dsh-vision-toolkit

This project is the first comprehensive vision-tool plugin in the DeepSeek Harness ecosystem: it was initiated before internal beta and built during the beta with reference to agent-vision-toolkit.

Original work: The system and division of responsibilities behind these visual tools, together with the vision-skills Skill, were personally created and continuously refined by the author through long-term real-world use and repeated iteration.

If this project helps you or gives you some inspiration, feel free to star & fork.

Highlights

• Paste an image and ask directly. In DSH Web, pasting an image switches the text-only model to its (Vision Toolkit) variant automatically — no manual path copying or model changes. Native thumbnails, session history, and workspace paths stay intact; Web can preview artifacts.
• One command to install. After installation, configure a vision provider in Settings → Vision Toolkit and start using the tools.
• Not just a caption — the content that matters. The model does not produce a generic description; it extracts evidence around the current task, such as “Where is the error?” or “Where is the button?”.
• A battle-tested visual-task methodology. The bundled Skill tells the agent what to look at for different visual tasks, which tool to choose, how to proceed, and how to verify the result.

agent-vision-toolkit gives an agent more than image captions: it can read, locate, crop, trace, rebuild, and verify visual work. DSH Vision Toolkit is its native DeepSeek Harness integration, bringing that workflow into Web and Headless Profiles.

This project has two layers:

1. Visual tools and a Skill: the agent learns when to inspect, ground, OCR, crop, trace, or compare pixels.
2. Native DSH integration: those capabilities live inside Profiles, sessions, Settings, Artifacts, and the Web UI.

dsh plugin --profile web add @anionex/dsh-vision-toolkit

Upstream toolkit: Anionex/agent-vision-toolkit · Project website: agent-vision.anionex.me

❤️ Sponsor

Want to sponsor this project? See FUNDING.md or email [email protected].

Click to collapse

Thanks to AIHubMix for sponsoring this project! AIHubMix is a stable, high-concurrency AI model API gateway that connects Claude, GPT, Gemini, DeepSeek, and other mainstream models through a single API key, compatible with multiple protocols, with free model options available. To sign up, use the AIHubMix entry outside mainland China or the Inferera entry within mainland China.

Thanks to E-API for sponsoring this project! E-API aggregates mainstream AI models behind OpenAI-, Anthropic-, and Codex-compatible APIs, with selected Claude models up to 98% below official prices and DeepSeek V4 models about 25% below official prices.

Contents

• Highlights
• Recent updates
• Who it is for
• See it in action
• Quick start: three steps
• Toolbox
• Configuration and limits
• Troubleshooting
• Donation
• Development and community

Recent updates

• 2026-08-20 · AIHubMix setup guide: Added a screenshot-based guide for getting an API key through the Inferera entry and configuring the Gemini 3.7 Flash vision model; Settings now links directly to this guide.
• 2026-08-19 · Transparent routing by default: The model selector keeps one entry per model with the original name, and image input (paste, history, read_image) works without manually switching to a (Vision Toolkit) variant. Disable “Transparent variant routing” in advanced settings → image input to restore the explicit entries.
• 2026-08-16 · Windows Python: Added Microsoft Store Python support, fixing first-time isolated-runtime setup failures for affected Windows users.
• 2026-08-17 · Vision upgrade: Switched the default model to Gemini 3.7 Flash and fixed Qwen/Gemini bounding-box coordinate order.
• 2026-08-16 · Image paste: Text-only routes now switch to a (Vision Toolkit) variant and keep a workspace path, fixing blocked pastes and images that could not be reused later.
• 2026-08-16 · Service stability: Expanded service capacity to reduce peak-time 429 responses.
• 2026-08-16 · Real model test: Added a full image-request test in Settings, fixing the false confidence caused by a successful /models request to a model that still cannot process images.

Who it is for

1. Want an interaction experience similar to a multimodal model: paste an image directly and ask a question or make a request.
2. Want more than image Q&A — complete more complex, high-value visual tasks such as turning a sketch into a front-end page, converting an image into HTML, or extracting chat messages from long screenshots; more scenarios are added over time.

The bundled vision-skills Skill carries the complete upstream playbooks, explaining when to use each workflow, in what order to call the tools, and how to verify the result:

See it in action

Paste an image directly into DSH

Paste an image into the conversation. A text-only model can switch to its Vision Toolkit variant and inspect the image in the context of the user's question.

Screenshot to editable page

Prompt example: “(Use vision-skills) Rebuild this image into HTML.”

Left: the reference screenshot. Right: an editable HTML/CSS result. The result can continue into screenshot rendering and pixel comparison instead of ending as an image description.

Sketch to working interface

Left: a hand-drawn reference. Right: the working interface reconstructed from it.

Prompt example: “(Use vision-skills) Turn this sketch into a working front-end page.”

Fast UI restoration: an approximate first pass

Prompt example: “(Use vision-skills) Quickly rebuild this image into HTML.”

Left: the original page. Right: a fast reconstruction that preserves the main layout, content, and visual hierarchy while allowing approximate colors and library icons. Fast mode targets a first screenshot in about three minutes.

Quick start: three steps

1. Install

dsh plugin --profile web add @anionex/dsh-vision-toolkit

You can install it into a Headless Profile too:

dsh plugin --profile headless add @anionex/dsh-vision-toolkit

Using DSH Desktop? It bundles its own dsh CLI and intentionally does not add it to your system PATH. Open DSH Terminal from the tray and run the command there, targeting the Desktop profile:

dsh plugin --profile desktop add @anionex/dsh-vision-toolkit

Then restart DSH Desktop. The built-in plugin marketplace in DSH Desktop 2.0.1 has known installation issues; the terminal command above is the reliable path until a fixed Desktop release is available.

For the full Desktop install, update, and troubleshooting walkthrough, see Installing and updating in DSH Desktop.

2. Restart and check it

Restart a running Web Profile, then open Settings → Vision Toolkit, configure a vision provider, and run Test vision model to confirm it is reachable.

The first start prepares an isolated runtime: the plugin prefers a system Python 3.11+; when none is found, it downloads a hash-verified standalone Python (about 35 MB) from the domestic mirror (dsh-vision-python-bootstrap-1317715800.cos.ap-guangzhou.myqcloud.com) on first use, falling back to the GitHub release when the mirror is unreachable. The locked runtime dependencies (Pillow, NumPy, vtracer) are installed from the Tencent Cloud PyPI mirror (mirrors.cloud.tencent.com/pypi/simple) first and fall back to the official PyPI index. A normal installation does not require an agent-vision-toolkit source checkout or a local path setting.

3. Paste an image and describe the outcome you want

Paste a screenshot into the conversation or place an image in the session workspace, then invoke /vision-skills. For example:

Inspect this screenshot. Explain the error and tell me what to fix first.
Find the login button in the top-right corner, return original pixel coordinates, and make a boxed preview.
Crop this icon and convert it to SVG.
Rebuild the page from reference.png. After each pass, render it and run a pixel diff until the major differences are gone.

Toolbox

The plugin provides 10 tools that can be called independently or composed into a workflow:

Coordinates always use original-image pixels in x1,y1,x2,y2 form, so grounding output can feed directly into cropping, tracing, or later automation.

For a long HTML document, pass fullPage=true. The requested width and height remain the layout viewport, while the resulting PNG covers the complete document and reports pageHeight in CSS pixels.

How it works

The plugin keeps image understanding and deterministic local image processing in one Agent workflow. The diagram below shows the implementation boundary.

Descriptions that keep the task in view

Most vision bridges for text-only models ask a multimodal model for a generic description and hand it to the text model, adding a semantic layer where information is lost. Vision Toolkit instead recovers **why the agent wa