Awesome-GPT-Image-2 Features: A Complete Guide to the Prompt-as-Code Ecosystem

Awesome-gpt-image-2 is an open-source repository that combines 500+ reverse-engineered prompt cases with a Vite + React gallery UI, Supabase backend, and a distributable NPM agent skill to create a complete "prompt-as-code" ecosystem for GPT-Image generation.

The awesome-gpt-image-2 repository by freestylefly transforms free-form text prompts into structured, reusable assets through atomic schema decomposition. According to the source code, this project bridges the gap between AI image generation and software engineering by treating prompts as version-controlled code components.

Prompt-as-Code Architecture with 500+ Reverse-Engineered Cases

At the heart of awesome-gpt-image-2 lies a decomposition engine that breaks prompts into atomic schema parts: subject, lighting, materials, layout, and visual details. The repository contains over 500 reverse-engineered cases that demonstrate how to consistently reproduce specific visual styles.

In /data/style-library.json, each template follows a strict schema defined in /docs/templates.md. This structure allows developers to programmatically construct prompts rather than relying on trial-and-error text generation. The "prompt-as-code" approach enables version control, automated testing, and CI/CD integration for visual assets.

Industrial Template Library with 20+ Reusable Schemas

The project ships with 20+ industrial-grade prompt templates categorized for specific use cases. These templates live in the canonical data file /data/style-library.json and cover domains including UI & Interfaces, Charts & Infographics, Products & E-commerce, and Illustration & Art.

Each template defines core capabilities and expected outputs, allowing agents to select the appropriate schema based on user intent. The templates are not static strings but parameterized objects that accept variables for subject, style modifiers, and technical specifications.

Four-Page Navigation System for Template Discovery

Templates are organized into four thematic pages that streamline discovery:

1. Design & Information - UI components, data visualizations, and infographic layouts
2. Commerce & Space - Product photography, architectural visualization, and retail environments
3. Imaging & Characters - Portrait generation, character design, and illustration workflows
4. Narrative & Extensions - Storytelling sequences, comic panels, and creative extensions

This categorization is implemented in the React frontend (/src/main.jsx) and allows users to filter the gallery by style or scenario via the Supabase-powered backend.

Agent Skill and NPM Package Integration

The repository distributes an agent skill named gpt-image-2-style-library that exposes the style library to Claude Code, Codex, Cursor, and other AI-assisted editors. The skill source code resides in /agents/skills/gpt-image-2-style-library/SKILL.md and draws from the same /data/style-library.json used by the web interface.

Install the skill globally via npm:

npm install -g gpt-image-2-style-library
gpt-image-2-style-library install all

This command copies the skill to default agent folders (~/.codex/skills, ~/.claude/skills, etc.), enabling natural language invocations like:

Use gpt-image-2-style-library to create an infographic prompt about Codex.

The build script in /scripts/generate-style-skill.mjs automates package generation from the JSON source, ensuring the skill and website always remain synchronized.

Vite + React Frontend with Supabase Backend

The visual interface at https://gpt-image2.canghe.ai is built with Vite and React, featuring a filtering UI that queries the Supabase backend for authentication, credit tracking, and user-specific favorites. The Supabase client configuration lives in /src/supabaseClient.js and manages:

• User authentication sessions
• Credit balance tracking
• Favorite case persistence
• Community QR-code handling

Environment variables such as VITE_SUPABASE_URL and VITE_SUPABASE_ANON_KEY configure the client for Vercel deployments, while APIMART_API_KEY (or HiAPI credentials) authenticate image generation requests.

APIMart Integration for Image Generation

The backend forwards generation requests through /api/generate-image.js to APIMart or HiAPI providers. This architecture separates prompt management from compute resources, allowing the open-source project to remain free while using paid API keys for actual image generation.

Call the generation endpoint directly using Node.js:

import { createClient } from '@supabase/supabase-js'

const supabase = createClient(
  process.env.VITE_SUPABASE_URL,
  process.env.VITE_SUPABASE_ANON_KEY
)

// Request image generation
await fetch('https://gpt-image2.canghe.ai/api/generate-image', {
  method: 'POST',
  headers: { 
    'Content-Type': 'application/json', 
    Authorization: `Bearer ${process.env.APIMART_API_KEY}` 
  },
  body: JSON.stringify({
    model: 'gpt-image-2-sunburst',
    prompt: 'A futuristic city skyline at sunset, ultra-realistic, 8k',
    size: '1024x1024'
  })
})

Query templates programmatically from the shared library:

import styleLib from '../data/style-library.json' assert { type: 'json' }

const illustrationTemplate = styleLib.templates.find(
  t => t.category === 'Illustration & Art'
)
console.log(illustrationTemplate.prompt)

GPT-Image 2.5 Model Support

The repository includes a spotlight section showcasing Sunburst and Flare models (GPT-Image 2.5) with side-by-side comparisons, draggable dividers, and image-enlargement tools. While the site does not perform live 2.5 generation, all existing cases, templates, and agent skills remain fully compatible with the new model versions.

Community Infrastructure and Sponsorship

The project maintains an active WeChat community and a paid tier managed via Supabase and Stripe integration. Sponsors including APIMart, HiAPI, and PackyCode support infrastructure costs, keeping the core templates and agent skills free for open-source contributors.

Automation-Ready Development Workflow

The repository includes npm scripts for generating site data, compiling the style skill, and building the Vite-powered frontend. CI workflows handle skill package publishing and APIMart integration testing, ensuring the entire ecosystem—from JSON data to React components to NPM packages—builds deterministically.

Summary

• Prompt-as-code architecture decomposes images into atomic, reusable schema components based on 500+ reverse-engineered cases.
• Industrial template library provides 20+ categorized templates in /data/style-library.json for consistent visual generation.
• Agent skill distribution via npm package gpt-image-2-style-library enables IDE integrations with Claude Code, Cursor, and Codex.
• Full-stack implementation combines a Vite + React frontend, Supabase backend for auth/credits, and APIMart integration for generation.
• GPT-Image 2.5 compatibility extends existing templates to Sunburst and Flare models with comparison tooling.

Frequently Asked Questions

What is awesome-gpt-image-2 and how does it work?

Awesome-gpt-image-2 is an open-source ecosystem that treats image prompts as structured code. It decomposes visual requirements into atomic schema parts (subject, lighting, materials) stored in /data/style-library.json, then exposes these templates through a React gallery UI and an installable agent skill for AI coding tools.

How do I install the awesome-gpt-image-2 agent skill?

Install the skill via npm using npm install -g gpt-image-2-style-library followed by gpt-image-2-style-library install all. This copies the skill to ~/.codex/skills, ~/.claude/skills, and other standard agent directories, allowing natural language invocation from supported editors.

Can I use awesome-gpt-image-2 with Claude Code or Cursor?

Yes. The repository specifically targets Claude Code, Cursor, Codex, and similar AI-assisted development environments. The skill in /agents/skills/gpt-image-2-style-library/ integrates with these tools to provide contextual prompt templates based on the industrial-grade schemas defined in the style library.

What models does awesome-gpt-image-2 support?

The codebase supports standard GPT-Image-2 models and includes compatibility features for GPT-Image 2.5 (Sunburst and Flare). While the web interface showcases 2.5 capabilities through static comparisons, the prompt templates and agent skills work across all model versions.

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