The Role of `data/style-library.json` in awesome-gpt-image-2: Master Style Catalogue Explained

data/style-library.json serves as the single source of truth for all visual styles, tags, categories, and prompt templates in the awesome-gpt-image-2 project, powering both the frontend UI and the GPT-Image-2 Agent skill generation.

The awesome-gpt-image-2 repository relies on a centralized data architecture to maintain consistency across its website and AI agent capabilities. At the heart of this architecture sits data/style-library.json, a master JSON file that functions as the definitive registry for every visual taxonomy used throughout the ecosystem. This file eliminates data duplication by serving as the shared foundation for the style selector interface, automated documentation generation, and the structured skill definitions that drive GPT-Image-2 interactions.

What data/style-library.json Contains

Hierarchical Style Taxonomy

The JSON file defines a comprehensive hierarchy of visual classifications. It organizes content into tag labels (e.g., "Art", "Dashboard", "Scene") and nested style groups such as UI & Interfaces, Charts & Infographics, and Products & E-commerce. Each group contains discrete style descriptors (e.g., "3D", "Brand", "Photography") accompanied by structured metadata.

According to the repository structure, every style entry includes multilingual titles and optional keyword arrays. This taxonomy drives both the categorization logic in the frontend and the parameter validation within the GPT-Image-2 skill.

Multilingual Tag Definitions

Each tag and style entry within data/style-library.json maintains localized labels for English (en) and Chinese (zh). This built-in internationalization allows the awesome-gpt-image-2 UI and generated documentation to render in multiple languages without requiring additional translation layers or external localization files.

Prompt Templates

The file contains structured prompt templates that standardize how style instructions are formatted for the GPT-Image-2 model. These templates ensure that when users select specific visual styles through the interface, the underlying prompts sent to the image generation API follow consistent grammatical and semantic patterns.

How awesome-gpt-image-2 Consumes the Style Library

Runtime Frontend Hydration (src/main.jsx)

The website dynamically loads the catalogue at runtime to populate the style selector interface. In src/main.jsx at line 3124, the application fetches the JSON file directly:

// Runtime fetch in the frontend component
fetch('/style-library.json')
  .then(r => r.json())
  .then(styleLib => {
    // Populate dropdown with tag labels from the catalogue
    const tags = Object.keys(styleLib.tagLabels);
    // Render UI components based on style groups...
  });

This approach ensures the frontend always reflects the current state of the style definitions without requiring a rebuild or redeployment when styles are updated.

Build-Time Skill Generation (generate-style-skill.mjs)

The repository automates GPT skill creation using scripts/generate-style-skill.mjs. At line 6, the script reads data/style-library.json to construct the formal skill definition:

import { readFileSync } from 'fs';
import { join } from 'path';

const libraryFile = join(root, 'data', 'style-library.json');
const library = JSON.parse(readFileSync(libraryFile, 'utf8'));

if (!library.templates?.length) {
  throw new Error('style-library.json must include templates');
}

// Build the skill definition from the library data...

This build process generates agents/skills/gpt-image-2-style-library/SKILL.md, ensuring the AI agent possesses an exact mirror of the visual taxonomy available to human users.

Static Site Data Pipeline (generate-site-data.mjs)

Similarly, scripts/generate-site-data.mjs at line 8 consumes the same JSON file to produce static reference documentation. This script outputs agents/skills/gpt-image-2-style-library/references/style-library.md, creating human-readable markdown that stays synchronized with the codebase. Because both the skill definition and the reference documentation derive from the same source file, the project eliminates documentation drift.

Architectural Benefits of the Centralized JSON Pattern

Data Consistency: Because the website UI, the generated markdown reference, and the Agent skill all derive from data/style-library.json, any addition or modification of a style propagates automatically to every consumer. This eliminates duplicate definitions across src/, scripts/, and agents/ directories.

Single Source of Truth: Modifying visual styles, adding new categories, or updating multilingual labels requires editing only one file. The change immediately affects runtime behavior, build artifacts, and documentation upon the next deployment or script execution.

Version Control Integration: Storing the master catalogue as a JSON file in version control allows teams to track style evolution through git history, review changes via pull requests, and rollback taxonomies if needed.

Summary

• data/style-library.json functions as the master registry for all visual styles, tags, and prompt templates in awesome-gpt-image-2.
• The file powers the frontend style selector via runtime fetch calls in src/main.jsx.
• Build scripts generate-style-skill.mjs and generate-site-data.mjs parse the JSON to create synchronized skill definitions and documentation.
• Built-in multilingual support (English and Chinese) enables internationalization without separate translation files.
• The centralized architecture ensures consistency across the website, AI agent capabilities, and reference materials.

Frequently Asked Questions

What happens if I modify data/style-library.json?

Any modification to data/style-library.json automatically propagates throughout the ecosystem. The frontend will display updated styles on the next page load, while running the build scripts will regenerate SKILL.md and the markdown reference files to reflect the changes. This ensures the AI agent and human users always see identical style options.

Does the frontend require a rebuild when styles change?

No. Because src/main.jsx fetches style-library.json at runtime via standard HTTP requests, style updates take effect immediately for web users. Only the GPT skill definition and static documentation require script regeneration via generate-style-skill.mjs or generate-site-data.mjs.

What types of data are stored in each style entry?

Each entry contains a style descriptor ID, multilingual display titles (en and zh), optional keyword arrays for search/filtering, and references to prompt templates. The hierarchical structure also defines parent-child relationships between categories and specific visual styles.

Where can I find the generated outputs from this file?

The build pipeline creates two primary artifacts: agents/skills/gpt-image-2-style-library/SKILL.md (the formal skill definition for GPT-Image-2) and agents/skills/gpt-image-2-style-library/references/style-library.md (human-readable documentation). Both files are derived entirely from the master JSON catalogue.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}