Watch Skill / DeepWatch

Why it's ranked here

The reason to trust this over a typical video-to-agent script is the second opinion: a check that runs outside the model and reports a plain verdict instead of an impression. Most competitors stop at handing frames to a model and hoping. This pairs that with local transcription and OCR that need no API key, a confidence score computed from actual retrieval signals, and a refusal to answer once that score drops too low. It is also one of very few tools in its category with a persistent index, so a second question about the same recording is a lookup, not a redo.

What's good

Two things stand out. The confidence score behind every answer is built from measurable retrieval signals, such as how much stronger the best match is than the runner-up, rather than one opaque number, and the system says plainly when it does not know instead of inventing an answer. Expensive steps run last: the pipeline exhausts free, local techniques, such as re-checking a frame region at higher resolution, before spending a paid model call, and whatever those local passes discover is saved so later questions benefit. A workspace mode separates an agent deciding to act from that action being allowed to happen.

Tradeoffs

The cost of that thoroughness is weight: the full install pulls in scene detection, OCR, local speech-to-text and a browser, and adds up to several hundred megabytes, well beyond what a single-purpose transcription script needs. A bare, no-extras install cannot even read a video until the right optional pieces are added, which the tool at least says clearly when it happens. Visual question answering and the automated critique loop still need a paid model or a locally hosted one, so a machine with no GPU and no cloud key gets transcripts and OCR but not judgment. The project is also young, with one person currently maintaining it.

How to use it well

Reach for this when an agent needs to answer more than one question about the same recording, or when a fix has to be proven rather than just claimed. Point it at a build or a UI change, capture a run, and treat the pass or fail verdict as a real gate instead of trusting the agent's own account of what happened. It is the wrong tool for a one-off summary of a short clip with no follow-up planned: a lighter single-purpose tool or a direct upload to a model gets that answer faster and with far less to install.

Observed

License

MIT

Language / runtime

Python engine (requires 3.11-3.13); CLI and workspace built on Node (requires ^22.19 or >=24)

Packaging

Distributed via PyPI (pip) and npm, plus an official Docker image

Interfaces

MCP server (stdio and streamable HTTP), CLI, REST API, and a Python library

Platform support

Install paths documented for Windows, macOS and Linux

Structural observation

Optional capabilities (perception, OCR, Whisper, indexing, MCP, REST, browser capture, cryptographic attestation, and five agent-framework adapters) ship as separate opt-in extras rather than one bundled install

Structural observation

Test suite marks network-dependent and real-model tests as opt-in, excluded from the default run

Core dependency surface

No API key or model provider is required for transcription, OCR, or the deterministic verification checks; a provider is only needed for vision-based answering and the critique loop