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Concept To Image

Community
Mathews-Tom
concept-to-image

Turn concepts into static HTML visuals exported as PNG or SVG files via HTML/CSS/SVG. Triggers on: "create an image of", "export as PNG", "save as SVG", "concept to image", "screenshot this HTML". NOT for interactive HTML, use static-web-artifacts-builder.

Overview

PublisherMathews-Tom
Repositoryarmory
Skill nameconcept-to-image
Stars
318
Forks
47
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Concept To Image AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/concept-to-image .claude/skills/concept-to-image
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Concept To Image in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Concept To Image on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Concept To Image is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Concept to Image

Creates polished visuals from concepts using HTML/CSS/SVG as a refineable intermediate, then exports to PNG or SVG.

Reference Files

FilePurpose
references/design-guide.mdDesign patterns, anti-patterns, color palettes, typography choices, layout examples
scripts/render_to_image.pyPlaywright-based export script — takes HTML in, PNG or SVG out
assets/template.htmlBase HTML template with .canvas container and CSS custom properties pre-configured

Why HTML as intermediate

HTML is the refineable layer between idea and image. Unlike direct canvas rendering, the user can see the HTML artifact, request changes ("make the title bigger", "swap the colors", "add a third column"), and only export once satisfied. This makes the workflow iterative and controllable.

Workflow

text
Concept → HTML artifact (view + refine) → PNG or SVG export
  1. Interpret the user's concept — determine what kind of visual best fits (diagram, infographic, card, chart, etc.)
  2. Design a self-contained HTML file using inline CSS and inline SVG — zero external dependencies
  3. Present the HTML as an artifact so the user can preview and request refinements
  4. Iterate on the HTML based on user feedback (colors, layout, content, sizing)
  5. Export to PNG and/or SVG when the user is satisfied, using scripts/render_to_image.py

Step 1: Interpret the concept

Determine the best visual format:

User intentVisual formatApproach
Explain a process/flowFlowchart or pipeline diagramSVG paths + boxes
Compare itemsSide-by-side or matrixCSS Grid
Show hierarchyTree or layered diagramNested containers + SVG connectors
Present dataChart or infographicSVG shapes + data labels
Social/marketing graphicCard or posterTypography-forward HTML/CSS
Icon, logo, badgeCompact symbolPure SVG
Educational conceptAnnotated diagramSVG + positioned labels

Step 2: Design the HTML

Read references/design-guide.md for detailed design patterns and anti-patterns.

Core rules:

  • Single file, self-contained: All CSS inline in <style>, all graphics as inline <svg>. No external resources.
  • Fixed viewport: Set explicit width and height on the root container matching the intended export size. This is critical — Playwright screenshots the element at this exact size.
  • Anti-AI-slop: Avoid centered-everything layouts, purple gradients, uniform rounded corners, and Inter/system font defaults. See design guide for alternatives.
  • SVG-first for shapes: Use inline SVG for icons, connectors, shapes, and any element that should scale cleanly. CSS for layout and typography.
  • Color with intention: 3-4 hues max + neutrals. Define as CSS custom properties. Every color encodes meaning.
  • Start from the template: Use assets/template.html as the base structure.

Sizing guidelines

Use caseRecommended size
Social media graphic1200×630
Infographic (portrait)800×1200
Presentation slide1920×1080
Square post1080×1080
Icon/badge256×256 or 512×512
Wide diagram1600×900

Set the .canvas container to the chosen size. The export script captures this element.

Step 3: Present and iterate

Present the HTML file to the user. They'll see it rendered as an artifact. Common refinement requests:

  • Color/theme changes → update CSS custom properties
  • Layout adjustments → modify grid/flexbox
  • Content changes → edit text/SVG elements
  • Size changes → update .canvas dimensions

Each iteration is a quick HTML edit, not a full re-render. This is the key advantage over direct image generation.

Step 4: Export to image

Once the user is satisfied, run the export script:

bash
python3 scripts/render_to_image.py <input.html> <output.png|.svg> [--width 1200] [--height 630] [--scale 2] [--selector ".canvas"]

Parameters

ParamDefaultDescription
input(required)Path to HTML file
output(required)Output path. Extension determines format (.png or .svg)
--widthautoViewport width (overrides HTML-defined size)
--heightautoViewport height (overrides HTML-defined size)
--scale2Device scale factor for PNG (2 = retina quality)
--selector.canvasCSS selector for the element to capture
--full-pagefalseCapture the full page instead of a specific element

PNG export

Uses Playwright to launch headless Chromium and screenshot the .canvas element at the specified scale factor. Scale 2 produces retina-quality output (e.g., 1200×630 CSS pixels → 2400×1260 PNG ≈ 3.02 MP).

Opus 4.7 feedback loop: Retina-scale exports (e.g., 2400×1260) fit within Opus 4.7's ~3.75 MP vision ceiling, so you can feed the rendered PNG back to the model for iterate-by-critique loops without downsampling. Under Opus 4.6 these outputs would have been downscaled on ingestion.

SVG export

Two strategies, chosen automatically:

  1. SVG-native content: If the .canvas element contains a single root <svg>, extracts it directly as a clean SVG file. This produces a true vector SVG.
  2. HTML-based content: If the content is CSS/HTML-heavy, falls back to PNG export with a note that true SVG requires SVG-native design. The script will warn and suggest redesigning with SVG elements if vector output is needed.

Delivering the output

Present the output file to the user. Always deliver both the HTML (for future editing) and the image (final output).

Error Handling

ErrorCauseResolution
playwright not foundPlaywright package not installedRun npx playwright install chromium or pip install playwright && playwright install chromium
Browser launch failureHeadless Chromium fails to startVerify --headless mode is supported; check available memory (Chromium needs ~200 MB)
.canvas selector not foundHTML does not contain an element matching .canvasVerify assets/template.html was used as the base; check the root container has class="canvas"
Render timeoutComplex HTML takes too long to render before screenshotIncrease the timeout via --timeout flag in the script, or simplify the HTML (reduce DOM depth, inline fewer SVGs)
SVG export falls back to PNG.canvas element contains HTML/CSS content, not a root SVGSee SVG export section; redesign with a single root <svg> if vector output is required

Limitations

  • Playwright + Chromium required — the export script cannot run without a working Chromium installation.
  • macOS and Linux only for headless browser export. Windows Subsystem for Linux works; native Windows Playwright may require separate setup.
  • SVG export is best-effort — complex HTML/CSS layouts fall back to PNG. True vector SVG requires a single root <svg> as the .canvas child.
  • Max viewport 4096×4096 — Chromium refuses screenshots larger than this. Use --scale to achieve higher effective resolution within this limit.
  • No animation support — exported images are static snapshots. CSS animations and JavaScript-driven transitions are frozen at their initial state.

Output Example

After a successful export, the script prints the output path and file stats:

text
Exported: concept-diagram.png
  Size:       2400 × 1260 px  (2× scale from 1200 × 630 canvas)
  File size:  ~180 KB
  Format:     PNG (RGBA)

Filename pattern follows whatever was passed as the output argument. Typical file sizes:

  • Simple diagrams (text + shapes): 80–200 KB
  • Dense infographics with gradients: 300–600 KB
  • Full 1920×1080 at 2× scale: 500 KB–1.5 MB

Design anti-patterns to avoid

These produce generic "AI-generated" looking output:

  • Centered everything with equal spacing
  • Purple/blue gradient backgrounds
  • Uniform border-radius on all elements
  • Generic icon libraries (use custom inline SVG)
  • System font stack without typographic intention
  • Drop shadows on everything
  • Low information density (too much whitespace)

Font handling

Since this environment has limited font access, use web-safe font stacks with intentional fallbacks:

  • Technical/mono: 'Courier New', 'Consolas', monospace
  • Clean sans: 'Helvetica Neue', 'Arial', sans-serif
  • Editorial serif: 'Georgia', 'Times New Roman', serif
  • Display: Use SVG text with custom paths for display typography when needed

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Concept To Image AI skill do?

Turn concepts into static HTML visuals exported as PNG or SVG files via HTML/CSS/SVG. Triggers on: "create an image of", "export as PNG", "save as SVG", "concept to image", "screenshot this HTML". NOT for interactive HTML, use static-web-artifacts-builder.

Why use Concept To Image on TypingMind?

Because you install it once and use it with any model. Concept To Image is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Concept To Image in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/concept-to-image. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Concept To Image?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Concept To Image?

As many as you like. As long as a model supports skills, you can use Concept To Image with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Concept To Image AI skill free?

Yes. It is published on GitHub by Mathews-Tom under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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