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.
Promote one gallery case into a maintained template
Pick a case that matches the job your asset must do, not only the style you enjoy. A product comparison chart needs readable labels and a clear comparison structure. An editorial illustration needs a recognizable concept. Converting one into the other by swapping a few nouns can preserve the wrong visual hierarchy.
Divide the prompt into fixed constraints and editable variables. Fixed constraints might include the number of panels, reading order, brand colors, and maximum amount of text. Editable variables might include product names, captions, and the central object. Keep these lists short enough that a reviewer can see what changed between generations.
For an example comparison graphic, define two columns and three rows before supplying the copy. Ask whether each row communicates a distinct difference. If the content needs five rows, change the layout deliberately rather than forcing extra text into a template designed for three. Prompt-as-code is useful when it makes these constraints inspectable.
Evaluate the template across awkward inputs
Try a short product name, a long one, an empty optional caption, and two labels that look similar. These cases expose clipping, crowding, and mistaken associations that a polished gallery example may hide. Review the actual output at its intended display size; a label readable in a large preview can become unusable in a social card.
Record prompt revision, variable values, reference assets, and generation settings with each selected image. The repository supplies reusable prompt structures; your records establish which combination produced your chosen result. A prompt file alone cannot guarantee identical pixels on a later run.
When a result fails, classify the defect. Incorrect copy, illegible type, wrong panel count, and unsuitable composition call for different changes. Rewriting every part of the prompt after a minor text defect destroys your ability to learn which constraint mattered.
Keep exact copy separate when correctness matters
If a graphic must contain a legally approved sentence, a price, or a precise measurement, maintain that text as a reviewed source field. Verify the rendered wording character by character. A visually convincing image can still substitute a digit or omit a qualification.
Consider generating the illustration without exact copy and composing reviewed text afterward when your workflow supports it. That is a design choice to evaluate, not a promise that every template supports separate layers. It can make localization and last-minute edits more predictable because the text stays in an editable format.
For multilingual assets, test each language as its own layout input. Translation changes line length and may change reading direction or font requirements. A successful English image does not establish that the template works unchanged with every script.
Define a release record for generated assets
Keep the selected output, its input record, the review decision, and any usage restrictions together. Identify which case inspired the template and preserve its source link. This makes later review possible if a customer asks about asset provenance or a team member needs to regenerate a related graphic.
Begin with one template that produces a useful set of reviewed outputs. Expanding to dozens of categories before learning its failure modes creates more catalog maintenance than creative value. The library becomes a production tool when your team can explain how a template changes, how it fails, and what an acceptable asset looks like.
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
- ChatGPT Images 2.5: Flare, Sunburst, and what changed · September 2026 successor model
- 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
- AI poster slop isn't a model problem — it's a prompting problem · the exact "name a design movement" technique this library systematizes
- 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.