Visual Style Extractor

A reference image is a complete design specification — it contains every token, every component, every spacing decision. The agent's job is to read it with precision, not approximate it.

1. Pre-extraction classification

Before extracting anything, classify the reference image into one of four types:

• Screenshot of real product: extract as-is, do not infer — what you see is the spec. Highest fidelity target.
• Design mockup / wireframe: extract intent, allow interpretation of fidelity level. Values are designed, not measured.
• Mood board / inspiration: extract style direction only, not exact values. Output is a style brief, not token values.
• Partial reference (one component, one section): extract only what is visible, explicitly flag everything that needs inference for areas not shown.

State the classification explicitly before proceeding. Different classifications trigger different precision tiers. See EXTRACTION-PROTOCOL.md for precision tier definitions.

2. Color extraction

Systematically sample colors across the entire image. Work in this order:

(a) Background surfaces — identify every distinct background layer:

• Page background, card/panel background, sidebar background, modal overlay
• For each: extract the hex value from the dominant color of that region (avoid edge pixels and anti-aliasing artifacts)

(b) Text colors — identify every distinct text color used:

• Primary text, secondary/muted text, placeholder text, disabled text, link text, error text
• Map each to a semantic role — do not output a list of hex codes without semantic meaning

(c) Brand/accent colors — identify the primary and secondary accent colors:

• Used on CTAs, active nav items, highlights, badges, data visualizations
• Extract the full accent range: base, hover (usually 10–15% darker), active/pressed state

(d) Border and separator colors — subtle lines between sections, card outlines, input borders

(e) Feedback colors — success, warning, error, info variants:

• Extract only if visible; explicitly flag which feedback colors are not present in the reference

Output format: primitive color table → semantic token mapping

| Hex      | Extracted Role         | Semantic Token Name  |
|----------|------------------------|----------------------|
| #1A73E8  | Primary CTA background | color-primary        |
| #FFFFFF  | Card surface           | color-surface        |
| #5F6368  | Body text secondary    | color-text-secondary |

3. Typography extraction

For every distinct text element visible in the image:

(a) Estimate font size using known reference anchors:

• Standard button text = 14–16px → use as calibration base
• Standard body copy = 14–16px, h1 is typically 2–2.5× body
• If multiple text sizes are visible, establish the ratio between them

(b) Identify font weight from stroke width:

• Very thin strokes = 300 (Light)
• Normal strokes = 400 (Regular)
• Slightly thicker = 500–600 (Medium/SemiBold)
• Clearly bold = 700 (Bold)
• Very heavy = 800–900 (ExtraBold/Black)

(c) Identify font family from letterform characteristics:

• Geometric sans (Circular, Futura, Nunito): perfect round 'O', uniform stroke width throughout
• Humanist sans (Inter, Source Sans Pro, Lato): slight stroke variation, open 'e', natural apertures
• Transitional/Classical sans (Helvetica, Arial): more neutral, moderate x-height
• Serif: visible serifs on letter terminals
• Monospace: characters occupy equal horizontal width

(d) Map to type scale roles: Page title / H1 / H2 / H3 / Body-large / Body / Label / Caption / Badge / Code

If font cannot be identified with confidence: name the closest widely-available alternative (Google Fonts preferred) and flag the value as (inferred).

4. Spacing and layout extraction

(a) Identify the base grid unit:

• Find the smallest repeated spacing value in the design
• Most designs use 4px or 8px as base unit — verify by checking multiple spacing instances
• Cross-check: card padding, button padding, and item gaps should all be multiples of the base unit

(b) Extract component padding from visible cards, buttons, inputs:

• Button: horizontal padding separately from vertical padding
• Card: all four sides if asymmetric, or shorthand if symmetric
• Input field: padding around the text content

(c) Extract gap values:

• Between items in a list or nav
• Between cards in a grid
• Between sections on a page (section-to-section gap is always the largest)

(d) Identify the layout structure:

• Single column / two column / sidebar + main content / grid
• Fixed sidebar width (estimate from proportional analysis)
• Topbar height (estimate from proportional analysis)
• Max-width of the main content area if visible

Output format:

| Token   | Estimated Value | Evidence                                      |
|---------|-----------------|-----------------------------------------------|
| space-2 | 8px             | Button vertical padding, consistent 3 buttons |
| space-4 | 16px            | Card inner padding, consistent across 4 cards |
| space-8 | 32px            | Gap between page sections                    |

5. Component inventory

Systematically identify every UI component visible in the reference. For each component:

• Component type: Button / Input / Card / Badge / Navigation item / Table / Modal / Tab / Toggle / etc.
• Variant visible: Primary / Secondary / Ghost / Outlined / Destructive / etc.
• States visible: Default / Hover (if shown) / Active / Disabled / Error / etc.
• Border radius: Sharp (0px) / Slight (2–4px) / Moderate (6–8px) / Rounded (12–16px) / Pill (9999px)
• Shadow level: None / Subtle (0 1px 3px) / Medium (0 4px 12px) / Elevated (0 8px 24px)
• Token references: reference the extracted color and spacing tokens by name, not by raw value

6. Visual style signature

Synthesize a single paragraph "style DNA" that captures the overall visual language. This paragraph becomes the north star for all downstream generation — every generated component should match this description.

Use this template:

"The reference uses a [light / dark / mid-tone neutral] palette anchored by [primary accent description]. Typography is [geometric / humanist / transitional / serif] with [compact / balanced / generous] line-height and [tight / normal / loose] letter-spacing. Components use [sharp / slight / moderate / rounded / pill] border radius, [no / subtle / medium / elevated] shadows, and [dense / comfortable / spacious] content density. The overall aesthetic registers as [minimalist / material / glassmorphic / neumorphic / flat / illustrated / brutalist]."

7. Fidelity gap analysis

Before passing outputs downstream, explicitly state what CANNOT be extracted from this static image:

• Motion and animation behavior (not visible in a screenshot)
• Interaction states not shown (hover, focus, loading, error — if not in the image)
• Contextual data vs. placeholder content
• Components that exist in the product but are not in this specific screenshot

For each gap, state the resolution strategy:

• (a) Infer from style signature — the gap can be reasonably extrapolated from the established style DNA
• (b) Use Agentway defaults — use the standard from the relevant skill (e.g., use accessibility minimum for touch targets)
• (c) Ask user — the decision cannot be made without additional input

8. Generate the extraction manifest

Output a structured style-extraction.json that all downstream skills can consume directly:

{
  "classification": "product-screenshot",
  "extractedAt": "ISO-8601 timestamp",
  "sourceDescription": "brief description of the reference image",
  "colorTokens": {
    "primitives": {
      "blue-500": "#1A73E8",
      "blue-600": "#1557B0",
      "gray-900": "#202124",
      "gray-600": "#5F6368",
      "white": "#FFFFFF"
    },
    "semantic": {
      "color-primary": "blue-500",
      "color-primary-hover": "blue-600",
      "color-surface": "white",
      "color-text-primary": "gray-900",
      "color-text-secondary": "gray-600"
    }
  },
  "typographyTokens": {
    "fontFamily": {
      "heading": "Inter (inferred — humanist sans characteristics)",
      "body": "Inter (inferred)"
    },
    "scale": {
      "display": "40px / weight 700",
      "h1": "32px / weight 700",
      "h2": "24px / weight 600",
      "h3": "20px / weight 600",
      "body-lg": "18px / weight 400",
      "body": "16px / weight 400",
      "label": "14px / weight 500",
      "caption": "12px / weight 400"
    }
  },
  "spacingTokens": {
    "baseUnit": "8px",
    "scale": {
      "space-1": "4px",
      "space-2": "8px",
      "space-3": "12px",
      "space-4": "16px",
      "space-6": "24px",
      "space-8": "32px",
      "space-12": "48px"
    }
  },
  "components": [
    {
      "type": "Button",
      "variant": "Primary",
      "radius": "6px",
      "shadow": "none",
      "paddingH": "16px",
      "paddingV": "8px",
      "colorBackground": "color-primary",
      "colorText": "white"
    }
  ],
  "layoutStructure": {
    "type": "sidebar-left",
    "sidebarWidth": "240px (estimated)",
    "topbarHeight": "56px (estimated)",
    "contentMaxWidth": "1200px (estimated)"
  },
  "styleDNA": "The reference uses a light palette anchored by a blue primary accent (#1A73E8). Typography is humanist sans-serif with comfortable line-height. Components use moderate border radius (6px), no shadows, and comfortable content density. The overall aesthetic registers as clean and minimalist.",
  "gaps": [
    { "item": "hover states", "resolution": "infer from style signature — primary darkens by ~15%" },
    { "item": "mobile layout", "resolution": "ask user" },
    { "item": "error states", "resolution": "use Agentway defaults from error-boundary-resilience-design" }
  ]
}

Downstream Integration

After extraction, the manifest feeds directly into downstream skills — they must use it instead of starting from scratch:

Mandatory rule: When style-extraction.json is available, no downstream skill may guess or assume style values. All style decisions must be traceable to an extracted value or a documented gap resolution.

Completion Criteria

• Image classified into one of four types, stated explicitly
• All visible background surfaces extracted with semantic token names
• All visible text colors extracted and mapped to semantic roles
• Brand/accent color range extracted (base + hover state)
• Font family identified with letterform evidence, or flagged as inference
• Font size scale estimated with calibration anchor named
• Font weights identified for all text elements
• Base grid unit identified with cross-check evidence
• Component padding and gap values extracted with evidence
• Layout structure identified (sidebar width, topbar height if present)
• All visible components inventoried with type, variant, radius, and shadow
• Style DNA paragraph written
• Gap analysis complete with resolution strategy for each gap
• style-extraction.json generated and valid JSON
• Downstream skills notified that manifest is available

Anti-patterns

• Approximating colors ("it looks like a blue") instead of extracting the hex value
• Skipping classification — treating a mood board as a screenshot produces wrong precision targets
• Extracting raw hex values without mapping them to semantic token names
• Claiming "font is Inter" without naming the letterform characteristics that support that identification
• Not flagging inferences — every estimated value that is not directly measurable must be marked (inferred)
• Passing gaps downstream silently — unresolved gaps must be documented with a resolution strategy
• Extracting only what is easy (colors) and skipping what is harder (spacing, component structure)