ads-dna

Ads DNA: Brand DNA Extractor

Extracts brand identity from a website and saves it as brand-profile.json for use by /ads create, /ads generate, and /ads photoshoot.

Quick Reference

Command	What it does
/ads dna <url>	Full brand extraction → brand-profile.json
/ads dna https://acme.com --quick	Fast extraction (homepage only)

Process

Step 1: Collect URL

If the user hasn't provided a URL, ask:

"What website URL should I analyze for brand DNA? (e.g. https://yoursite.com)"

Step 2: Fetch Pages

Use the WebFetch tool to retrieve each page. For each URL, use this fetch prompt:

"Return all visible text content, the full contents of any <style> blocks, inline style= attributes, <meta> tags, Google Fonts @import URLs, and any og:image values found on this page."

Fetch in this order:

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

If --quick flag was provided: fetch the homepage only; skip steps 2 and 3.

If a secondary page returns a 404 or redirect error, continue with fewer pages and note: "Secondary pages unavailable; extraction based on homepage only. Confidence may be lower."

Step 2b: Capture Brand Screenshots

After fetching pages, capture 3 screenshots for comprehensive brand anchoring. These serve as visual style references during /ads generate; the same approach Pomelli uses to anchor ad images to the actual brand aesthetic.

Capture the following:

1. Homepage hero section (above the fold):

python ~/.claude/skills/ads/scripts/capture_screenshot.py [url]

Saves: ./brand-screenshots/{domain}_homepage.png

2. Product or services page:

python ~/.claude/skills/ads/scripts/capture_screenshot.py [url]/products

Saves: ./brand-screenshots/{domain}_product.png

3. About page (brand personality):

python ~/.claude/skills/ads/scripts/capture_screenshot.py [url]/about

Saves: ./brand-screenshots/{domain}_about.png

If a page is not found or returns an error, skip it gracefully and continue with the remaining pages.

If --quick flag was provided: skip screenshot capture entirely.

If capture fails (Playwright not installed, network error, JS-heavy SPA that times out):

• Log: "Screenshot capture skipped; run: python3 -m playwright install chromium"
• Continue without screenshots
• Do NOT set the screenshots field in brand-profile.json

Step 3: Extract Brand Elements

From the fetched HTML, extract:

Colors:

• og:image meta tag → analyze dominant colors (note 2-3 prominent hex values)
• CSS background-color on body, header, .hero, .btn-primary
• CSS color on h1, h2, .btn
• CSS border-color or background on .cta, .button
• Identify: primary (most prominent brand color), secondary (supporting colors), background, text

Typography:

• @import url(https://fonts.googleapis.com/...) → extract font names from URL path
• CSS font-family on h1, h2, body, .headline
• If Google Fonts URL contains family=Inter:wght@..., heading_font = "Inter"

Voice: Analyze hero headline, subheadline, About page intro, and CTA button text. Score each axis 1-10 using these heuristics:

Signal	Score direction
Uses "you/your" frequently	formal_casual → casual (+2)
Uses technical jargon	expert_accessible → expert (-2)
Short punchy sentences (≤8 words)	bold_subtle → bold (+2)
Data/stats in hero	rational_emotional → rational (-2)
"Transform", "revolutionize", "disrupt"	traditional_innovative → innovative (+2)
Customer testimonials lead	rational_emotional → emotional (+2)
Industry awards, "trusted by X"	traditional_innovative → traditional (-1)

Confidence Scoring

Each voice axis gets a confidence rating based on signal count:

• High (3+ signals): strong evidence for axis position
• Medium (2 signals): moderate evidence, may need validation
• Low (1 signal): weak evidence, treat as estimate

Also extract structured data when available: schema.org markup, Open Graph tags (og:title, og:description, og:image), Twitter Card metadata.

Imagery style (from og:image and any visible hero image descriptions):

• Photography vs. illustration vs. flat design
• Subject matter (people, product, abstract, data)
• Composition style (clean/minimal vs. busy/editorial)

Forbidden elements (infer from brand positioning):

• Enterprise/B2B brands → add "cheesy stock photos", "consumer lifestyle imagery"
• Healthcare → add "unqualified medical claims", "before/after imagery"
• Finance → add "get rich quick imagery", "unrealistic wealth displays"
• Consumer brands → usually no forbidden elements

Step 4: Build brand-profile.json

Read ~/.claude/skills/ads/references/brand-dna-template.md for the exact schema.

Construct the JSON object following the schema precisely. Use null for any field that cannot be confidently extracted; do not guess.

Example of a low-confidence field:

"typography": {
  "heading_font": null,
  "body_font": "system-ui",
  "pairing_descriptor": "system default (Google Fonts not detected)"
}

Step 5: Write brand-profile.json

Write the JSON to ./brand-profile.json in the current working directory (where the user is running Claude Code).

If screenshots were captured successfully in Step 2b, include a screenshots field:

"screenshots": {
  "homepage": "./brand-screenshots/{domain}_homepage.png",
  "product": "./brand-screenshots/{domain}_product.png",
  "about": "./brand-screenshots/{domain}_about.png"
}

Include only the screenshots that were successfully captured. If a page was not found or errored, omit that key. Omit the screenshots field entirely if Step 2b was skipped or all captures failed.

Step 6: Confirm and Summarize

Show the user:

✓ brand-profile.json saved to ./brand-profile.json

Brand DNA Summary:
  Brand: [brand_name]
  Voice: [descriptor 1], [descriptor 2], [descriptor 3]
  Primary Color: [hex]
  Typography: [heading_font] / [body_font]
  Target: [age_range] [profession]
  Screenshots: [N captured (homepage, product, about) in ./brand-screenshots/] OR [skipped]

Run `/ads create` to generate campaign concepts from this profile.

Visual Designer Integration

The visual-designer agent uses the most relevant screenshot per concept as a style reference when generating images via banana. For example, a product-focused concept references the product page screenshot, while a brand awareness concept references the homepage or about page screenshot.

Limitations

• Sparse content: Sites with <200 words of body text produce lower-confidence profiles. Note: "Low confidence extraction; limited content available for analysis."
• Dynamic sites: JavaScript-rendered content may not be captured. Playwright is not used by default. If the site appears to be SPA/React with no static HTML, note this.
• Multi-brand enterprises: This tool creates one profile per URL. Run separately for each brand/product line.
• Dark mode sites: If body background is #333 or darker, swap background/text values.
• CSS-in-JS: Modern React sites may not have extractable CSS. Use og:image colors as fallback.

brand-profile.json Schema

{
  "schema_version": "1.0",
  "brand_name": "string",
  "website_url": "string",
  "extracted_at": "ISO-8601",
  "voice": {
    "formal_casual": 1-10,
    "rational_emotional": 1-10,
    "playful_serious": 1-10,
    "bold_subtle": 1-10,
    "traditional_innovative": 1-10,
    "expert_accessible": 1-10,
    "descriptors": ["adjective1", "adjective2", "adjective3"]
  },
  "colors": {
    "primary": "#hexcode or null",
    "secondary": ["#hex1", "#hex2"],
    "forbidden": ["#hex or color name"],
    "background": "#hexcode",
    "text": "#hexcode"
  },
  "typography": {
    "heading_font": "Font Name or null",
    "body_font": "Font Name or system-ui",
    "pairing_descriptor": "brief description"
  },
  "imagery": {
    "style": "professional photography | illustration | flat design | mixed",
    "subjects": ["subject1", "subject2"],
    "composition": "brief description",
    "forbidden": ["element1", "element2"]
  },
  "aesthetic": {
    "mood_keywords": ["keyword1", "keyword2", "keyword3"],
    "texture": "minimal | textured | mixed",
    "negative_space": "generous | moderate | dense"
  },
  "brand_values": ["value1", "value2", "value3"],
  "target_audience": {
    "age_range": "e.g. 25-45",
    "profession": "brief description",
    "pain_points": ["pain1", "pain2"],
    "aspirations": ["aspiration1", "aspiration2"]
  }
}