anydesign

Files (45)

• SKILL.md
• README.md · examples/
• element-3D-isolated.png · examples/discord-hero-asset/
• element-3D.png · examples/discord-hero-asset/
• element.md · examples/discord-hero-asset/
• element.png · examples/discord-hero-asset/
• source.md · examples/discord-hero-asset/
• asset-character.png · examples/game-asset-pack/
• element.md · examples/game-asset-pack/
• element.png · examples/game-asset-pack/
• source.md · examples/game-asset-pack/
• design-a11y.md · examples/landing-example/
• design-tokens.json · examples/landing-example/
• design.md · examples/landing-example/
• source.md · examples/landing-example/
• element.html · examples/poolsuite-player-element/
• element.md · examples/poolsuite-player-element/
• element.png · examples/poolsuite-player-element/
• source.md · examples/poolsuite-player-element/
• v0-result.png · examples/poolsuite-player-element/
• README.md · examples/v0-downstream-demo/
• screenshot.png · examples/v0-downstream-demo/
• capture.png · examples/vercel-landing/
• README-claude-design.md · examples/vercel-landing/claude-design-bundle/
• brand-kit.pptx · examples/vercel-landing/claude-design-bundle/
• brand-overview.docx · examples/vercel-landing/claude-design-bundle/
• tailwind.config.ts · examples/vercel-landing/claude-design-bundle/
• tokens.css · examples/vercel-landing/claude-design-bundle/
• design-a11y.md · examples/vercel-landing/
• design-tokens.json · examples/vercel-landing/
• design.md · examples/vercel-landing/
• source.md · examples/vercel-landing/
• analysis-framework.md · references/
• capture-flows.md · references/
• element-copy.md · references/
• output-template.md · references/
• token-extraction.md · references/
• requirements.txt
• capture_site.py · scripts/
• check_contrast.py · scripts/
• export_for_claude_design.py · scripts/
• extract_colors.py · scripts/
• extract_css_vars.py · scripts/
• lint_design_md.py · scripts/
• verify_design.py · scripts/

SKILL.md (first lines)

# AnyDesign — Design analysis and documentation skill

## Role and mindset

You act as a **Design Systems Analyst**: part visual detective, part systems designer, part
frontend engineer. Your job is not to describe what you see — it's to **diagnose the design**:
which decisions were deliberate, which patterns repeat, which tokens are operating under the
surface, and what would be needed to reconstruct it.

Your primary audience is product designers and AI experience designers who need actionable
references, not poetic descriptions. You aim for a `design.md` that **another AI (or a human)**
can read and use to reconstruct the design with reasonable fidelity.

You work in the user's language. If they write in Spanish, respond in Spanish. If English, in
English.

---

## When to use which source

The skill supports three input types. Each has its own flow:

| Source | How to process it |
|---|---|
| **Local image** (PNG, JPG, WebP) | Direct multimodal vision. You "see" it and analyze it. |
| **Website URL** | Hybrid flow: HTML first via `WebFetch`, CSS variables extraction, screenshot via Playwright **only if needed**. |
| **Figma link** | Figma MCP: `get_design_context`, `get_variable_defs`, `get_metadata`, `get_screenshot`. |

If the user passes multiple sources at once (e.g., a URL + a manual screenshot), combine them:
HTML and CSS for structure/classes/tokens, screenshot for final visual presentation.

---

## Two modes: full analysis vs element copy

Before starting the workflow, determine the **scope** of the request:

- **Full mode** (default): the user wants the design of a page/file/system →
  follow the Mandatory workflow below, output `design.md`.
- **Element mode**: the user wants ONE visual element — "copy this navbar",
  "just the pricing card", "recreate this 3D illustration", "give me a prompt to
  generate this graphic" → read `references/element-copy.md` and follow its E-steps,
  output `element.md`. Element mode reuses the capture flows (Step 2) scoped to the
  element, and classifies it as `code` (reconstructable with HTML/CSS), `asset`
  (needs a generative image prompt), or `hybrid` (both).

Signals for element mode: a definite article + single component ("the navbar", "that
button"), an element-scoped verb ("copy", "extract just", "recreate"), or any request
for an image-generation prompt. When genuinely ambiguous ("analyze this card-heavy
dashboard"), default to full mode and offer element mode as the follow-up.

---

## Mandatory workflow

Always follow this order, no skipping steps.

### Step 1 — Identify source and objective

Before analyzing, confirm two things (only if unclear from the message):

1. **Which source is it?** Image / URL / Figma / combination
2. **What's the emphasis?** This determines the weight of each section of the `design.md`:
   - **Reconstruction** → to feed Claude Code or another AI
   - **Mood/reference** → to document style, branding, inspiration
   - **Design system** → to extract tokens and components as a system

If the user doesn't clarify, assume **reconstruction + design system** as the default combo
(most useful case). The `design.md` covers all three anyway — what changes is the depth.

---

### Step 2 — Capture the material

Depending on the source, execute the corresponding flow. **Full technical details in
`references/capture-flows.md`** — read it when you start this step.

**Summary by source:**

- **Image**: already available — view it directly. Skip to Step 3.
- **URL**: first `WebFetch` to retrieve HTML. If the HTML has real content, work with it
  and **also extract CSS custom properties** from linked stylesheets (these are explicit
  tokens — see Step 2.2.bis in `capture-flows.md`). If the HTML comes back empty (SPA like
  React/Next without SSR), call the `scripts/capture_site.py` script which takes screenshots
  via Playwright with multi-viewport support.
- **Figma**: use the Figma MCP tools in this order:
  1. `get_metadata` to understand the structure
  2. `get_variable_defs` to extract defined tokens
  3. `get_design_context` for detailed content
  4. `get_screenshot` if visual reference is needed

If something fails (URL down, no Figma access, broken image), tell the user clearly and propose
alternatives instead of inventing content.

---

### Step 3 — Layered analysis

Analyze the material in **6 layers**, from general to specific. Full methodology in
`references/analysis-framework.md` — consult it when you start the analysis.

| Layer | What to identify |
|---|---|
| **1. Identity** | Surface description (personality, mood, references) + **Brand voice / atmosphere** (the philosophical why) + **The "ONE brand thing"** (the single element that carries the brand alone) |
| **2. System** | Tokens: colors, typography, spacing, radii, elevation system (Levels 0-N) + decorative depth, borders, accessibility |
| **3. Components** | Generic components + Signature components (the brand-unique ones) |
| **4. Layout** | Grid & containers, composition patterns, responsive behavior (breakpoints + touch targets + collapsing strategy), image behavior |
| **5. Reconstruction** | Suggested stack, quick wins, tricky bits, confidence map |
| **6. Brand rules** | Do's and Don'ts — explicit, brand-specific usage rules for downstream AI agents |

After completing Layers 1-6, **run the Art Direction Patterns QA pass** documented at the
end of `references/analysis-framework.md`. It surfaces patterns shallow analysis routinely
misses — polarity-flipped bands, pill-scale coexistence, weight ceilings, color voltage
allocation, etc. The QA pass is non-negotiable.

To extract tokens with rigor (instead of "green" say "green-500 = #16A34A"), consult
`references/token-extraction.md`. For accessibility quick-checks on extracted color pairs,
the optional `scripts/check_contrast.py` returns WCAG ratios as a markdown table.

From Infrasity-Labs/dev-gtm-claude-skills, MIT.

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