# How to Generate Structured JSON Prompts for GPT-Image2 Agents

> Learn to generate structured JSON prompts for GPT-Image2 agents. Gain programmatic control over image style, layout, and constraints for enhanced generation.

- Repository: [苍何/awesome-gpt-image-2](https://github.com/freestylefly/awesome-gpt-image-2)
- Tags: how-to-guide
- Published: 2026-09-06

---

**Structured JSON prompts treat image generation instructions as executable code rather than ambiguous text, enabling programmatic control over style, layout, and constraints.**

The GPT-Image2 ecosystem in the `freestylefly/awesome-gpt-image-2` repository defines prompts as **JSON objects** that agents consume directly. This architecture eliminates the guesswork of natural language descriptions and makes automation pipelines, CI/CD workflows, and bot integrations predictable and reproducible.

---

## Understanding the Three-Layer Architecture

The repository organizes prompt generation into three cooperating layers:

- **Prompt Templates** — Domain-specific JSON schemas for every visual category
- **Style Library** — A canonical database of 500+ style definitions
- **Agent Skill** — An NPM package that assembles complete prompts programmatically

Each layer builds upon the previous, allowing you to work at the abstraction level that matches your use case.

---

## Step 1: Select a Template from [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md)

Templates are organized by visual domain. Each entry provides both a human-readable explanation and a machine-parseable JSON skeleton.

Available categories include:
- UI & Interfaces
- Infographic & Information Visualization
- Posters & Marketing Materials
- Product Photography
- Architecture & Interiors
- Photography Styles
- Illustration & Art
- Character Design
- Scene & Environment
- Historical Reconstruction
- Document & Data Visualization

The JSON templates use descriptive placeholder keys like `[product]`, `[platform]`, and `[audience]` that you replace with concrete values.

### UI Screenshot Template Example

```json
{
  "type": "UI Screenshot",
  "platform": "iOS",
  "product": "Fitness App",
  "layout": "Card‑based feed with bottom tab bar",
  "style": {
    "theme": "Dark Mode",
    "primary_color": "Neon Green",
    "typography": "Clean sans‑serif"
  },
  "content": {
    "header": "Today's Activity",
    "cards": [
      { "title": "Running",   "data": "5.2 km", "button": "Start" },
      { "title": "Calories",  "data": "340 kcal" }
    ]
  },
  "constraints": "High fidelity, readable text, 9:16 aspect ratio"
}

```

*Source: [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md), UI & Interfaces section [templates.md†L24-L46]*

---

## Step 2: Apply Style Definitions from [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json)

The [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) file contains a curated collection of style entries, each with:

- Unique identifier
- Descriptive keywords
- Color palette specifications
- Layout hints and aesthetic guidance

You can reference styles directly by ID or let the skill match based on your high-level description. The flat JSON structure makes programmatic lookup straightforward in any language.

---

## Step 3: Generate Complete Prompts with the Agent Skill

The `gpt-image-2-style-library` skill automates template selection, style matching, and JSON assembly. Located at [`agents/skills/gpt-image-2-style-library/SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/skills/gpt-image-2-style-library/SKILL.md), this NPM package exposes a CLI interface for headless operation.

### CLI Usage

```bash

# Generate an infographic prompt automatically

npx gpt-image-2-style-library generate \
  --type infographic \
  --topic "Urban Metabolism" \
  --audience "General Public"

```

The skill performs three operations:

1. Retrieves the matching template structure from [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md)
2. Queries [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) for appropriate style entries
3. Merges user variables, style data, and constraints into a single valid JSON object

### Infographic Output Example

```json
{
  "type": "Infographic",
  "topic": "Urban Metabolism",
  "audience": "General Public",
  "structure": {
    "title_area": "城市生命系统图谱",
    "layout": "Isometric cutaway, 12 numbered panels",
    "modules": [
      { "title": "能源", "icon": "lightning", "text": "Power flows" },
      { "title": "水循环", "icon": "water_drop", "text": "Water flows" }
    ]
  },
  "style": {
    "aesthetic": "Scientific atlas",
    "colors": "Low saturation, color‑coded flows",
    "background": "Light paper texture"
  },
  "constraints": "No cyber‑punk, no gibberish text, strict structural layout"
}

```

*Source: [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md), Infographic section [templates.md†L7-L29]*

---

## JSON Schema Design for Validation

The structured JSON prompts use a consistent top-level schema that enables early validation:

| Key | Purpose | Example Values |
|-----|---------|--------------|
| `type` | Visual category discriminator | `"UI Screenshot"`, `"Infographic"` |
| `platform` / `topic` / `product` | Subject identifier | `"iOS"`, `"Urban Metabolism"` |
| `layout` / `structure` | Spatial composition rules | `"Card‑based feed"`, `"Isometric cutaway"` |
| `style` | Aesthetic parameters from style library | Nested object with colors, typography |
| `content` | Data payload for image elements | Arrays of cards, modules, or fields |
| `constraints` | Quality and exclusion directives | Aspect ratios, "no text garble" |

Because every key has explicit semantics, downstream services in [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js) can reject malformed requests before invoking expensive image generation APIs like APIMart or HiAPI.

---

## Deploying to Generation Endpoints

The final JSON payload routes through [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js), which validates the structure and forwards to configured backends. The endpoint accepts the complete prompt object and handles provider-specific authentication and rate limiting.

To integrate into automation pipelines, structure your generation workflow as:

```bash
1. Define variables (product, topic, audience)
2. Call skill CLI or construct JSON manually
3. POST to /api/generate-image.js with validated payload
4. Handle response (image URL or error details)

```

---

## Summary

- **JSON prompts are code**: The GPT-Image2 ecosystem treats generation instructions as structured data, not free text
- **Templates in [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md)** provide domain-specific schemas for every visual category
- **[`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json)** supplies 500+ canonical style definitions for consistent aesthetics
- **`gpt-image-2-style-library` skill** automates assembly: install via NPM and call the CLI for headless operation
- **Flat, self-describing schema** enables validation at [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js) before expensive API calls

---

## Frequently Asked Questions

### How do I extend the JSON prompt schema for custom use cases?

Modify the template structure in [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md) following the existing key naming conventions. Add your custom keys under `content` or introduce new top-level fields alongside `constraints`. The [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js) handler passes through unknown keys, so backend providers can implement extensions without breaking existing clients.

### Can I use the style library without the NPM skill?

Yes. [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) is plain JSON—parse it directly in Python, TypeScript, or any language. Query by `id` or filter on `keywords` to retrieve matching entries, then manually merge the style object into your prompt under the `style` key.

### What validation occurs at the API endpoint?

[`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js) checks for required top-level keys (`type`, `content`) and validates that `style` contains recognized entries when strict mode is enabled. Missing or malformed fields return 400 errors with descriptive messages, preventing wasted API calls to image generation services.

### How do I prevent "text garble" in generated images?

Include explicit constraints in the `constraints` field: `"readable text only"`, `"no gibberish characters"`, or `"verified font rendering"`. The templates in [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md) demonstrate proven constraint phrasing that correlates with higher text accuracy in GPT-Image2 outputs.