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dsh-vision-primitives

Model Inference Updated 2026.08.24

Run the following command in DeepSeek Harness:

dsh plugin install zouyuanqing/dsh-vision-primitives

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install zouyuanqing/dsh-vision-primitives in your DeepSeek Harness terminal to install the plugin (source: https://github.com/zouyuanqing/dsh-vision-primitives); the full vision_* toolset registers automatically after restarting DSH.

About this plugin

Text-only agents operating GUIs or analyzing screenshots often struggle with a persistent problem: vision models return vague coordinates that cannot be resolved to precise pixel positions. dsh-vision-primitives gives DeepSeek Harness agents a pixel-accurate set of eyes anchored on a Set-of-Mark numbered grid and deterministic coordinate math. A pure-JS kernel handles capture, lossless local zoom, geometric annotation, frame differencing, color segmentation, and native OCR entirely inside the DSH runtime with zero external MCP dependencies.

The plugin also integrates a MiMo V2.5 multimodal backend (vision_describe / vision_locate) and a Visual Evidence Protocol (VEP) that lets text-only models structurally interpret images. A chat-box image bridge ensures that pasting images while using a text-only model no longer raises an error; on send, images are cached as workspace files the model can read on demand.

Ideal for DSH developers building desktop automation, UI testing, or screenshot parsing agents that require precise visual-coordinate reasoning, especially teams who prefer a single-runtime, dependency-light setup over external MCP services.

Use Cases

  • Precisely locating buttons and UI elements via a numbered grid when an agent drives a desktop GUI
  • Injecting structured layout and element bounding boxes into a text-only model context after screenshot analysis
  • Detecting inter-frame differences and localizing changed regions in automated UI regression testing

Best For

  • Developers building desktop automation or UI-testing agents inside DeepSeek Harness
  • Teams that need pixel-accurate visual coordinates without spinning up external MCP services
  • Architects extending text-only models with deterministic, structured visual understanding