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 — the official DeepSeek Harness with Watch Skill already composed in — where every tool call leaves a receipt you can open and every result can be checked by something other than the agent that produced it.

Install · THE LOOP · Workspace · 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 sees and proves. DeepWatch is the workspace it comes built into.

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 <video-url-or-file>
watch-skill ask <id> "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

2. The whole workspace

Prerequisites. Node 22.19+ or 24+. Python 3.11+ 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.

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 <dir> installs from verified local tarballs instead, for an air-gapped machine or a checkout build.

A model provider is not required to start. The workspace boots, the Library works and Watch tools answer without one. Configure a provider in Settings when you want the agent to reason; deepwatch doctor reports what is configured.

3. Into a DeepSeek Harness you already run

dsh plugin --profile web add @deepwatch/dsh-bundle

Compatible Harness. This release was measured against @deepseek-ai/dsh@0.1.1-rc.2, exactly — it is a pinned peer, not a range, so a profile on a different Harness is a combination nobody tested. dsh --version tells you which you have.