oil-oil/dsh-vision

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dsh-vision is a plugin for DeepSeek Harness. Vision-capable models keep receiving images natively. When the selected main model is text-only, the plugin asks a separate vision model to observe the original images, then lets the original DeepSeek model produce the final answer.

How it works

The plugin does not replace the main model selected in Harness. Multiple image attachments are analyzed together, so comparisons and combined evidence work naturally. The user's task is forwarded unchanged instead of being wrapped in a fixed report template.

Install

Use the plugin manager built into DeepSeek Harness:

npx @deepseek-ai/dsh plugin --profile web add github:oil-oil/dsh-vision

Restart Harness, then paste or drag images into the composer as usual. The plugin replaces the official deepseek-official adapter while preserving its model catalog, settings, and credentials. It also adds a Vision Recognition card to Settings → Plugins → Plugin configuration.

DeepSeek Harness is still in Developer Preview. This release supports 0.1.0-rc.6 and 0.1.0-rc.7; its settings-card registration satisfies both the legacy list Slot and the current keyed Slot without relying on private runtime inspection.

Configure Vision Recognition

Open Settings → Plugins → Plugin configuration → Vision Recognition. Select ZenMux, Alibaba Cloud Model Studio, TokenDance, or OpenRouter, then enter its API key. The same card lets you change the model ID, API endpoint, and image limit.

The API key is stored through Harness's official credential service. It is write-only in the browser: the plugin can report whether a key exists, but never reads it back into the page, chat, settings document, or session log.

Routing follows the user's choice. A provider selected in Vision Recognition is primary for text-only models. Other enabled Harness vision routes, an existing see configuration, and local OCR are failover only. When the current main model supports images, the original images pass through natively and none of these bridge routes are used.

Choose Automatic to skip plugin-managed cloud credentials. The bridge then tries image-capable models already configured in Harness, followed by see-compatible private configuration and local OCR. A Harness custom model must declare image as an input modality or it remains a text model.

Advanced file configuration

Most setups should use the UI. The equivalent non-secret fields live in the existing llm-deepseek section of $DSH_HOME/settings.yaml:

llm-deepseek:
  visionBackend: zenmux
  visionBackendModel: qwen/qwen3.7-plus
  visionBackendBaseURL: https://zenmux.ai/api/v1
  maxImages: 8

Do not put API keys in this file. Save them in the Vision Recognition card or provide the matching environment variable. Changes apply without a restart.

see-skill compatibility

If Harness has no usable vision model, the plugin also reads ~/.config/see/config.env. It supports ZenMux, Alibaba Cloud Model Studio, OpenRouter, and TokenDance. Environment variables override the private config file.

export SEE_PROVIDER=zenmux
export ZENMUX_API_KEY=your-key

SEE_PROVIDER selects the primary provider. Other providers with configured keys are failover routes only. If no provider is selected and only one is configured, that provider is used.

When no cloud key is available, or every cloud route fails, the plugin tries local capabilities:

• macOS: built-in Vision OCR, with no extra dependency.
• Linux / Windows: Tesseract with the required language data installed.

Local fallback is primarily OCR and is not equivalent to full multimodal understanding.

Security boundary

• Original images are sent only to vision services configured by the user.
• Vision output is marked as untrusted observation data; instructions inside an image receive no system authority.
• Generated vision context affects only the current model request and does not rewrite message history.
• API keys are resolved through Harness credentials or the user's private see config and are never written to this repository.

Development

pnpm install
pnpm check

The project is available under the MIT License. Cloud routing, joint multi-image analysis, and local fallback behavior are based on the MIT-licensed oil-oil/see-skill. The DeepSeek icon comes from the official deepseek-ai/deepseek-harness repository.