Watch Skill

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

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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.

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Start here

Three entry paths. Pick the row that describes you.

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.

<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