GPT-Image-2 moved the question from "can it render?" to "can you ship the same layout twice?" freestylefly/awesome-gpt-image-2 — roughly 19.9k GitHub stars, 2k forks, MIT license — answers with Prompt-as-Code: 530+ reverse-engineered cases, 20+ industrial templates, and a gpt-image-2-style-library Agent Skill that shares one JSON library with the live gallery at gpt-image2.canghe.ai.
The repo is bilingual (English README plus Chinese and Japanese variants). For builders wiring GPT-Image-2 APIs or transparent PNG workflows, this is a structured prompt engine — not a mood board of one-off strings.
TL;DR — what API builders ask first
| Question | Direct answer |
|---|---|
| Repo? | github.com/freestylefly/awesome-gpt-image-2 |
| Cases / templates? | 530+ gallery cases · 20+ industrial template categories |
| Skill name? | gpt-image-2-style-library |
| Fast install? | npx skills add freestylefly/awesome-gpt-image-2 --skill gpt-image-2-style-library … |
| Data sync? | data/style-library.json powers website + skill |
| License? | MIT on repo structure; verify third-party case rights for commercial use |
| vs raw ChatGPT UI? | Atomic schema for agents, batch jobs, and DESIGN.md-style specs |
Project vision: compress prose into protocol
After GPT-Image-2 became widely available, scattered community examples stopped scaling. This project splits subjects, lighting, materials, layout, and typography into composable parts — the same move context engineering makes for text, applied to pixels.
Core goals from README:
- Atomic schema — swap business variables without breaking hierarchy
- Workflow friendly — agents, scripts, automation-first
- Structured control — layout, copy, and information density become parameters
Category map: cases and templates
Start from the full gallery, then jump to templates.md for reusable skeletons.
| Category | Cases (approx.) | Template focus |
|---|---|---|
| UI and interfaces | 73 | Components, page hierarchy, screenshot texture |
| Charts and infographics | 52 | Modules, arrows, data structure |
| Posters and typography | 88 | Headline systems, layout impact |
| Products and e-commerce | 41 | Selling points, detail-page structure |
| Brand and logos | 27 | Identity systems, touchpoints |
| Photography and realism | 78 | Lenses, lighting, texture |
| Illustration and art | 58 | Brushwork, materials, styles |
| Characters and people | 31 | Pose sheets, consistency |
| Scenes and storytelling | 21 | Storyboards, emotional pacing |
| History and classical Chinese | 16 | Scroll format, dynasty detail |
| Documents and publishing | 10 | Page systems, TOC layout |
| Other | 28 | Mixed experimental workflows |
Industrial templates and a pitfalls guide live in docs/templates.md — read pitfalls before batch-generating UI screenshots with embedded text.
Install the Agent Skill
Recommended: npx skills (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
Install to every supported local agent:
npx skills add freestylefly/awesome-gpt-image-2 --global --all --copy
Claude Code plugin marketplace
Inside Claude Code:
/plugin marketplace add freestylefly/awesome-gpt-image-2
/plugin install gpt-image-2-style-library@awesome-gpt-image-2
npm CLI
npm install -g gpt-image-2-style-library
gpt-image-2-style-library install all
Or without global install:
npx gpt-image-2-style-library install all
install all writes to ~/.codex/skills, ~/.claude/skills, and ~/.agents/skills. Restart the agent session after installing.
Example request:
Use gpt-image-2-style-library to create an infographic prompt about Codex.
For local development of the skill source:
git clone https://github.com/freestylefly/awesome-gpt-image-2.git
cd awesome-gpt-image-2
npm run generate:style-skill
npm run install:skill
Skill source: agents/skills/gpt-image-2-style-library — generated from data/style-library.json.
See what Agent Skills are and how to build your first skill if you plan to fork the schema.
How to use the repository (four steps)
- Pick a direction from featured cases (infographic, UI screenshot, product poster).
- Open the gallery — copy structure first, style words second (top AI prompts for image generation explains general prompt hygiene).
- Fill a template from
docs/templates.mdwith your product variables. - Call your API — OpenAI
gpt-image-2, a gateway, or the hosted site if you accept their auth/billing stack.
Example API-minded workflow
Pair structured prompts with parameters documented for native transparent PNGs:
- Model:
gpt-image-2 background="transparent"when you need alpha channels for slides or merch mockups- Prompt body from template — do not describe a backdrop if you asked for transparency
For evaluation discipline, cross-read evaluating prompts for quality.
Hosted gallery vs open repo
| Surface | What you get |
|---|---|
| GitHub repo | Cases, templates, skill CLI, Vite site source |
| gpt-image2.canghe.ai | Large previews, filters, Google sign-in generation, favorites |
| Your pipeline | Templates + skill + your own API keys |
Self-hosting the full billed site requires Supabase migrations, Stripe webhooks, and proxy keys listed in README — substantial ops. Most explainx.ai readers will clone templates + skill and generate via their existing OpenAI or gateway account.
Featured cases worth studying
| Case | Why builders open it |
|---|---|
| Case 1 — Urban Metabolism Atlas | Engineering infographic hierarchy + bilingual labels |
| Case 17 — Interaction design diagram | Structured product explainer layout |
| Case 310 — Snack brand breakdown | Brand narrative + structural callouts |
| Case 334 — RAG technical explainer | Process arrows + concept modules (pairs with RAG guides) |
| Case 534 — Red-beam editorial poster | Strict typography rules in 9:16 layout |
Latest community imports (cases 533–538) stress reference-image editing, same-face lookbooks, and no-text constraints — read before assuming text renders reliably.
Honest limits
| Limit | Detail |
|---|---|
| Third-party case rights | README disclaimer — community-sourced; commercial use may need author OK |
| Text in images | Still the hardest failure mode; templates flag typography pitfalls, not guarantees |
| Hosted site deps | Auth, credits, Stripe, Supabase — not required to use templates locally |
| Language mix | Primary docs include Chinese community context; English navigation layer on homepage |
| Model drift | Re-verify cases when OpenAI updates gpt-image-2 behavior or pricing |
| Sponsor APIs | README lists relay providers — evaluate latency, storage, and ToS yourself |
This library organizes prompts; it does not replace C2PA/provenance thinking for production asset pipelines.
The bottom line
awesome-gpt-image-2 is the largest open Prompt-as-Code library for GPT-Image-2 in August 2026: gallery cases, industrial templates, and an installable gpt-image-2-style-library skill synced to one JSON catalog.
Star github.com/freestylefly/awesome-gpt-image-2, install the skill into Claude Code, pick one template category matching your product surface, and promote the filled schema into your API job runner — not into one-off chat messages.
Related on explainx.ai
- OpenAI ChatGPT Images 2 / GPT-Image-2 Guide · model capabilities baseline
- GPT-Image-2 Transparent Backgrounds API · alpha PNG parameters
- Top AI Prompts for Image Generation · general prompt patterns
- What Are Agent Skills? · skill install model
- How to Build Your First Agent Skill · extend the style library
- DESIGN.md Templates for AI Agents · spec-first UI before image gen
- Evaluating Prompts for Quality · measure batch output
- Context Engineering vs Prompt Engineering · structured inputs for agents
Source: freestylefly/awesome-gpt-image-2 on GitHub (MIT; README and docs as of August 26, 2026).
Case counts, skill paths, and hosted-site features reflect the public repository at publication time. Verify gallery and license notes before commercial deployment.