design-dna
4.079 instalações · zanwei/design-dna
Open source Repository Open in the app JSON README (API)
About
Skill publicada por zanwei/design-dna, com 4.079 instalações medidas pelo skills.sh. Instale com: npx skills add zanwei/design-dna@design-dna
Details
- Kind
- Agent skills
- Topic
- No topic detected
- Publisher
- zanwei
- Origin
- skillssh
- Category
- ferramentas
- Stars
- 1,688
- Forks
- 96
- Open pull requests
- 1
- Last push
- 2026-08-28T01:58:49Z
- Repository state
- ativo
- Language
- JavaScript
- License
- MIT
- Added
- 2026-08-30 15:21:39
- Updated
- 2026-09-02 18:01:28
- Origin id
zanwei/design-dna/design-dna
README
<h1 align="center">design-dna</h1>
<p align="center">
English | <a href="README.zh-CN.md">中文</a> | <a href="README.ja.md">日本語</a> | <a href="README.ko.md">한국어</a> | <a href="README.es.md">Español</a> | <a href="README.zh-TW.md">繁體中文</a>
</p>
An agent skill for extracting, structuring, and applying visual design identity as machine-readable "Design DNA" across three dimensions: design tokens, qualitative style, and visual effects.
https://github.com/user-attachments/assets/00e0a28d-42ce-4a08-a0c0-1ecf8b9f7e97
<h3 align="center">Other cases</h3>
https://github.com/user-attachments/assets/80793608-930d-42ca-951f-eb21ac188d54
https://github.com/user-attachments/assets/cd4cba94-cd2c-480f-8efa-4ac86e00ae1f
## Prerequisites
- [Node.js](https://nodejs.org/) environment installed
- Ability to run `npx` commands
## Installation
### Quick Install (Recommended)
```bash
npx skills add zanwei/design-dna
```
### Install to Specific Agent
```bash
# Cursor only, non-interactive, global install
npx skills add zanwei/design-dna -a cursor -g -y
# Claude Code only
npx skills add zanwei/design-dna -a claude-code -g -y
```
### Install from Local Clone
```bash
git clone https://github.com/zanwei/design-dna.git
npx skills add ./design-dna -y
```
### List Available Skills
```bash
npx skills add zanwei/design-dna --list
```
## What It Does
| Dimension | Role |
|-----------|------|
| **Design System** | Measurable tokens: color, typography, spacing, layout, shape, elevation, motion, components |
| **Design Style** | Qualitative perception: mood, visual language, composition, imagery, interaction feel, brand voice |
| **Visual Effects** | Beyond plain CSS: Canvas, WebGL, 3D, particles, shaders, scroll-driven motion, cursor effects, SVG animation, glassmorphism, etc. |
The skill drives a **three-phase** workflow:
1. **Structure** — Surface the full schema and field meanings (see `references/schema.md`).
2. **Analyze** — From screenshots, images, or URLs, produce a complete JSON profile (every field filled; conflicts noted).
3. **Generate** — Given DNA JSON plus content, implement the design (default: self-contained HTML/CSS/JS), following `references/generation-guide.md`.
Phases can be used alone or chained (e.g. Analyze → Generate).
## How It Works
Pipeline at a glance ([Mermaid](https://github.blog/news-insights/product-news/github-now-supports-mermaid-diagrams-in-markdown/) renders on GitHub):
```mermaid
flowchart LR
A["Reference designs<br/>Screenshots · URLs · images<br/><br/>Any design you admire"]
B["Design DNA JSON<br/>Quantified spec<br/><br/>Structured profile"]
C["Final output<br/>Faithful implementation<br/><br/>Production-ready UI"]
A -->|"Analyze — extract every visual property"| B
B -->|"Generate — apply DNA to your content"| C
B -.-> D["Save · reuse · version control"]
```
**Step 1 — Curate references.** Collect screenshots, images, or live URLs of designs whose visual identity you want to capture. Multiple references can be combined; the skill identifies dominant patterns and notes variants.
**Step 2 — Extract DNA.** Feed the references to the agent. It inspects every visual property across all three dimensions and outputs a complete, quantified Design DNA JSON — no empty fields, no guesswork. This JSON becomes a portable, reusable design specification.
**Step 3 — Generate from DNA.** Provide the DNA JSON together with your own content. The agent produces implementations that faithfully reproduce the original design language while adapting to your material.
The DNA JSON is the key artifact. Once extracted, it can be **committed to version control**, **shared across teams**, **reused across projects**, and **iteratively refined** — turning subjective "make it look like that site" into a precise, reproducible specification that any agent can consume.
> [!TIP]
> **Refining visual richness.** If the first pass still feels visually thin or under-detailed next to your references, run a deliberate **polish iteration**: re-attach the **same URLs or screenshots**. This narrows the gap between a workable draft and a reference-faithful, visually rich result without starting over.
>
> **Prompt:** **Against the reference, audit hierarchy, ornamentation, typographic rhythm, motion, materiality, and overall UI—then merge your conclusions back into the current implementation.**
## Deterministic Measurement (optional)
LLM color perception drifts toward familiar palette defaults — a brand pink like `#ff90e8` gets "seen" as `#ec4899` (ΔE ≈ 29). Two optional scripts make the Analyze and Generate phases measurable:
The manual commands below are for a local clone of this repository. Run them from the clone's root:
```bash
npm install --prefix ./scripts
# Analyze: measure the exact palette from a reference screenshot
node scripts/measure-colors.mjs reference.png > measured-colors.json
# Generate: score the implementation screenshot against the reference
node scripts/verify.mjs implementation.png measured-colors.json
```
When installed as an agent skill via Quick Install, the agent must resolve these scripts from the absolute directory containing the loaded `SKILL.md`; users do not need a `scripts/` directory in their project root.
`measure-colors.mjs` runs deterministic k-means clustering over the actual pixels (with perceptual ΔE merging of anti-aliasing noise) and outputs exact hexes, coverage as `0..1` fractions, background/text/accent roles, and the clustering `k`. `verify.mjs` reuses the recorded `k`, re-measures the generated output, and reports per-color ΔE and coverage drift with PASS/FAIL thresholds, giving the agent a self-correction loop instead of relying on the user's eye. The skill instructs agents to use both automatically when references are image files; no API keys required.
Same reference (bun.sh's hero), same agent — perceived rebuild vs measured rebuild:
## Compatibility
Follows the [Agent Skills specification](https://agentskills.io). Installable via [`skills` CLI](https://github.com/vercel-labs/skills) to all [supported agents](https://github.com/vercel-labs/skills#supported-agents) including Cursor, Claude Code, Codex, GitHub Copilot, and [39 more](https://github.com/vercel-labs/skills#supported-agents).
## Contributing
Issues and pull requests are welcome. For substantive behavior changes, update `SKILL.md` and any affected files under `references/` so the skill stays internally consistent.
## License
MIT
## Star History
[](https://star-history.com/#zanwei/design-dna&Date)