What Are the Categories and Core Capabilities of Awesome-GPT-Image-2 Templates?

The awesome-gpt-image-2 repository organizes more than 390 prompt templates into 15 distinct visual categories and delivers industrial-grade generation capabilities including JSON-based agent templates, multilingual support, and automated workflows designed to eliminate common generation errors.

The awesome-gpt-image-2 project serves as a comprehensive style library for GPT-Image-2 models, providing a taxonomy of production-ready prompt templates that span the entire visual design spectrum. According to the source code in docs/templates.md, this system enables both human users and automated agents to generate high-fidelity images while avoiding common pitfalls like garbled text or incorrect aspect ratios.

The 15 Visual Categories of Awesome-GPT-Image-2 Templates

The library categorizes templates into 15 top-level domains that reflect typical visual output requirements. As defined in docs/templates.md (lines 10-88), these categories include:

• UI / 界面 – UI and Interface Design
• Infographics / 信息可视化 – Information Visualization
• Posters / 排版 – Typography and Poster Design
• Product & e-commerce
• Brand & logo
• Architecture & space
• Photography & realism
• Illustration & art
• Character & role
• Scene & narrative
• History & antiquity
• Document & publishing
• Other – Catch-all category

This taxonomy ensures that user intents map precisely to specialized templates, whether generating iOS mock-ups or scientific infographics.

Core Capabilities and Technical Architecture

Beyond categorization, the repository provides robust technical capabilities designed for production environments.

Industrial-Grade Prompt Generation with Pitfall Avoidance

Each template in docs/templates.md includes a ready-to-copy prompt paired with a pitfall-avoidance guide (防坑指南). These guides explicitly constrain generation parameters to prevent common errors such as misplaced UI elements, unreadable text, or incorrect color spaces. The templates enforce explicit constraints that keep outputs readable and on-brand.

JSON-Advanced Templates for Agent Integration

For programmatic use, the system offers JSON-advanced templates (JSON 进阶模板) optimized for agent consumption. As documented in agents/skills/gpt-image-2-style-library/SKILL.md, these templates include structured fields for subject, composition, style, text elements, aspect ratio, and constraints. Agents can parse these JSON objects to assemble prompts dynamically rather than relying on string concatenation.

Multilingual Support and Style Tagging

The skill implementation in SKILL.md (lines 2-8 and 24-35) features automatic language detection that outputs prompts in either Chinese or English based on user input. Additionally, the system maintains style tags and scene tags that allow agents to rapidly match user intent to appropriate templates by comparing semantic similarity against the tag taxonomy.

The Six-Step Agent Workflow

According to SKILL.md (lines 22-35), agents interact with the library through a deterministic six-step workflow:

1. Language detection – Determines output language (Chinese or English)
2. Target type identification – Maps the request to one of the 15 categories
3. Ranking by category – Scores templates by relevance within the selected category
4. Optional disambiguation – If multiple templates score similarly, presents 2-3 options with rationales
5. Prompt assembly – Constructs the final prompt from modular blocks (subject, composition, style, text, aspect ratio, constraints)
6. Final output generation – Produces a copyable prompt with template name and example-case IDs

This workflow ensures consistent, high-quality outputs while reducing hallucination risks.

Repository Structure and Key Implementation Files

The architecture relies on several critical files:

• docs/templates.md – Contains the complete category taxonomy, example prompts, JSON templates, and pitfall-avoidance documentation
• agents/skills/gpt-image-2-style-library/SKILL.md – Defines the agent skill workflow, language handling logic, and output defaults
• agents/skills/gpt-image-2-style-library/references/style-library.md – Reference data consulted by agents during template selection
• data/style-library.json – The generated JSON source that powers both markdown templates and JSON-advanced versions; can be refreshed via npm commands as noted in SKILL.md (lines 45-52)

Practical Usage Examples

The following examples demonstrate how agents invoke the library for different use cases.

For UI mock-ups in Chinese:

# Example 1 – UI mock‑up (Chinese)

模板：UI与界面 → 常规模板  
Prompt:
为 iOS 生成一张 Fitness App 界面图。核心功能：计步、卡路里、心率。视觉风格：极简，主色 Neon Green，强调色 White。布局：卡片流，信息层级清晰，留白充足。输出：高保真 UI 截图，文字可读，比例 9:16。

For programmatic infographic generation:

// Example 2 – Infographic (JSON, for an agent)
{
  "type": "Infographic",
  "topic": "Urban Metabolism",
  "audience": "General Public",
  "structure": {
    "title_area": "城市生命系统图谱",
    "layout": "Isometric cutaway, 12 numbered panels",
    "modules": [
      {"title": "能源", "icon": "lightning", "text": "Power flows"},
      {"title": "水循环", "icon": "water_drop", "text": "Water flows"}
    ]
  },
  "style": {
    "aesthetic": "Scientific atlas",
    "colors": "Low saturation, color‑coded flows",
    "background": "Light paper texture"
  },
  "constraints": "No cyberpunk, no gibberish text, strict structural layout"
}

Summary

• The awesome-gpt-image-2 library covers the complete visual design spectrum, organizing 390+ templates into 15 categories from UI mock-ups to historical archives
• It provides dual-format outputs: human-friendly markdown prompts and machine-friendly JSON templates suitable for agent automation
• Each template includes explicit constraint systems and pitfall-avoidance guides to ensure industrial-grade output quality
• The system implements automatic language detection and a deterministic six-step workflow to standardize generation processes
• The architecture separates concerns between data (data/style-library.json), documentation (docs/templates.md), and agent logic (SKILL.md)

Frequently Asked Questions

How many prompt templates are included in awesome-gpt-image-2?

The repository contains more than 390 prompt-template cases organized across 15 distinct categories, covering domains from UI design to scientific infographics and realistic photography.

What is the difference between regular templates and JSON-advanced templates?

Regular templates provide copy-paste ready text prompts with pitfall-avoidance guides for human users, while JSON-advanced templates offer structured data objects with typed fields for subject, style, and constraints that agents can programmatically manipulate during automated workflows.

How does the repository prevent common image generation errors?

Each template includes a pitfall-avoidance guide (防坑指南) that specifies explicit constraints such as aspect ratio requirements, text readability rules, and prohibited style elements, effectively eliminating common issues like garbled text or incorrect UI element placement.

Can the template library be updated or extended?

Yes. The library generates from data/style-library.json and supports refresh workflows via npm commands, allowing maintainers to update the taxonomy and regenerate markdown documentation without manually editing individual template files.

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