# How to Use GPT-Image2 Prompt Templates for Automation: A Developer’s Guide

> Automate image generation with GPT-Image2 prompt templates. This guide shows developers how to leverage structured JSON prompts for programmatic control and efficient automation. Explore the template library.

- Repository: [苍何/awesome-gpt-image-2](https://github.com/freestylefly/awesome-gpt-image-2)
- Tags: tutorial
- Published: 2026-08-26

---

**GPT-Image2 ships a machine-readable structured prompt library in** **[`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md)** **that separates prompts into discrete JSON fields, enabling you to programmatically fill parameters, validate structure, and automate image generation via the** **[`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js)** **endpoint or generated agent skills.**

The `freestylefly/awesome-gpt-image-2` repository provides an industrial-grade automation framework for AI image generation. By treating prompts as structured data rather than raw strings, you gain version-controlled templates, type-safe parameter filling, and direct integration with LLM agents like Claude and Cursor.

## Understanding the Structured Template Architecture

At the core of the automation system lies [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md), which defines every prompt as both human-readable plain text and a **JSON schema**. For example, the UI Screenshot template (starting at line 24) provides a structured breakdown of fields including `type`, `platform`, `product`, `layout`, `style`, `content`, and `constraints`.

This dual-format design serves a critical automation purpose: while humans read the Markdown documentation, machines parse the JSON schema to construct valid prompts programmatically. The JSON version eliminates manual string concatenation errors and ensures every required field is populated before reaching the model.

## Loading Templates from the Style Library

Before filling parameters, your automation script must locate the correct template schema. The repository indexes all templates in [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json), which maps template anchors (like `tpl-ui` or `tpl-infographic`) to their respective schema definitions.

```javascript
import styleLibrary from '../data/style-library.json';

// Select the UI Screenshot template by anchor
const uiTemplate = styleLibrary.templates.find(t => t.anchor === 'tpl-ui');

// Access the base JSON schema
const baseSchema = uiTemplate.json;

```

By referencing the style library, your automation pipeline always uses the latest approved template versions tracked in Git, ensuring consistency across development environments.

## Programmatically Filling Template Fields

Once loaded, the JSON schema acts as a type-safe skeleton for your prompt. You populate discrete fields rather than concatenating strings, which enables validation and reusability.

```javascript
const filled = {
  ...uiTemplate.json,                     // Base schema from docs/templates.md
  platform: 'Web',
  product: 'Dashboard',
  layout: 'Sidebar + Main panel',
  style: {
    theme: 'Light',
    primary_color: '#0066FF',
    typography: 'Roboto'
  },
  content: {
    header: 'Analytics Overview',
    cards: [
      { title: 'Visitors', data: '12k' },
      { title: 'Conversions', data: '3%' }
    ]
  },
  constraints: 'High fidelity, 16:9 aspect ratio'
};

```

This approach enforces **predictable structure**—every field is validated before it reaches the generation API, preventing malformed prompts that waste compute resources.

## Converting JSON to API-Ready Text Prompts

The [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js) endpoint (lines 26-38) expects a plain-text `prompt` payload in the POST body. To bridge the gap between structured JSON and the raw text API, implement a conversion function that flattens your object into the specific format the model expects.

```javascript
function jsonToPrompt(obj) {
  return `
Type: ${obj.type}
Platform: ${obj.platform}
Product: ${obj.product}
Layout: ${obj.layout}
Theme: ${obj.style.theme}
Primary colour: ${obj.style.primary_color}
Typography: ${obj.style.typography}
Header: ${obj.content.header}
Cards: ${obj.content.cards.map(c => c.title + ':' + c.data).join(', ')}
Constraints: ${obj.constraints}
`.trim();
}

const promptText = jsonToPrompt(filled);

```

This transformation step maintains the separation between your structured data (which lives in version control) and the runtime API requirements.

## Executing the Image Generation

With the prompt text constructed, send the payload to the generation endpoint. The handler in [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js) forwards the request to the underlying image service and returns the resulting URL.

```javascript
fetch('/api/generate-image', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ 
    prompt: promptText, 
    caseId: 123 
  })
})
.then(r => r.json())
.then(res => {
  if (res.ok) {
    console.log('✅ Image URL:', res.image);
  } else {
    console.error('❌ Generation error:', res.error);
  }
});

```

## Generating Reusable Agent Skills

For teams using Claude, Cursor, or other LLM-as-tool frameworks, the repository includes `scripts/generate-style-skill.mjs` (lines 7-13). This utility reads [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md) and automatically generates a skill package exposing each template as a callable function.

```bash
node scripts/generate-style-skill.mjs

```

The resulting skill allows direct function calls like `uiScreenshot({platform: 'iOS', product: 'Fitness App'})` without manually handling JSON conversion. This creates **plug-and-play skills** that non-technical team members can invoke through natural language while the automation handles the underlying API structure.

## Summary

- **Structured templates** in [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md) provide JSON schemas that validate prompt fields before generation.
- **Version-controlled schemas** via [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) ensure automation pipelines always use approved template versions.
- **Programmatic filling** of discrete fields (platform, style, content) eliminates string concatenation errors.
- **Runtime conversion** functions bridge JSON structures and the plain-text `prompt` parameter expected by [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js).
- **Skill generation** via `scripts/generate-style-skill.mjs` exposes templates as callable functions for LLM agents.

## Frequently Asked Questions

### How does the JSON schema prevent prompt errors?

The JSON schema in [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md) defines required fields and data types for each template. When your automation script populates these fields programmatically, you can validate the structure before sending it to [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js), ensuring the model receives complete prompts without missing constraints or malformed content descriptors.

### Can I use these templates with LLM agents like Claude or Cursor?

Yes. Run `scripts/generate-style-skill.mjs` to generate a skill package that exposes each template as a typed function. The generated skill (lines 7-13 of the script) handles the JSON-to-text conversion internally, allowing agents to call specific templates directly with natural language parameters.

### What is the difference between the style library and the templates file?

The [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) serves as an index that maps template anchors (like `tpl-ui`) to their source locations, while [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md) contains the actual JSON schemas and human-readable documentation. Your automation should query the library to locate templates, then reference the Markdown file for the full schema definition.

### Does the API accept JSON directly or require text conversion?

According to the source code in [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js) (lines 26-38), the endpoint accepts a plain-text `prompt` field in the POST body. You must convert your filled JSON template to a formatted string using a function like `jsonToPrompt()` before sending the request, as the underlying service expects structured natural language rather than raw JSON.