infographics-generate-style

name	infographics-generate-style
description	Generates a new infographic style definition from reference material (images, PPTX files, URLs, or verbal descriptions). Analyzes the source, extracts design DNA, and writes a style markdown file. Use when user asks to "create a style", "new infographic style", "generate style", "add a style from", or "extract style from".

Infographic Style Generator

Analyzes source material — PPTX files, images, URLs, or verbal descriptions — and produces a structured style definition that the LLM-based infographic generation pipeline can use.

Usage

/infographics-generate-style path/to/reference.pptx
/infographics-generate-style path/to/screenshot.png
/infographics-generate-style https://example.com/design-page
/infographics-generate-style "dark tech aesthetic with neon accents"

Source: $ARGUMENTS

---

Pre-Flight Gate

Before investing time in style generation, answer these 3 questions. If any answer is "no", stop and tell the user why.

1. Is this a visual style, not a rendering medium? Mediums (chalkboard, claymation, craft-handmade) describe how something is rendered but lack a distinctive visual vocabulary — they produce generic images "rendered as chalk." A style has specific colors, motifs, compositional rules, and recognizable visual DNA.

2. Can this style produce recognizably different images across 3+ layout types? Test mentally: would a bento-grid, a linear-progression, and a tree-branching layout each look distinct and good in this style? Styles that only work for one layout type (e.g., subway-map) are too narrow for the library.

3. Does this overlap with an existing library style? If yes, proceed only if you can write at least 5 specific Anti-Patterns differentiating the new style from the existing one. If you can't articulate the difference, the style is redundant.

---

Step 1: Analyze the Source Material

Adapt your analysis approach to the source type:

PPTX files

PPTX files are ZIP archives containing XML. Unzip to a temp directory and extract:

• Theme colors from ppt/theme/*.xml — look for <a:clrScheme> with dk1, lt1, dk2, lt2, accent1–6
• Fonts from <a:majorFont> and <a:minorFont> in theme XML
• Slide dimensions from ppt/presentation.xml — <p:sldSz> (EMUs ÷ 914400 = inches)
• Backgrounds from ppt/slideMasters/*.xml and actual slides — solid fills, gradients, image treatments
• Text styling from slide XML — font sizes (hundredths of a point), weights, spacing, alignment
• Layout patterns from ppt/slideLayouts/*.xml — names and structure of content arrangements
• Recurring elements from ppt/slides/slide*.xml — panels, dividers, image treatments, repeated shapes

Images / screenshots

Read the image files directly (Claude is multimodal). Identify:

• Dominant and accent colors (estimate hex values)
• Typography characteristics (serif/sans-serif, weight, size hierarchy)
• Layout grid and compositional structure
• Recurring visual motifs and textures
• Background treatments
• Illustration style (flat, 3D, hand-drawn, photographic, etc.)

Reference images — use selectively: When the user provides images as style references, save 2-4 of the strongest/most representative ones in a references/ subdirectory next to the style file. These can be passed as --ref images during generation.

However, reference images can add noise for illustration styles. Photos from Unsplash are photographs, not illustrations — they guide palette and motifs but push the generator toward over-decoration and photorealism. Only use reference images when:

• The user provides actual artwork/illustrations in the target style
• The style is photographic or realistic in nature
• The style is from a cultural tradition where specific visual details matter and the text description alone is insufficient

For most illustration styles (comics, graphic design, ink-based, geometric), a well-written style definition with strong Anti-Patterns produces better results than adding photographic references.

URLs / websites

Fetch the page and analyze visual design. Extract same properties as images.

Verbal descriptions

Work with the user to pin down concrete visual properties. Ask clarifying questions about colors, typography feel, layout preferences, and reference examples.

---

Step 2: Understand the Target Format by Reading Existing Styles

Before writing anything, read 2-3 existing style files from backend/sumi/references/data/styles/ to calibrate on the actual format, tone, and detail level used in practice. Pick styles that are adjacent to the one you're creating — they'll also serve as Anti-Pattern targets in Step 3.

This step is critical. Writing from an abstract template alone produces inconsistent structure, mismatched section formatting, and weaker Anti-Patterns. Reading real files ensures your output matches the established library standard.

Style definitions live at: backend/sumi/references/data/styles/{style-id}.md

The filename becomes the style ID (kebab-case). The loader at backend/sumi/references/loader.py parses them automatically.

Parsed fields

The StyleRef dataclass extracts:

• id: From filename stem (e.g., adobe-slide from adobe-slide.md)
• name: From H1 heading, converted via _kebab_to_title() or _NAME_OVERRIDES dict
• best_for: Text content of the ## Best For section
• color_palette_desc: Text content of the ## Color Palette section
• content: Full markdown (passed to the LLM during generation)

Name handling

The loader converts kebab-case IDs to title case automatically (cyberpunk-neon → Cyberpunk Neon). If the style name needs special casing (e.g., acronyms, proper nouns, non-standard capitalization), add an entry to _NAME_OVERRIDES in backend/sumi/references/loader.py:

_NAME_OVERRIDES: dict[str, str] = {
    "ukiyo-e": "Ukiyo-e",
    "art-nouveau": "Art Nouveau",
    # add new override here if needed
}

---

Step 3: Write the Style Definition

Use this template. All sections are important — the full markdown is passed to the LLM during infographic generation.

CRITICAL: Write for the image generation model, not a design textbook

The style definition is a prompt that guides an AI image generator. Every description must be vivid, concrete, and visually evocative — as if you're painting a picture with words. The generation model responds to sensory language, not abstract theory.

DO — use concrete, evocative descriptions:

• "Large bold circles with luminous colored halos radiating outward — pink glow around black, blue aura around yellow"
• "Sweeping thin black lines shooting diagonally across the entire composition"
• "Warm cream parchment-toned background with subtle tonal variation — never sterile white"
• "Faces built from overlapping bold geometric shapes — eyes as concentric colored rings, noses as sharp triangles"

DON'T — use abstract design theory:

• "Pure geometric primitives as foundational shapes" (too abstract, produces flat clip-art)
• "Asymmetric but rigorously balanced compositions" (design principle, not a visual instruction)
• "Primary colors assigned to shapes per Kandinsky's theory" (academic reference, not actionable)
• "Modular, repeatable units assembled into larger structures" (systems thinking, not visual)

The difference is stark: theory-oriented descriptions produce sterile, diagrammatic output. Prompt-oriented descriptions produce expressive, artistic output. Always ask: "Can the model see what I'm describing?" If the answer requires understanding design history, rewrite it.

# {style-id}

{1-2 sentence description capturing the essence and visual identity of the style. Make it vivid — describe what you SEE, not what design movement it belongs to.}

## Color Palette

- Primary: {2-4 dominant colors with hex codes}
- Background: {Background colors/treatments with hex codes}
- Accents: {3-6 accent colors with hex codes}
- Text: {Text color rules — what color on what background}

### Palette Combinations

| Combination | Colors |
|-------------|--------|
| {Name} | {Color A + Color B + Color C + ...} |
{3-4 combinations covering different moods/contexts}

## Variants

| Variant | Focus | Visual Emphasis |
|---------|-------|-----------------|
| **{Name}** | {Use case} | {Key visual characteristics} |
{2-4 variants offering meaningful visual diversity within the style}

## Visual Elements

- {10-15 bullet points defining the visual vocabulary}
- {What shapes, textures, treatments ARE used}
- {What is explicitly NOT used — helps the LLM avoid wrong choices}
- {Specific rendering rules: stroke weights, corner radii, fills, effects}

## Compositional Patterns

| Content Structure | Composition | Reference |
|-------------------|-------------|-----------|
| Hierarchy/levels | {How this style represents hierarchy} | {Visual reference} |
| Flow/process | {How this style represents sequences} | {Visual reference} |
| Comparison | {How this style represents side-by-side analysis} | {Visual reference} |
| Categories | {How this style groups related items} | {Visual reference} |
{Map 6-10 common content structures to style-specific compositions}

## Visual Metaphor Mappings

| Abstract Concept | {Style Name} Metaphor |
|------------------|----------------------|
| Data/information | {How this style visualizes data} |
| Processes | {How this style represents steps/flows} |
| Connections | {How this style shows relationships} |
| Growth | {How this style represents increase/progress} |
| Time | {How this style represents temporal progression} |
| Hierarchy | {How this style shows importance/ranking} |
{Map 10-15 abstract concepts to concrete visual representations}

## Typography

- {Font style: serif, sans-serif, hand-drawn, monospace, etc.}
- {Weight hierarchy: what weight for headings, body, captions}
- {Size hierarchy: relative or absolute size relationships}
- {Special treatments: letter-spacing, line-height, effects}
- {Alignment rules}
- {What to avoid}

## Anti-Patterns

{THE MOST IMPORTANT SECTION. This is what prevents the generation model from producing generic output. Write at least 8 anti-patterns for styles with close siblings in the library, at least 5 for unique styles. Be aggressive, specific, and repetitive.}

- **NOT {adjacent-style-id}** — {explain the specific visual difference, e.g., "NOT bauhaus — Bauhaus uses primary colors in geometric grids; this style uses earth tones in organic flowing forms"}
- **NOT {another-adjacent-style-id}** — {explain difference}
- **NOT {most likely wrong interpretation}** — {what it looks like when the model gets it wrong}
- **NOT {sterile/corporate version}** — {describe the generic fallback to avoid}
- {Quantitative threshold}: "{maximum N shapes per figure}", "{at least N% negative space}", "{no more than N colors per composition}"
- {Repeat the single most important constraint with emphasis}: "Flat fills only. NO gradients. ZERO shading. Flat. Flat. Flat."
- {End with a grounding sentence}: "This must look like [specific real-world artifact from a specific year/place], not [generic fallback]"

## Reference Images

{If reference images were provided, list them here with brief descriptions of what each contributes to the style definition. These are passed as --ref during generation.}

- `references/{filename}` — {what this image demonstrates about the style}

## Best For

{Comma-separated list of ideal use cases — this is used by the recommendation engine to match styles to content}

Non-negotiable sections

Every style — regardless of complexity — MUST include:

• Color Palette with hex codes and Palette Combinations table
• Variants table (at least 3 variants)
• Visual Elements (at least 10 bullets)
• Anti-Patterns (at least 5, ideally 8+)
• Best For

Never skip Anti-Patterns for "simple" styles. Styles that seem self-explanatory (like "pixel art" or "chalkboard") actually need strong Anti-Patterns most, because the model's default interpretation of these terms is often too generic to produce distinctive infographics.

Optional sections (include for rich/artistic styles, skip for straightforward ones):

• Compositional Patterns table
• Visual Metaphor Mappings table

Writing effective Anti-Patterns (most impactful section)

Anti-Patterns have more impact on output quality than any other section. A style with weak Visual Elements but strong Anti-Patterns produces better images than the reverse. Follow these rules:

1. Name adjacent styles by their library ID:

• Good: "NOT bauhaus — Bauhaus uses primary colors in strict geometric grids with functional clarity. This style uses earth tones in organic curves."
• Bad: "NOT geometric" (too vague — half the library is geometric)

2. Include quantitative thresholds:

• "Maximum 6 shapes per figure — if you can count more than 10, it is TOO detailed"
• "At least 30% of the canvas must be open background color"
• "No more than 4 colors per composition excluding black and white"

3. Repeat the #1 constraint with aggressive emphasis:

• "Flat fills only. NO gradients. ZERO shading anywhere. Not even subtle gradients. Flat. Flat. Flat."
• The generation model responds to repetition — saying it once is not enough for the most critical constraint.

4. Name the most likely failure mode specifically:

• "The most common mistake is adding decorative scattered elements to fill empty space. DO NOT fill empty space. The empty space IS the design."

5. End with a grounding sentence:

• "This must look like a 1984 Keith Haring Pop Shop poster, not a generic flat illustration with thick outlines."
• Anchoring to a specific real-world artifact gives the model a concrete target.

Example — Charley Harper anti-patterns (strong):

- **NOT atomic-age/mid-century generic** — While Harper IS mid-century, his style is specifically
  about EXTREME geometric reduction of NATURE subjects. Not starbursts, not boomerangs, not Googie.
- **NOT corporate Memphis** — Memphis uses pastel humanoid figures with noodle arms. Harper uses
  bold-colored geometric animals. The reduction is toward geometry, not toward soft blobby shapes.
- **NOT clip art** — Clip art has too many curves and details. Harper animals are geometrically
  pure — a bird body is a literal triangle, not a rounded cartoon blob.
- Maximum 8 geometric shapes per animal. If you can count more than 10, reduce further.
- This must look like a mid-century wildlife serigraph print where a cardinal is literally a red
  triangle with eyes, not a generic flat illustration of a bird.

Writing effective Visual Elements

Each bullet in Visual Elements should pass the "can you see it?" test. Compare:

• Bad: "Dynamic diagonal compositions" → vague, the model doesn't know what to draw
• Good: "Sweeping thin black lines shooting diagonally across the entire canvas, creating tension between curved arcs and sharp angles" → the model can render this

Specific patterns that produce strong results:

• Describe color interactions: "pink glow radiating around black circles", not "accent colors"
• Describe surface quality: "warm cream parchment background with subtle tonal variation", not "off-white background"
• Describe how figures are built: "faces constructed from overlapping triangles and circles — eyes as concentric colored rings", not "geometric figure constructions"
• Describe what NOT to render: "never sterile white", "not clip-art", "not a flat corporate diagram"

---

Step 4: Validate

After writing the file:

1. Check the loader — confirm _kebab_to_title() produces the correct display name. If not, add to _NAME_OVERRIDES in backend/sumi/references/loader.py.
2. Review the H1 — must match the filename stem exactly (e.g., # adobe-slide in adobe-slide.md).
3. Review section headings — must use ## Color Palette, ## Best For exactly (the loader regex depends on these).
4. Read back the file to verify formatting.

Step 5: Test Generation

Generate a test infographic using the new style definition to catch issues that only surface at generation time.

1. Generate a test infographic using simple content (e.g., "5 steps to make coffee" or "3 types of renewable energy") — something with enough structure to exercise the style across headings, labels, and illustrations.
2. If the user provided reference artwork, include it as --ref images. Do NOT use generic stock photos as references — they push illustration styles toward photorealism.
3. Diagnose common failure modes:
   • Too flat/sterile: The Visual Elements section uses abstract theory instead of vivid descriptions. Rewrite with concrete, sensory language.
   • Too generic / looks like another style: The Anti-Patterns section is too weak. Add more entries that name specific adjacent styles and include quantitative thresholds.
   • Wrong color mood: The Color Palette describes colors by name only. Add emotional/sensory qualities ("warm cream parchment", "dense velvety black", "luminous colored halos").
   • Lifeless figures: People/characters described generically. Add specific construction details ("eyes as concentric colored rings, nose as sharp triangle, hair as sweeping arcs").
   • Over-decorated / too busy: Too many Visual Elements describing motifs. Reduce to fewer elements and add Anti-Patterns like "NO scattered decorative elements", "NO filling empty space".
4. Iterate — update the style definition based on test results and regenerate until the output is distinctive and recognizable.

---

Existing Styles (for reference and differentiation)

There are currently 42 styles in the library. Read 2-3 of the most adjacent ones before writing a new style — they are your primary Anti-Pattern targets.

aged-academia, airline-travel-poster, art-nouveau, art-nouveau-mucha, atomic-age, axonometric, bauhaus, bold-graphic, charley-harper, constructivism, de-stijl, dia-de-muertos, fantasy-map, futurism, golden-age-comics, googie, ikea-manual, isometric-technical, isotype, jack-kirby, kandinsky, keith-haring, knolling, ligne-claire, matsumoto, memphis, moebius, origami, osamu-tezuka, patent-drawing, pop-art-lichtenstein, renaissance-diagram, richard-scarry, saul-bass, shan-shui, storybook-watercolor, studio-ghibli, sumi-e, superflat, synthwave, tibetan-thangka, ukiyo-e

If the new style overlaps significantly with an existing one, you must either:

1. Write at least 5 specific Anti-Patterns differentiating the two, or
2. Tell the user the style is redundant and suggest the existing alternative.