GPT-Image2 Style Library skill
gpt-image-2-style-library
A skill picks a matching template and style from a library of GPT-Image2 cases and assembles it into a finished prompt
- 35,752 ★ on the repository
- Last commit
- MIT
Minimal access
Why this level
- The skill only assembles a text prompt; the model itself performs generation and billing separately
Install
- Works in
In your terminal, with SkillFoxx CLI
npx skillfoxx add skills/gpt-image-2-style-libraryDetects the agents on your machine, shows the access level and pins the version.
Other ways to install
Access and checks
How we reviewMinimal accessThe tool runs nothing and makes no network calls.
- The skill only assembles a text prompt; the model itself performs generation and billing separately
This is third-party code. Review the repository files before installing.
What it does
The skill turns a description of a desired image into a ready GPT-Image2 prompt using a reference drawn from the awesome-gpt-image-2 repository, which holds hundreds of parsed cases and dozens of industrial templates across categories such as product, poster, UI, infographic, brand, photo, illustration, and character. The agent first detects the request's language and target output type, then matches a template by category, visual style, or scene, offering two or three options when the choice is ambiguous. The finished prompt is assembled from blocks: subject and task, composition, style and materials, on-image text requirements, aspect ratio, and constraints.
Who it is for. For designers and content specialists who regularly generate images with GPT-Image2 and want a consistent prompt instead of a guessed one.
Good fit when
- You need a GPT-Image2 prompt for a specific job: a poster, a cover, an infographic, or a product render
- You want to quickly compare two or three style options before generating
- You need a prompt with exact requirements for on-image text and aspect ratio
Not a fit when
- You need a prompt for a different image model, not GPT-Image2
- The repository with the style reference is not and will not be cloned locally
Example request
Build a GPT-Image2 prompt for a city life system poster in a flat infographic styleLimitations
The skill does not generate the image itself and does not call the GPT-Image2 API; it only builds a prompt from the repository's local style reference. The reference needs to be kept up to date with the package.json command when the repository's data changes.
How to disable. Delete the gpt-image-2-style-library folder from your agent's local skills folder, where npm run install:skill placed it.
FAQ
Does the skill generate the image itself?
No, it only assembles the prompt from the template library; the actual image generation is done separately by the GPT-Image2 model.
How does the skill keep its reference current?
The references/style-library.md file is generated from data/style-library.json by running npm run generate:style-skill whenever the repository's data changes.
Discussion
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README in short
The parent repository is a large library of parsed examples and industrial prompt templates for GPT-Image2, with its own website for browsing the gallery. Inside it sits a reusable gpt-image-2-style-library skill with its own SKILL.md, an install script in bin/install.mjs, and a template reference the agent reads instead of guessing a style from memory. The skill is also distributed as part of a Claude Code plugin marketplace through .claude-plugin/marketplace.json. MIT licensed.
README badge
Original SKILL.md
--- name: gpt-image-2-style-library description: Choose GPT-Image2 / gpt-image-2 visual styles and industrial prompt templates from the awesome-gpt-image-2 style library. Use when an agent needs to create, rewrite, classify, or improve image-generation prompts with repository-backed templates, categories, style tags, scene tags, pitfalls, and example cases. --- # GPT-Image2 Style Library Use this skill to turn a user's image-generation intent into a production-ready GPT-Image2 prompt using the awesome-gpt-image-2 style library. ## Workflow 1. Detect the user's language and answer in that language. 2. Identify the user's target output: product, poster, UI, infographic, brand, photo, illustration, character, scene, history, document, or special task. 3. Match the request in this order: template category, visual style tag, scene tag, then nearest example cases. 4. If one template is clearly strongest, use it directly. If several are plausible, present 2-3 options with short reasons and ask the user to choose. 5. Build the final prompt with these blocks: subject and task, composition and layout, visual style and materials, text and label requirements, aspect ratio and output format, constraints and negative details.