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gpt-image-2-style-library

Convert image-generation intents into structured GPT-Image2 prompts using a style library.

Updated May 13, 2026
Search Tags:#prompt generation#visual style#template selection#gpt-image-2#image synthesis#negative prompt#composition constraints
One-click install
npx skills add https://github.com/Ricardo-Vae/codex-research-skills --skill gpt-image-2-style-library
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gpt-image-2-style-library
Source: https://github.com/Ricardo-Vae/codex-research-skills/tree/main/skills/gpt-image-2-style-library
Command: npx skills add https://github.com/Ricardo-Vae/codex-research-skills --skill gpt-image-2-style-library

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

It helps you quickly choose the right GPT-Image2 visual style and prompt template for a given image-generation request, then assemble a structured prompt with concrete constraints (layout, text, aspect ratio, and negative details).

Core Features & Use Cases

  • Template + Style selection from a style library: Matches your request to a template category, then refines using visual style tags and scene tags.
  • Production-grade prompt construction: Produces a final prompt composed of subject/task, composition, materials/style, required text/labels, output format, and constraints/negative details.
  • Higher control via repository-backed reference: Uses references/style-library.md (generated from data/style-library.json) to prioritize correct template names, categories, cover/style tags, and scene tags.
  • Chinese/English handling: Automatically outputs the final prompt in Chinese or English to match the user request.
  • When ambiguity exists, it offers options: If multiple templates fit, it proposes 2–3 candidates with short reasons for the user to choose.

Quick Start

Use the gpt-image-2-style-library skill to generate a production-ready GPT-Image2 prompt for a city life system map in Chinese.

Frequently Asked Questions about gpt-image-2-style-library

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate production-ready GPT-Image2 prompts with composition and negative constraints?▼

To generate production-ready GPT-Image2 prompts, this tool matches your image intent to a template category, then refines it using visual style and scene tags to assemble explicit composition, text, aspect ratio, and negative constraints.

What is the best way to structure a GPT-Image2 prompt for infographics and UI visuals?▼

The best way to structure a GPT-Image2 prompt is by combining subject/task, composition, materials/style, required labels, output format, and negative details using a repository-backed style library for deterministic template selection.

Can I output GPT-Image2 prompts in Chinese for localized poster generation tasks?▼

Yes, you can output GPT-Image2 prompts in Chinese. The skill automatically matches the final prompt language to your original request, ensuring localized poster and scene generation tasks are handled natively.

How do I select the right visual style when multiple GPT-Image2 templates fit my request?▼

When multiple GPT-Image2 templates fit your request, the skill proposes 2–3 candidate templates with short reasons, allowing you to choose the most appropriate visual style and scene tags for your specific image synthesis task.

What are the limitations of using repository-backed references for GPT-Image2 prompt rewriting?▼

The limitation of using repository-backed references for prompt rewriting is that template and style selection are strictly deterministic to the library's existing categories, meaning outputs rely entirely on predefined style tags rather than generating entirely novel visual styles.

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