awesome-gpt-image-2: The "Prompt as Code" Library for GPT-Image-2 (520+ Reverse-Engineered Cases), Explained (2026)

"How do I write GPT-Image-2 prompts that reliably produce great images? There's a library with 500+ cases."

The verdict: "awesome-gpt-image-2" is an open-source library that systematizes prompts for OpenAI's GPT-Image-2. It reverse-engineers 520+ community examples into 20+ industrial templates and an Agent Skill — the definitive "Prompt as Code" resource.

This article explains what it is, why it matters, and how to use it — with full source citations.

What you'll learn

What awesome-gpt-image-2 is
The "Prompt as Code" idea
The library's structure (diagram)
The 13 categories and 520+ cases
How to install the Agent Skill
Honest caveats (licensing and rights)
FAQ

What is awesome-gpt-image-2?

awesome-gpt-image-2 is a GitHub library that systematizes prompt engineering for GPT-Image-2.

Repo: freestylefly/awesome-gpt-image-2
Stars: 11.4k, forks: 1.3k
License: MIT (free for commercial use)
Languages: English, Japanese, Chinese
Contents: 520+ cases, 20+ templates, Agent Skill, website

Why it exists: the "Prompt as Code" idea

The core concept is "Prompt as Code" — treating prompts as code.

The shifting challenge

With GPT-Image-2, AI image generation moved from "can it make an image?" to "can it make stable, controllable, reusable images?"

A pile of one-off prompt examples goes nowhere. This library turns scattered community examples into structured assets that agents and automation workflows can reuse.

Three design principles

🧱 Atomic schema: split subjects, lighting, materials, layout, and visual details into composable parts
⚙️ Workflow friendly: designed for agents, scripts, and automation
🧬 Structured control: better control over layout, copy, and information hierarchy

In short: compress prose prompts into structured protocols — that's "Prompt as Code."

The library's structure (diagram)

awesome-gpt-image-2 has four pillars.

awesome-gpt-image-2 structure (Prompt as Code)

1. Cases (520+)

Community examples, reverse-engineered into 13 categories.

2. Templates (20+)

Industrial generic and JSON templates — drop in your business variables to reuse.

3. Agent Skill

A style library skill for Claude Code, Codex, and Cursor.

4. Web gallery

gpt-image2.canghe.ai — browse, preview, copy full prompts, and filter by style/scenario.

The 13 categories and 520+ cases

Category	Cases	Content
📰 Posters & Typography	82	Event posters, covers, type-driven visuals
📷 Photography & Realism	75	Portraits, film texture, commercial photography
🧩 UI & Interfaces	73	Apps, websites, dashboards, social screenshots
🎨 Illustration & Art	56	Illustration, art styles, material experiments
📊 Charts & Infographics	52	Infographics, knowledge maps, technical explainers
🛍️ Products & E-commerce	40	Product shots, detail pages, packaging, ads
🧪 Other	28	Creative experiments, special tasks, mixed workflows
🏷️ Brand & Logos	27	Logos, identity, brand touchpoints
🧍 Characters & People	26	Character design, pose sheets, 3D toys
🎬 Scenes & Storytelling	20	Storyboards, narrative scenes, worldbuilding
🏮 Classical Chinese Themes	16	Classical scrolls, historical figures, poetry visuals
🏛️ Architecture & Spaces	12	Architecture renders, interiors, city maps
📚 Documents & Publishing	10	White papers, manuals, publishing layouts

Key point: the practical categories — UI, charts, posters, products — are the strongest. The emphasis is on dashboards, infographics, and e-commerce images, not just pretty pictures.

Installing the Agent Skill

The library ships as an Agent Skill for AI agents.

For Claude Code / Codex / Cursor

npx skills add freestylefly/awesome-gpt-image-2 --skill gpt-image-2-style-library --agent claude-code codex --global --yes --copy

For all agents

npx skills add freestylefly/awesome-gpt-image-2 --global --all --copy

Via npm

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

Then use it like:

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

This lets AI agents reference GPT-Image-2 styles, templates, and categories to produce optimal prompts.

Caveats

Third-party cases: prompts are collected from public communities (YouMind, OpenNana)
Copyright belongs to original authors: the project itself is MIT, but commercial use of third-party content is not guaranteed — get authorization from the original rights holder first
Source preservation: the project works to preserve author profiles, original links, and source repos
Takedown policy: original authors can request removal via Issue

In short: the library's mechanics and templates are free under MIT, but the case prompts inside are third-party works — be careful with commercial use.

Questions Readers Ask

Q. What is awesome-gpt-image-2? An open-source library that systematizes GPT-Image-2 prompts: 520+ cases, 20+ templates, and an Agent Skill.

Q. Is it free? Yes — MIT license; the library itself is free to use, modify, and redistribute.

Q. Can I use it commercially? The library's mechanics and templates are MIT (commercial OK). Third-party case prompts require authorization from the original author.

Q. Which model is it for? OpenAI's GPT-Image-2.

Q. Does it work with AI agents? Yes — shipped as an Agent Skill for Claude Code, Codex, and Cursor.

Q. Is it available in Japanese? Yes — README in English, Japanese, and Chinese.

Q. Is there a web view? Yes — browse the gallery at gpt-image2.canghe.ai.

Q. Which categories are strongest? Posters (82), photography (75), UI (73), illustration (56), and charts (52).

Summary: turning prompts into assets

awesome-gpt-image-2 systematizes GPT-Image-2 prompts as "Prompt as Code."

520+ cases, reverse-engineered and structured
20+ industrial templates for reuse
Agent Skill for AI agents
Trilingual, MIT license

As image generation shifts from "can it generate?" to "can it be controlled?", treating prompts as reusable assets could become the new standard. A must-see for anyone serious about GPT-Image-2 or integrating image generation into AI agents.

Sources

Information as of August 2026. Case and template counts are updated continuously and may change. See the sources above for the latest.