gpt-image-2-style-library
freestylefly/awesome-gpt-image-2
Generate production-ready GPT-Image2 prompts using curated style templates and visual guidelines.
What is gpt-image-2-style-library?
This skill provides access to the awesome-gpt-image-2 style library, a repository of visual styles, prompt templates, and best practices for image generation. Use it when you need to transform user intent into optimized GPT-Image2 prompts with structured composition, style, and constraint blocks.
- Match user requests to template categories (product, poster, UI, infographic, character, scene, etc.)
- Apply visual style tags and scene tags from the curated library
- Present multiple template options when several are plausible
- Build final prompts with subject, composition, style, text requirements, aspect ratio, and constraints
- Support both Chinese and English prompt generation
- Reference example cases and pitfalls from the repository
How to install gpt-image-2-style-library
npx skills add https://github.com/freestylefly/awesome-gpt-image-2 --skill gpt-image-2-style-library- Access to the awesome-gpt-image-2 repository
- NPM and Node.js installed to run the skill
- Familiarity with GPT-Image2 prompt structure
How to use gpt-image-2-style-library
- 1.Identify the user's target output type (product, poster, UI, infographic, etc.)
- 2.Consult references/style-library.md to review available templates and style tags
- 3.Match the request to template categories, visual styles, and scene tags in order of relevance
- 4.If multiple templates fit, present 2-3 options with brief reasons and ask the user to choose
- 5.Build the final prompt using the five-block structure: subject/task, composition/layout, visual style/materials, text/labels, aspect ratio/format, and constraints
- 6.Include the selected template name and relevant example case IDs in the output
Use cases
- Generate a product mockup prompt with consistent branding and layout
- Create an infographic prompt with readable labels and visual hierarchy
- Build a character illustration prompt with specific art style and composition
- Develop a poster design prompt with text placement and visual impact
- Generate multiple concept variations using a single template base
- AI agents creating or optimizing image-generation prompts
- Designers and product teams using GPT-Image2 for visual content
- Teams needing consistent, production-ready prompt templates
- Users working in Chinese or English who want language-appropriate prompts
gpt-image-2-style-library FAQ
Run `npm run generate:style-skill` to regenerate the skill from the updated repository, then `npm run install:skill` to install it locally.
Match the user's language: write in Chinese for Chinese requests and English for English requests, unless the user explicitly asks otherwise.
Present 2-3 of the strongest options with short explanations of why each fits, then ask the user to choose rather than picking one unilaterally.
Read `references/style-library.md`, which is generated from `data/style-library.json` in the repository. Prefer this reference over memory when template names or style tags matter.
Subject and task, composition and layout, visual style and materials, text and label requirements, and aspect ratio plus output format with constraints.
Full instructions (SKILL.md)
Source of truth, from freestylefly/awesome-gpt-image-2.
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.
Example Output

Example request: 用 gpt-image-2-style-library 技能生成城市生命系统图谱
Reference
- Read
references/style-library.mdbefore choosing a template or style. - The reference is generated from
data/style-library.jsonin the repository. - Prefer the reference over memory when template names, categories, covers, or style tags matter.
Workflow
- Detect the user's language and answer in that language.
- Identify the user's target output: product, poster, UI, infographic, brand, photo, illustration, character, scene, history, document, or special task.
- Match the request in this order: template category, visual style tag, scene tag, then nearest example cases.
- 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.
- 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
- Include the selected template name and any useful example case IDs.
Output Defaults
- Provide a copyable prompt first.
- Keep constraints concrete: exact text, aspect ratio, readable labels, layout hierarchy, and avoided artifacts.
- For Chinese requests, write the final prompt in Chinese unless the user asks for English.
- For English requests, write the final prompt in English unless the user asks for Chinese.
- When the user asks for multiple concepts, reuse one template and vary subject, composition, palette, and scene.
Maintenance
When the source repository changes, run:
npm run generate:style-skill
To install the skill into the local Codex skill folder, run:
npm run install:skill
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