Visual DNA
/install visual-dna
Design DNA
Extract visual identity from any reference. Apply it to any content. Reuse it everywhere.
Three phases — use any combination:
- Analyze — screenshots, images, or URLs → Design DNA JSON
- Generate — Design DNA JSON + your content → faithful implementation
- Structure — show the full schema when asked
Read references/schema.md for the full field list.
Read references/generation-guide.md for generation rules.
The Three Dimensions
| Dimension | What it captures |
|---|---|
| design_system | Measurable tokens: colour, typography, spacing, layout, shape, elevation, motion, components |
| design_style | Qualitative feel: mood, visual language, composition, brand voice, interaction personality |
| visual_effects | Special rendering: Canvas, WebGL, particles, shaders, scroll effects, SVG animation |
Phase 1: Analyze
When the user provides screenshots, images, or URLs:
- Read
references/schema.md— know every field before starting - For each reference: analyze or fetch and analyze
- Fill every field — no empty strings, no guesswork flagged as guesswork
- When references conflict: note the dominant pattern, mention variants
- Output complete Design DNA JSON
- Ask: "Want to adjust any values before generating?"
Key extraction rules:
- Colour: sample dominant palette by area. Primary = largest area, accent = CTA usage
- Typography: identify font class visually (geometric, humanist, serif). Estimate scale ratios from heading/body relationships
- Spacing: assess density by element proximity. Measure section rhythm consistency
- Visual effects: scan for Canvas, WebGL, Three.js, GSAP, particles, shaders, custom cursors. Set
enabled: falsefor anything not present
Phase 2: Generate
When the user provides DNA JSON + content:
- Read
references/generation-guide.md - Build CSS custom properties from
design_systemtokens - Apply
design_stylequalitative fields to subjective decisions - Implement
visual_effectsat the appropriate tech tier (CSS → Canvas → WebGL) - Fetch real assets from original URLs when possible — don't recreate
- Default output: self-contained HTML with inline CSS/JS
Priority order:
- Colour & typography (80% of visual identity)
- Spacing & layout
- Shape & elevation
- Design style qualitative fields
- Visual effects
- Motion & interaction
Quality check before delivering:
- Does it actually look like the reference?
- Are all design tokens applied (not just some)?
- Is the output self-contained (no external dependencies that could break)?
- Do visual effects degrade gracefully without JS?
The DNA JSON as an Asset
The extracted JSON is the key output — not just the generated UI. Once extracted:
- Commit it to version control
- Share it across teams and projects
- Feed it to any agent for any future generation
- Refine it iteratively
This turns "make it look like that site" into a precise, reproducible spec.
Pair with no-slop-ui
When generating UI from DNA:
- Apply
no-slop-uirules alongside DNA tokens - DNA tells you WHAT the design is
no-slop-uitells you what NOT to do (no AI defaults slipping in)- Together: faithful to reference, clean execution
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install visual-dna - 安装完成后,直接呼叫该 Skill 的名称或使用
/visual-dna触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Visual DNA 是什么?
Extract and apply visual design identity by analyzing images or URLs into structured Design DNA JSON, then generate matching UI with complete style and effects. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 41 次。
如何安装 Visual DNA?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install visual-dna」即可一键安装,无需额外配置。
Visual DNA 是免费的吗?
是的,Visual DNA 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Visual DNA 支持哪些平台?
Visual DNA 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Visual DNA?
由 Leo Stehlik(@leostehlik)开发并维护,当前版本 v0.1.0。