akqwpeter-prog/dsh-media-skills

🎨 dsh-media-skills

Give DeepSeek Harness eyes — and a brush. Read images in any chat, generate new ones, all with free models.

DeepSeek Harness is brilliant at reasoning — but a text-only model can't see the image you just dragged into the chat. This bundle fixes that with two free skills and a free vision model route:

• 📎 Paste to read — paste, drag, or pick an image in any session; the free vision model turns it into text your current model understands.
• 👁️ vision-review — analyze images and screenshots, catch UI visual bugs, detect watermarks, turn images into text.
• 🎨 media-tools — generate illustrations, avatars, backgrounds and banners with a free, watermark-free model.

No hardcoded keys, no paid API, no file saving, no session switching.

Why · Quick start · See it in action · Usage · Keys & privacy · FAQ · Examples

English · 简体中文 · 繁體中文 · 日本語 · 한국어 · Español · Deutsch · Português · Русский

---

🤔 Why

Most DSH vision plugins only read images — and many push you through a shared third-party endpoint. dsh-media-skills takes a different stance:

	This bundle	Typical vision-only plugin
Read images for free	✅ Zhipu GLM-4V-Flash	✅
Generate images for free	✅ SiliconFlow Kolors	❌ usually absent
Auto model route in the picker	✅ installed automatically	sometimes
Keys committed to the repo	❌ never — keys stay local	⚠️ often required
Docs in multiple languages	✅ 9 languages	❌ usually English only
Privacy	✅ you choose the provider; images only go to your provider	shared free endpoints can see your images

Why bring your own free key instead of a built-in anonymous endpoint? Privacy and reliability. Your images go only to the provider you choose, under your account and your rate limits — no shared third-party service in the middle.

✨ What you get

Capability	What it does	Model	Cost
📎 Paste-image reading	In a text-only session, the input bar gains an “Add image” button (paperclip); pasted images are auto-described by the vision model and handed to the current model as text	Zhipu GLM-4V-Flash	Free
🧠 Vision model route	「智谱 GLM-4V-Flash（视觉）」 appears in the model selector automatically — pick it for a new conversation and talk about images directly	Zhipu GLM-4V-Flash	Free
👁️ vision-review	Analyze / recognize / describe images & screenshots; catch UI visual bugs (overlap, overflow, misalignment); detect watermarks/logos; turn images into text. Optional --structured mode returns ModLens-style evidence JSON (summary, full OCR, reading-order layout, entities/relations, uncertainty). Engine failover chain: GLM-4V-Flash → Google Gemini (auto-joins with a free GEMINI_API_KEY) → any OpenAI-compatible endpoint	Zhipu GLM-4V-Flash + Google Gemini	Free
🎨 media-tools	Generate images, illustrations, avatars, backgrounds, banners	SiliconFlow Kolors	Free, no watermark

⚡ Quick start

dsh plugin --profile <name> add github:akqwpeter-prog/dsh-media-skills

1. Get two free keys (~2 minutes, no payment):

   • Zhipu — open.bigmodel.cn → API Keys (glm-4v-flash is free)
   • SiliconFlow — siliconflow.cn → API Keys (Kolors is free)
   • (optional third) Google Gemini — aistudio.google.com → Get API key; joins the vision failover chain automatically

2. Add them in the Web GUI (Settings → Models → the zhipu-vision provider's API Key field), or use the credentials file:

   # ~/.dsh/.credentials.yaml (chmod 600)
   GLM_API_KEY: <your key>
   

3. Restart dsh web, then hard-refresh (Cmd+Shift+R).

Verify: the model selector shows 智谱 GLM-4V-Flash（视觉）. If your Harness build supports paste-image reading, the input bar also has a 📎 Add image button — paste an image in any session and it arrives as a text description.

Full walkthrough and troubleshooting: docs/SETUP_VISION_EN.md.

📸 See it in action

Paste an image in a text-only session → the free vision model describes it → your model answers. The same bundle also generates new images on demand.

How it works in one picture:

🚀 Usage

Three ways to read images:

Way	How	When
A. Paste directly (recommended)	In any session, click the 📎 button / drag / paste an image and send	Everyday image questions — no file saving, no model switching
B. Vision model session	New conversation, pick 智谱 GLM-4V-Flash（视觉）, paste images and chat	Multi-turn image conversations, native read_image
C. Files + skill	Put the image in the workspace and say “read this image with vision-review”	Batch review, scripted workflows

Descriptions follow your message language (Chinese message → Chinese description; English message → English description; no text → Chinese).

Also just say:

• “Look at this image / check this screenshot for visual bugs” → vision-review
• “Generate an image of …” → media-tools

🔑 Keys & privacy

Keys are never stored in this repo. Skill scripts read, in order: environment variables → ~/.dsh/secrets/media-tools.env → ~/.codex/secrets/media-tools.env (legacy fallback). The vision model route reads GLM_API_KEY from DSH's credential store.

Where to get the keys (all free): Zhipu — open.bigmodel.cn → API Keys (glm-4v-flash). SiliconFlow — siliconflow.cn → API Keys (Kolors). Google (optional, joins the vision failover chain automatically) — aistudio.google.com → Get API key.

# ~/.dsh/secrets/media-tools.env (chmod 600, one KEY=value per line)
GLM_API_KEY=...
SILICONFLOW_API_KEY=...
GEMINI_API_KEY=...   # optional

Your images are sent only to the provider you configure — never to this repo, never to a shared anonymous endpoint.

❓ FAQ

Does paste-image reading require a DeepSeek Harness core patch? The auto-describe pipeline lives in the Harness core (api-proxy image-admission logic; see docs/HARNESS_PATCH_EN.md). This bundle ships the model route + skills: the vision model works on any DSH build, but paste-image reading requires a Harness build with that core support — see FAQ Q1 in docs/SETUP_VISION_EN.md.

Why not just use a built-in free endpoint with no key at all? We prefer to let you own the route: your images go to the provider you pick, under your rate limits, with no shared middleman. The keys are free and take about two minutes to create.

Is media-tools really free? Yes — SiliconFlow Kolors is free and watermark-free. If a model is temporarily disabled, the skill lists available models and you can switch.

🎁 Examples

Sample material to try instantly — 6 AI-generated images with their prompts, plus a purpose-built vision test card (title, buttons, bar-chart values) for checking reading accuracy:

→ examples/README.md

🗺️ Layout

dsh-media-skills/
├── package.json           # dsh.bundle manifest
├── cordis.patch.yml       # plugin layer
├── index.js               # registers skills + seeds the zhipu-vision model route
├── skills/
│   ├── vision-review/     # image reading
│   └── media-tools/       # image generation
├── examples/              # sample images + vision test card
├── docs/
│   ├── screenshots/       # demo mockup & how-it-works diagram
│   ├── SETUP_VISION_EN.md # detailed setup guide (English)
│   ├── SETUP_VISION.md    # 详细配置指南（中文）
│   ├── HARNESS_PATCH_EN.md# core patch notes (English)
│   ├── HARNESS_PATCH.md   # 本体补丁说明（中文）
│   ├── COMPARE_MODLENS.md # 与 ModLens 的对比/共存（中文）
│   └── lang/              # READMEs in 9 languages
├── scripts/make-banner.py # regenerates docs/social-preview.png
└── docs/social-preview.png

🧩 Using ModLens alongside?

Both this bundle and ModLens give text-only models vision. Installed together they do not conflict: ModLens intercepts pastes first (path → modlens_read_image tool), and this bundle's api-proxy fallback handles anything it doesn't take over. See docs/COMPARE_MODLENS.md (中文) for the full comparison, the paste routing order, and how to point ModLens at the same free Zhipu endpoint.

🤝 Join the DSH plugin ecosystem

DeepSeek Harness developer preview is still in its testing phase for Harness developers; core plugins and base APIs will keep iterating. We look forward to exploring the upper limits of intelligence together with developers worldwide, on top of open-source, open, reusable, and composable infrastructure.

• dsh-plugin topic
• Quickstart
• DeepSeek Harness repo

This repo is tagged dsh-plugin and listed in the awesome-dsh-plugin curated list. PRs, issues and translations are welcome.

📄 License

MIT