UI Screenshot Prompt Template in awesome-gpt-image-2: A Complete Implementation Guide

The UI Screenshot prompt template in awesome-gpt-image-2 provides both human-readable text blocks and structured JSON definitions in docs/templates.md to generate high-fidelity interface mockups via the /api/generate-image endpoint.

The awesome-gpt-image-2 repository ships with industrial-level prompt templates designed for generating consistent, high-quality AI images. The UI Screenshot prompt template serves as a core component for creating realistic interface mockups, offering both copy-paste text and machine-readable JSON formats that integrate seamlessly with the style library and generation API.

UI Screenshot Prompt Template Definition in docs/templates.md

The UI Screenshot template lives in docs/templates.md, where it is defined in two complementary representations to serve different use cases.

Human-Readable Text Format

For quick manual use, the template includes a human-readable text block that can be copied directly into chat interfaces. This format provides a natural language description of the requested UI elements.

为Fitness App生成一张iOS界面图。
核心功能：跑步、卡路里统计、社交分享。
视觉风格：极简、暗色主题、主色 neon green。
布局：卡片式信息流，底部标签栏。
输出：高保真UI截图，文字清晰可读，比例9:16。

Structured JSON Format

For programmatic use, the template provides a machine-readable JSON object stored at lines 26-46 of docs/templates.md. This format includes explicit keys that allow agents to fill values programmatically without ambiguity.

{
  "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"
}

System Integration Architecture

The UI Screenshot template integrates with the broader awesome-gpt-image-2 ecosystem through three key components that handle registration, API consumption, and agent automation.

Style Library Registration

The template is exposed to the front-end via the ui-screenshot-system entry in data/style-library.json. This file drives the style selector in the user interface, allowing users to pick "UI Screenshot System" and automatically load the JSON skeleton. The entry includes guidance and pitfalls fields to help users avoid common mistakes.

API Endpoint Consumption

When clients send generation requests to /api/generate-image (implemented in api/generate-image.js), the request payload includes a prompt field. The back-end treats JSON templates as standard prompts, enforcing only the APIMART_MAX_PROMPT_LENGTH limit without requiring special handling. This keeps the UI Screenshot template agnostic to the underlying generation engine.

Agent Skill Automation

The repository includes an agent skill at agents/skills/gpt-image-2-style-library that can automatically populate the UI Screenshot template. This skill reads the template definition and fills in fields based on user intent before invoking the generation endpoint, enabling automated workflows.

Practical Implementation Examples

Developers can leverage the UI Screenshot template through multiple approaches depending on their technical requirements.

Copy-Paste Text Prompt

For manual generation workflows, use the human-readable format from docs/templates.md:

为Fitness App生成一张iOS界面图。
核心功能：跑步、卡路里统计、社交分享。
视觉风格：极简、暗色主题、主色 neon green。
布局：卡片式信息流，底部标签栏。
输出：高保真UI截图，文字清晰可读，比例9:16。

Programmatic JSON Construction

For applications building prompts dynamically, construct the JSON object with the required schema:

{
  "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"
}

Node.js API Integration

To send the template to the generation endpoint, serialize the JSON and POST it to /api/generate-image:

import fetch from 'node-fetch';

const prompt = /* JSON string from template */;
const response = await fetch('https://your-app.com/api/generate-image', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ prompt, caseId: 17, language: 'en' })
});

const result = await response.json();
console.log(result);

The endpoint enforces APIMART_MAX_PROMPT_LENGTH but processes the UI Screenshot template identically to standard text prompts.

Summary

• The UI Screenshot prompt template is defined in docs/templates.md with both human-readable text and structured JSON formats.
• JSON templates include specific keys (type, platform, product, layout, style, content, constraints) for programmatic manipulation.
• The style library at data/style-library.json exposes the template to the front-end via the ui-screenshot-system entry.
• The API endpoint at api/generate-image.js accepts JSON templates without special handling, only enforcing length limits via APIMART_MAX_PROMPT_LENGTH.
• Agent skills at agents/skills/gpt-image-2-style-library can auto-populate template fields for automated workflows.

Frequently Asked Questions

Where is the UI Screenshot prompt template defined in awesome-gpt-image-2?

The template is defined in docs/templates.md (lines 26-46), which contains both the human-readable description and the structured JSON schema. This file serves as the central documentation for the template's structure and usage patterns.

What are the required fields in the UI Screenshot JSON template?

The JSON template requires seven top-level keys: type (set to "UI Screenshot"), platform (e.g., "iOS"), product (application name), layout (structural description), style (object containing theme and color), content (object with UI elements), and constraints (quality specifications like "9:16 aspect ratio").

How does the front-end use the UI Screenshot template?

The front-end accesses the template through the ui-screenshot-system entry in data/style-library.json. When users select "UI Screenshot System" from the style selector, the interface automatically loads the JSON skeleton from this registry, allowing users to fill in specific values before generation.

Can the UI Screenshot template be used with automated agents?

Yes. The repository includes an agent skill at agents/skills/gpt-image-2-style-library that programmatically populates the template fields based on user intent. This skill reads the template definition from docs/templates.md and constructs the final prompt before calling the /api/generate-image endpoint.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}