers in this category
Published to the registry more recently than 30% of the other 2202 servers in this category
Basic checks equal to or better than 100% of the other 2202 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Watch Skill safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.4.3) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Figma Context MCP

by GLips

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

Runs on your computer★ 16,165MITUpdated 5 months ago

Install

Claude Real Video

by HUANGCHIHHUNGLeo

Let any LLM watch a video locally — and search everything it has ever watched.

Runs on your computer★ 2,284MITUpdated 2 months ago

Install

Figma MCP Server

by figma

Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.

Runs in the cloud★ 2,152Updated 13 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Watch Skill · DeepWatch

Give AI agents eyes, ears, and verifiable results.

Watch Skill turns video, audio and screen activity into searchable, timestamped evidence, and answers *did that actually work?* with a deterministic contract rather than a model's opinion. Add it to the agent you already use over MCP.

DeepWatch is a ready-made agent workspace, built on the official DeepSeek Harness with Watch Skill already composed in, where a tool call leaves a receipt you can open and a result can be checked by something other than the agent that produced it.

Python · PyPI

Node · npm

Gates and directories

Install · Use it · THE LOOP · Packages · Architecture · Docs · Community

Two capabilities, and they work apart

Perception. Video, audio and screen activity become frames, transcripts and OCR text, each carrying an absolute timestamp. Index a source once and query it for as long as you keep it; every answer cites a moment you can open.

Verification. A frozen contract — file digests, JSON values, SQL results, HTTP responses, DOM state — is evaluated by a separate process. The verdict is VERIFIED, FAILED, UNVERIFIED or INCONCLUSIVE, and it does not come from a language model.

Either is useful on its own, and the split is deliberate.

Watch Skill — the engine

Index a recording once and ask it questions for as long as you keep it. Answers cite timestamps you can open. Verification contracts check file digests, JSON values, SQL results, HTTP responses and DOM state, and report *passed*, *failed*, *unverified* or *inconclusive* — four answers, because three of them are not the same as "no".

Any agent can use it: MCP, a CLI, or a REST API.

DeepWatch — the workspace

Built on the official DeepSeek Harness, with Watch Skill composed in, installed by one command. You get an agent that can see and prove, without wiring anything together yourself.

Every tool call leaves a receipt naming what it touched. Every path a tool declares is checked against one workspace boundary, so a tool cannot quietly write outside it. Results carry a Core verdict you can open, and the Library keeps them after a restart.

Runs in your browser. Compare puts two runs of the same contract side by side and shows where their verdicts diverged.

Watch Skill sees and proves. DeepWatch is the workspace it comes built into.

Start here

Three entry paths. Pick the row that describes you.

You have	You want	Go to
An agent already (Claude Code, Cursor, Codex, any MCP client)	Give it eyes, ears and verification	Watch Skill
Nothing yet	The whole workspace, agent included	DeepWatch
A DeepSeek Harness you already run	Add Watch to it, keep your setup	`@deepwatch/dsh-bundle`

1. Add Watch Skill to an agent you already use

pip install 'watch-skill[standard]'   # frames, retrieval and the MCP server

watch-skill doctor # checks, and repairs what it can watch-skill watch watch-skill ask "what changed at 3:12?"

Take the extra seriously. A bare pip install watch-skill gives you the CLI, the verifier and the Bridge, and it cannot extract a frame: watch stops at perceive.missing_dependency on the first video. [standard] is frames, retrieval and MCP; add [ocr] to read on-screen text, [whisper] for local transcription when a source has no captions, [loop] for the browser, or take [all]. watch-skill doctor names the exact command for whatever is missing.

Wire it into any MCP client — [standard] includes the server:

watch-skill serve              # stdio MCP server, 39 tools

Or install the skills into 25+ agents at once:

npx skills add oxbshw/watch-skill -g

[Every supported client, and how far each is verified →](docs/agents/README.md)

2. The whole workspace

Prerequisites. Node ^22.19 || >=24, which is what the CLI's engines declares. Python 3.11, 3.12 or 3.13 — the versions CI runs and the classifiers list — and only if you want the perception and verification engine. DeepWatch starts without it and reports every Watch capability as unavailable until it is there.

# 1. the engine that sees and proves (optional, but it is the point)

pip install 'watch-skill[standard,ocr]'

2. the workspace

npm install -g @deepwatch/cli deepwatch doctor # what is present, what is missing, how to fix it deepwatch setup # builds the runtime; shows the download and asks first

3. a workspace directory to work in

mkdir my-project deepwatch web --workspace ./my-project

deepwatch web prints a local URL and opens the workspace there. This is the first thing you see:

Two numbers rather than one fraction, because "installed" and "proved" are different facts and a single percentage would blur them. Ready now counts what has passed a runtime gate. Needs setup counts what has not been configured or not been tested — saved is never presented as tested. You can open the workspace without a provider: the Library, the index and the Watch tools all work locally.

Without a global install, the same package through npx:

npx --yes @deepwatch/cli setup

mkdir my-project npx --yes @deepwatch/cli web --workspace ./my-project

npx is a way of running @deepwatch/cli, not a different package — there is no unscoped deepwatch on npm.

What `setup` downloads. The pinned DeepSeek Harness, its exact required peers, and the DeepWatch packages at this release's version, into a runtime under your DeepWatch home. It prints the registry, the versions and the destination and stops for your agreement; --yes agrees in advance and --offline refuses outright. Nothing is installed globally except the CLI you installed yourself. --artifacts takes the *DeepWatch* packages from verified local tarballs this product hashes itself instead of from the registry, which is what a checkout build wants. It does not make the install offline: the pinned Harness and its generated peer closure are fetched from npm in that mode too, and the plan setup prints says so before it fetches anything.

A model provider is not required to start. The workspace boots, the Library works and Watch tools answer without one. You need a provider for the *agent* — chat, tool use, and the critique step of THE LOOP.

Connect a model, and prove the connection

Four steps in the workspace itself, in this order. The last one is the point.

In the app	What it does
Settings → Models → Add provider	Names a provider and takes a key, or leaves the field blank and reads one from the launch environment.
Settings → Role Bindings → Choose a model	Binds a specific provider and model to a role — Chat, or Visual perception.
Run provider test	Sends one real request to that exact binding and reports what came back.
Ready	Only now will the workspace send anything to it.

<img src="workspace/docs/screenshots/release/02-provider-ready.png" width="88%" alt="DeepWatch Settings on Role Bindings. Chat is marked Ready, bound to OpenRouter and deepseek/deepseek-v4-pro, with the actions Change model, Ru

Only the beginning is shown; read the rest in the repo

Watch Skill

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

Watch Skill · DeepWatch

Two capabilities, and they work apart

Watch Skill — the engine

DeepWatch — the workspace

Start here

1. Add Watch Skill to an agent you already use

2. The whole workspace

2. the workspace

3. a workspace directory to work in

Connect a model, and prove the connection