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Source: YoMii (@Gas1688) highlighted freestylefly/awesome-gpt-image-2, an open-source collection of reverse-engineered image prompts.

Text-to-image prompts are often paragraphs filled with modifiers such as "hyperrealistic, cinematic lighting, 8k octanerender". The repository takes a different approach to GPT-Image-2: it records layout, palette, typography, camera, and lighting choices as explicit fields that an agent can reuse.

The project calls this Prompt as Code (PaC). A structured prompt can be versioned and edited like configuration, although the model output remains probabilistic rather than deterministic.

Where prose prompts lose precision

When a coding agent such as Claude Code, Antigravity, or Cursor needs a dashboard mockup, product render, or infographic, a loose prose prompt can leave important constraints implicit:

  • Layout drift: Spatial hierarchy, grids, and padding can change between generations.
  • Typography errors: Text may render as incorrect glyphs, especially when labels and placement are underspecified.
  • Style spillover: An adjective intended for one object can affect another, such as a neon-blue button changing the appearance of a dark walnut table.

Prose and structured prompt examples

The JSON below is my illustrative schema, not a schema copied from or versioned by the repository.

// Unstructured prose
"Create a futuristic cyberpunk analytics dashboard with dark theme and neon blue graphs and cards."

// Prompt as Code (explicit, modular constraints)
{
  "schema_version": "2.1.0",
  "domain": "UI/Dashboard",
  "theme": {
    "palette": ["#0c0c0e", "#18181b", "#3b82f6", "#10b981"],
    "glassmorphism": { "blur": "12px", "border_opacity": 0.15 }
  },
  "layout": {
    "grid": "12-column-responsive",
    "hierarchy": ["TopNav", "KPI_Cards_4x", "MainAreaChart", "ActivityFeed"]
  },
  "typography": {
    "headers": "Space Grotesk Bold",
    "data_points": "JetBrains Mono SemiBold",
    "strict_labels": ["Active Nodes", "Throughput (GB/s)", "Error Rate 0.02%"]
  },
  "lighting": "Subtle top-down ambient with 45deg soft specular on glass cards"
}

Fields used in the prompt schemas

The repository repeatedly specifies subject, camera, lighting, and material. I read these as useful editing handles, not a universal protocol or a guarantee of repeatable output.

Subject Entity, pose, silhouette, and state
Camera Focal length (e.g. 50mm, 85mm), aperture, angle (isometric, macro, 3/4 top-down)
Lighting Color temperature, falloff, and rim light
Surface Roughness, reflection, translucency, and grain

Cases by category

The repository groups its examples by intended use. The table below summarizes five of those groups and the controls they emphasize:

Domain Category Examples in the repository Key Control Mechanics Primary Production Use Case
🧩 UI & Interfaces Dashboards and app mockups Component bounding boxes, glassmorphism, responsive grid layout Landing page hero mockups, mobile app previews, SaaS dashboards
📊 Charts & Infographics Charts and technical diagrams Node-edge graphs, data density hierarchies, color-coded flows Technical explainers, research whitepapers, architecture diagrams
📰 Posters & Typography Posters and editorial layouts Swiss grid alignment, kerning anchors, editorial headline ratios Event keynotes, newsletter covers, brand campaign posters
🛍️ Products & Hardware Product and packaging renders Studio cyclorama lighting, macro lens depth, CNC/anodized aluminum shaders E-commerce hero banners, industrial hardware mockups, packaging
🏛️ Architecture & Spatial Architecture and interior studies Two-point perspective, brutalist concrete texture, architectural blue hour Spatial concept art, city mapping, interior mood boards

Using the repository as an agent skill

The repository also packages the template library as an Agent Skill (agents/skills/gpt-image-2-style-library/SKILL.md).

For a frontend task, an agent such as Antigravity or Claude Code can use the skill to select a template and construct a more explicit request:

  1. Skill discovery: The agent reads SKILL.md and matches the request to a template, such as a dark minimalist telemetry dashboard.
  2. Schema assembly: It fills a JSON or YAML payload with color tokens, aspect ratio, and typography specifications.
  3. Generation: It sends the payload to an asynchronous endpoint through Remote MCP or a REST API.
  4. Review: It inspects the result, checks the requested layout, and then embeds the asset in the application.

How I would use it

I would use the collection as a reference library, then keep the chosen prompt beside the code that consumes it. That makes revisions reviewable without pretending that image generation is deterministic.

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