Plugin directory / Developer / dsh-qwen-multimodal
dsh-qwen-multimodal
Verified · install-tested on dsh wuwangmao
What it does
DSH bundle: Qwen multimodal bridge — vision (qwen3-vl), speech-to-text (qwen3-asr), text-to-image (qwen-image), for DeepSeek Harness
Works — verified, early-stage project
DSH bundle: Qwen multimodal bridge — vision (qwen3-vl), speech-to-text (qwen3-asr), text-to-image (qwen-image), for DeepSeek Harness It installs cleanly and boots without issues in our testing. It's early-stage but functional.
“Verified” means our automated CI actually ran dsh plugin add in a clean profile and it booted — nothing more. Feature descriptions and version compatibility are the author’s claims. This is not a security audit and not an endorsement of third-party code.
README
dsh-qwen-multimodal
A DSH bundle that gives text-only main models (e.g. DeepSeek) **three multimodal skills in one plugin** through Qwen APIs: vision, speech-to-text, and text-to-image — with a built-in generate-then-verify quality loop. | Tool | Capability | Backend | |---|---|---| |describe_image | Image / screenshot / OCR / chart understanding (multiple images at once) | Qwen VL (default qwen3-vl-flash) |
| transcribe_audio | Speech / recording transcription (wav/mp3/m4a/aac/flac/ogg/amr) | Qwen3-ASR (qwen3-asr-flash) |
| generate_image | Generate images from text and save them locally | Qwen-Image (qwen-image-plus) |
Media never enters the main model context: visual/audio content is converted to text, and generated images are saved to local files with paths returned by the tool.
How it works
All API calls reuse the original Python scripts inskills/deepseek-vision/scripts/*.py and the .env configuration (vision/audio use the Alibaba Cloud Bailian OpenAI-compatible endpoint; image generation uses the native multimodal-generation endpoint). The plugin itself is a pure-JS Cordis bundle depending only on the host's mounted subprocess / tools services — **no build step required for git installs**.
Install
From GitHub
``sh
dsh plugin --profile demo add github:wuwangmao/dsh-qwen-multimodal
`
Local checkout / tarball
`sh
dsh plugin --profile demo add ./dsh-qwen-multimodal
or
pnpm pack # then
dsh plugin --profile demo add ./dsh-qwen-multimodal-0.1.0.tgz
`
Before first use, configure your API key: copy skills/deepseek-vision/.env.example to
skills/deepseek-vision/.env and fill in VISION_API_KEY (create one in the Alibaba Cloud Bailian console; new users get free quota, college students get a ¥300 annual voucher). Vision/audio/image reuse the same key by default, or configure them separately (see .env.example).
Python
**Python 3.10+ is required** (the image-generation script uses int | None type-annotation syntax).
python is resolved from the system PATH by default. If it cannot be resolved, restate the plugin row in your profile's cordis.patch.yml and set config.pythonPath.
Configuration overrides
The plugin uses the bundled skill directory by default. To point it at an external directory (e.g. to reuse an existing .env and scripts, or to keep your key outside node_modules), restate the row:
`yaml
- insert:
- id: qwen-multimodal
name: dsh-qwen-multimodal
config:
skillDir: 'D:/qwen-vision'
pythonPath: 'C:/path/to/python.exe'
`
Usage
Once loaded, the model can call the three tools directly:
- describe_image({ images: ['screenshot.png'] }) — verbatim extraction of text/code/errors in images
- describe_image({ images: ['chart.png'], prompt: '逐字提取图中所有文字,保留原样' }) — custom prompt
- transcribe_audio({ audios: ['recording.m4a'], language: 'zh' }) — specify language for accuracy
- generate_image({ prompt: 'a cute orange cat on a windowsill watching the sunset', out_dir: './out' }) — generate and save locally
- generate_image({ prompt: '...', out_dir: './out', verify: true }) — generate, then automatically re-check the result with Qwen VL against the prompt (quality loop)
Layout
`
dsh-qwen-multimodal/
├── package.json # dsh.bundle manifest
├── cordis.patch.yml # bundle layer: inserts the plugin row
├── src/index.js # plugin: registers the three model tools (pure JS)
├── scripts/selfcheck.mjs # self-check: node scripts/selfcheck.mjs
└── skills/deepseek-vision/ # skill assets: SKILL.md + Python scripts + .env.example
``
License
MITInstall
Install the catalog once, then DeepSeek Harness can find and install any plugin from this site automatically:
dsh plugin add dshbase-catalog Then say "install dsh-qwen-multimodal for me" — your agent finds it in the directory and installs it. Docs: dshbase-catalog · verified packs.
This plugin is GitHub source (not published to npm) — install it straight from the repo:
Web profile:
dsh plugin --profile web add github:wuwangmao/dsh-qwen-multimodal Headless (CLI) profile:
dsh plugin --profile headless add github:wuwangmao/dsh-qwen-multimodal Test report
Verified: L1 install + L2 load + L3 runtime from GitHub source on dsh 0.1.0-rc.6.
When to use it
Extend the agent's coding surface — give it a new tool, workflow, or integration so it handles a dev task it couldn't before.
Who it's for
Developers who want dsh to behave like a teammate on real codebases — editing, running, and verifying changes rather than just answering.
For developers — extending it
The tool/command surface is the seam: expose more of the SDK, add smarter context wiring, or tighten the loop between code changes and verification.