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dsh-vision-toolkit
Anionex/dsh-vision-toolkit
Vision tasks for text-only models: intent-aware image Q&A, long-screenshot OCR, UI reproduction, grounding, and pixel diff.
Install
Add dsh-vision-toolkit to DeepSeek Harness
via npm
Resolves a published tarball rather than cloning the repository, and installs without any extra setup. Swap `web` for your profile name if you run another one.
via GitHub · npm package
Installing from GitHub runs the project's build script, which pnpm blocks until you allowlist it — run the command once and pnpm prints the exact key to add under `allowBuilds` in ~/.dsh/profiles/web/pnpm-workspace.yaml.
Due diligence
Before you install dsh-vision-toolkit
- Source of record: Anionex/dsh-vision-toolkit — present in the community registry that DSH's own plugin market installs from.
- Licensed under MIT.
- A listing here is not a security review. Plugins run with your agent's permissions.
What dsh-vision-toolkit does
dsh-vision-toolkit is a DeepSeek Harness plugin that gives text-only agents task-focused access to images, screenshots and visual comparison results. It packages agent-vision-toolkit as a native DSH plugin. In DSH Web, an agent can receive a pasted image; text-only routes switch to a Vision Toolkit variant, retain a workspace path, and send the image together with the reason for inspection to the configured vision service. The default path uses a free Gemma 4 service without an API key. Operations such as cropping, pixel diffing, colour analysis, foreground extraction, SVG tracing and HTML screenshot rendering run locally. Outputs can include task-focused answers, original-image coordinates, OCR in Markdown with chunks and manifests, crops, transparent PNGs, palettes, editable SVGs, screenshots, heatmaps and JSON.
It is intended for agents handling screenshots, UI reconstruction, long-image OCR, element grounding and visual verification rather than generic image captioning. The toolkit is a good fit when a text-only model needs evidence it can use in a subsequent action, such as locating a button or diagnosing a visual mismatch. It is less suitable when an existing vision-capable model already covers these workflows, or when images must not be sent to the default vision service. The shared free service also has documented limits of 100/day, 400/day and 20/minute for per-client, global and burst usage. dsh-vision-toolkit is MIT-licensed and has no detected risk flags in this listing.
dsh-vision-toolkit documentation
How it behaves
The plugin integrates agent-vision-toolkit into DSH. In DSH Web, pasting an image causes text-only routes to use a Vision Toolkit variant and preserve a workspace path so the image can be reused. The request includes the inspection reason, allowing answers to focus on questions such as an error location or a button colour instead of returning only a general caption.
Capabilities and outputs
The documented workflows cover intent-aware image Q&A, element grounding with original-image pixel coordinates, long-screenshot OCR, UI reconstruction, asset extraction and screenshot comparison. The toolkit can produce:
- OCR Markdown, chunks, manifests and resumable run state;
- crops, transparent PNGs, colour palettes and editable SVG traces;
- HTML screenshots, difference percentages, ranked regions, heatmaps and JSON.
Service and local processing
New installations use the built-in Gemma 4 vision service and do not require an API key. Cropping, pixel diffing, colour analysis, foreground extraction, SVG tracing and HTML screenshots run locally rather than consuming vision API requests. A real image-request test is available in Settings; checking /models alone is not sufficient to verify image support.
Limits and platform notes
The README documents free-service limits of 100/day per client, 400/day globally and 20/minute for bursts. Windows first-time isolated-runtime setup supports Microsoft Store Python. The provided excerpt does not specify additional configuration keys or standalone tool command names.
Written from the project's own documentation and kept in sync with it. Where the two disagree, the source is authoritative — read the README on GitHub
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