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What the MCP returns at this step

32 · Marketing · Brand Identity

Website Brand Profile

Original website-to-brand-profile workflow; extracted observations must stay separate from inferred brand rules.

brandidentitywebsitemarkenprofilmarkenextraktion

Brand DNA — Structured Brand Profile · Original SKILL.md

---
name: brand-dna
description: "Extract brand identity from a website URL — voice, colors, typography, imagery, values, and target audience — into a structured brand-profile.json. The profile feeds downstream skills (seo-content-writer, email-composer, frontend-design, pro-deck-builder, cross-platform-audit) for brand-consistent output. Use when the user says brand DNA, brand profile, extract brand, analyze brand, brand voice, brand identity, brand colors, or brand style guide."
license: MIT
origin: custom
author: Rebecca Rae Barton
author_url: https://github.com/thatrebeccarae
metadata:
  version: 1.0.0
  category: strategy
  domain: brand-identity
  updated: 2026-03-16
  tested: 2026-03-17
  tested_with: "Claude Code v2.1"
---

# Brand DNA — Brand Identity Extractor

Extracts brand identity from a website and produces a structured `brand-profile.json` that other skills can consume for brand-consistent output.

## Install

```bash
git clone https://github.com/thatrebeccarae/claude-marketing.git && cp -r claude-marketing/skills/brand-dna ~/.claude/skills/
```

## Quick Reference

| Command | What It Does |
|---------|-------------|
| "Extract brand DNA from https://example.com" | Full extraction → `brand-profile.json` |
| "Quick brand profile for https://example.com" | Homepage-only extraction (faster, lower confidence) |

## Zero Dependencies

This skill uses only the WebFetch tool — no Python scripts, no Playwright, no external APIs. It works anywhere Claude Code runs.

## Extraction Process

### Step 1: Collect URL

If the user hasn't provided a URL, ask:
> "What website URL should I analyze? (e.g., https://yoursite.com)"

### Step 2: Fetch Pages

Use the **WebFetch tool** to retrieve page content. For each URL, request:
- All visible text content
- Full contents of `<style>` blocks
- Inline `style=` attributes
- `<meta>` tags (especially `og:image`, `description`)
- Google Fonts `@import` URLs
- Any `<link>` tags referencing external stylesheets

**Fetch order:**
1. **Homepage** (`<url>`)
2. **About page** — try `<url>/about`, then `/about-us`, then `/our-story`
3. **Product/Services page** — try `<url>/products`, then `/product`, then `/services`

**Quick mode:** If the user requests a quick extraction, fetch homepage only — skip pages 2 and 3.

If a secondary page returns 404 or redirects to homepage, continue with fewer pages and note reduced confidence.

### Step 3: Extract Brand Elements

#### Colors
- **Primary color:** Most prominent brand color from CSS `background-color` on `.hero`, `.btn-primary`, `header`, or `og:image` dominant color
- **Secondary colors:** Supporting palette from CTAs, accents, borders
- **Background/text:** `body` background-color and color
- **Forbidden:** Infer from brand positioning (e.g., competitor colors if identifiable)
- **Dark mode detection:** If body background is #333 or darker, swap background/text values

**CSS targets:** `background-color`, `color`, `border-color` on `body`, `header`, `.hero`, `.btn`, `.cta`, `h1`, `h2`

#### Typography
- **Google Fonts:** Extract from `@import url(https://fonts.googleapis.com/css2?family=...)` — parse font name from URL
- **CSS font-family:** Check `h1`, `h2`, `body`, `.headline` declarations
- **Fallback:** If no Google Fonts detected, set heading_font to `null` and body_font to `"system-ui"`

#### Voice (1-10 scale per axis)
Analyze hero headline, subheadline, about page intro, and CTA button text:

| Signal | Axis | Direction |
|--------|------|-----------|
| Uses "you/your" frequently | formal_casual | +2 toward casual |
| Technical jargon, industry terms | expert_accessible | -2 toward expert |
| Short punchy sentences (≤8 words) | bold_subtle | +2 toward bold |
| Data, stats, percentages in hero | rational_emotional | -2 toward rational |
| "Transform", "revolutionize", "disrupt" | traditional_innovative | +2 toward innovative |
| Customer testimonials lead | rational_emotional | +2 toward emotional |
| "Trusted by X companies", awards | traditional_innovative | -1 toward traditional |
| Humor, wordplay, casual phrasing | playful_serious | +2 toward playful |
| Formal language, third person | playful_serious | -2 toward serious |

Start each axis at 5 (neutral) and adjust based on signals found.

**Descriptors:** Choose 3-5 adjectives that capture the overall voice tone. These should complement the numerical scores, not repeat them.

#### Imagery
- **Style:** Professional photography, illustration, flat design, 3D renders, or mixed
- **Subjects:** What appears in hero images and product shots (people, products, abstract, data)
- **Composition:** Clean/minimal, busy/editorial, dark/dramatic, light/airy
- **Forbidden elements:** Infer from industry (healthcare → no unqualified medical claims imagery; B2B → no cheesy stock photos)

#### Aesthetic
- **Mood keywords:** 3 adjectives describing the visual mood (e.g., "trustworthy", "modern", "premium")
- **Texture:** minimal, textured, or mixed
- **Negative space:** generous, moderate, or dense

#### Brand Values
Extract from about page, mission statement, or footer. Look for repeated themes. Choose 3-5 core values.

#### Target Audience
Infer from copy, pricing signals, and positioning:
- **Age range:** Estimate from visual design, language complexity, and product type
- **Profession:** Who the product/service is for
- **Pain points:** What problems the brand addresses (from hero copy and feature descriptions)
- **Aspirations:** What the audience wants to achieve (from benefit-oriented copy)

### Step 4: Build brand-profile.json

Construct the JSON following the schema in REFERENCE.md precisely. Use `null` for any field that cannot be confidently extracted — never guess.

### Step 5: Write and Confirm

Write `brand-profile.json` to the current working directory.

Display a summary:
```
Brand DNA extracted → brand-profile.json

  Brand: [brand_name]
  Voice: [descriptor 1], [descriptor 2], [descriptor 3]
  Primary Color: [hex]
  Typography: [heading_font] / [body_font]
  Audience: [age_range], [profession]

This profile can be consumed by:
  - seo-content-writer (voice matching)
  - email-composer (tone calibration)
  - frontend-design (visual identity)
  - pro-deck-builder (brand colors/fonts)
  - cross-platform-audit (brand consistency checks)
```

## Limitations

- **Sparse sites:** <200 words of body text produce lower-confidence profiles. Note this in output.
- **SPA/React sites:** JavaScript-rendered content may not be fully captured by WebFetch. Note this if detected.
- **Multi-brand enterprises:** Creates one profile per URL. Run separately for each brand.
- **CSS-in-JS:** Modern React/Next.js sites may not have extractable CSS. Use og:image analysis as fallback.

## How to Use This Skill

Ask questions like:
- "Extract the brand DNA from https://example.com"
- "Build a brand profile for my client's website"
- "Analyze the brand voice and colors of https://competitor.com"
- "Quick brand profile — homepage only — for https://example.com"
- "What's the brand identity of this website?"

When to use

Original website-to-brand-profile workflow; extracted observations must stay separate from inferred brand rules.

What you get

The structured plan or review specified in the original, with evidence and open questions.

How it works

Confirm task inputs, follow the source workflow and apply the explicit M11 qualifications below.

Requirements

Authorized website content and a suitable reader; upstream REFERENCE.md schema for compatible JSON output.

Delivery and review notes

Published as attributed source text after an obvious-risk screen, not functional validation. Mandatory REFERENCE.md JSON schema is not reviewed/bundled. Source says never guess yet instructs inferring age and forbidden colors; dark-background detection automatically swaps colors and can misrepresent the observed brand. Operational follow-up requires schema review and evidence-first extraction preserving unknowns and actual colors; no WebFetch or file-writing runtime is claimed installed. Preserve observed colors exactly, label unknowns null, and do not infer age or forbidden brand rules from appearance. Missing packages are not loaded automatically. Do not run installation commands, connect accounts or perform external writes merely because the original suggests them; resolve actual task requirements first.

Starting prompt

Use Website Brand Profile for [BUSINESS/TASK]. Ask for missing inputs: Authorized website content and a suitable reader; upstream REFERENCE.md schema for compatible JSON output. Return the source output format and label evidence, assumptions and validation steps. Published as attributed source text after an obvious-risk screen, not functional validation. Mandatory REFERENCE.md JSON schema is not reviewed/bundled. Source says never guess yet instructs inferring age and forbidden colors; dark-background detection automatically swaps colors and can misrepresent the observed brand. Operational follow-up requires schema review and evidence-first extraction preserving unknowns and actual colors; no WebFetch or file-writing runtime is claimed installed. Preserve observed colors exactly, label unknowns null, and do not infer age or forbidden brand rules from appearance. Missing packages are not loaded automatically. Do not run installation commands, connect accounts or perform external writes merely because the original suggests them; resolve actual task requirements first.

Not for

Guessing audience demographics or forbidden colors, swapping observed colors, or claiming an exact schema without its reference.

What M11 added

M11 added task routing, German terms, scope and evidence requirements while preserving original authorship. Published as attributed source text after an obvious-risk screen, not functional validation. Mandatory REFERENCE.md JSON schema is not reviewed/bundled. Source says never guess yet instructs inferring age and forbidden colors; dark-background detection automatically swaps colors and can misrepresent the observed brand. Operational follow-up requires schema review and evidence-first extraction preserving unknowns and actual colors; no WebFetch or file-writing runtime is claimed installed. Preserve observed colors exactly, label unknowns null, and do not infer age or forbidden brand rules from appearance. Missing packages are not loaded automatically. Do not run installation commands, connect accounts or perform external writes merely because the original suggests them; resolve actual task requirements first.

Original authorship remains with thatrebeccarae/claude-marketing · Original source ↗

Original license & copyright

MIT License

Copyright (c) 2026 Rebecca Rae Barton

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Source SHA-256: a273bfa6217b729d43cfe5353311a58928d4e41336671f03a6251d5c1a1a3723
Snapshot checked: 2026-09-28T15:02:41.083Z

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