How the Template Engine in awesome-gpt-image-2 Works: A Deep Dive

The template engine in awesome-gpt-image-2 is a lightweight, markdown-driven system that converts human-readable prompt recipes stored in docs/templates.md into ready-to-use plain-text strings or structured JSON objects through regex-based placeholder substitution.

The repository provides a complete pipeline for managing GPT-4o image generation prompts. Understanding how the template engine in awesome-gpt-image-2 works reveals a three-layer architecture that bridges human-readable documentation and machine-consumable agent skills.

How the Template Engine Works

The engine operates through a clear separation of concerns: human writers maintain prompts in markdown, a Node.js parser handles variable injection, and a JSON library distributes the final templates to AI agents.

Source Templates in docs/templates.md

All prompt recipes originate in docs/templates.md. This single source of truth uses markdown headers as anchors to organize templates by category.

Each section follows a strict naming convention with anchors like #tpl-ui or #tpl-infographic. Within these sections, templates exist in two formats:

• Plain-text templates containing placeholder tokens inside square brackets—e.g., 为[产品类型]生成一张[平台]界面图。
• JSON "advanced" templates designed for automated agents, containing structured fields like type, platform, layout, and style

This dual-format approach allows both manual copy-paste workflows and programmatic agent consumption from the same source file.

Parsing and Variable Substitution

The transformation logic lives in scripts/generate-style-skill.mjs. This script reads docs/templates.md, extracts sections using the anchor headers, and builds a mapping of template names to template strings.

The engine identifies placeholder tokens using the regular expression \[(.+?)\]. When processing a request, it performs simple string replacement:

• For plain-text templates, it substitutes every bracketed token with the corresponding value from the request object
• For JSON templates, it injects supplied values into specific JSON fields while preserving the overall schema structure

This parsing step converts the static markdown documentation into a dynamic, programmable interface.

Export as an Agent Skill

Processed templates compile into data/style-library.json, a compact JSON representation mapping categories to template lists. The generate-style-skill.mjs script then bundles this JSON with a runtime shim and publishes the gpt-image-2-style-library npm package.

The published skill exposes a simple API for AI agents. According to the documentation in agents/skills/gpt-image-2-style-library/SKILL.md, integration works as follows:

import { getTemplate } from "gpt-image-2-style-library";

const prompt = getTemplate("ui", { 
  product: "Fitness App", 
  platform: "iOS" 
});

This allows installation in Claude Code, Cursor, or any AI-agent framework that supports npm packages.

Working with Templates: Code Examples

Plain-Text Usage

Fill a UI template manually by passing an object with keys matching the bracketed placeholders:

// Assume `engine` is the exported parser
const template = engine.getPlainTemplate('tpl-ui');
const result = engine.fill(template, {
  产品类型: '健身应用',
  平台: 'iOS',
  功能点A: '计步',
  功能点B: '卡路里跟踪',
  颜色: '蓝色',
});

console.log(result);
// Output: 为健身应用生成一张iOS界面图。
//   核心功能：计步、卡路里跟踪。
//   视觉风格：极简，主色蓝色，...

JSON Usage for Agents

Retrieve a ready-to-send JSON payload structured for automated image generation:

import { getJSONTemplate } from "gpt-image-2-style-library";

const uiPrompt = getJSONTemplate('tpl-ui', {
  type: "UI Screenshot",
  platform: "iOS",
  product: "Fitness App",
  layout: "Card-based feed with bottom tab bar",
  style: { 
    theme: "Dark Mode", 
    primary_color: "Neon Green", 
    typography: "Clean sans-serif" 
  },
  content: {
    header: "Today's Activity",
    cards: [{ title: "Running", data: "5.2 km", button: "Start" }]
  },
  constraints: "High fidelity, readable text, 9:16 aspect ratio"
});

console.log(JSON.stringify(uiPrompt, null, 2));

Generating the Skill Package

Build and install the npm package locally using the generator script:

# Build the npm package containing the full style library

node scripts/generate-style-skill.mjs

# Install it globally (or locally) for an agent

npm install -g gpt-image-2-style-library

# Then use it inside a Claude Code or Cursor session

Key Files and Architecture

File	Role
docs/templates.md	Human-written source of truth containing both plain-text and JSON templates organized by anchor headers.
scripts/generate-style-skill.mjs	Parser that scans templates, replaces placeholders using \[(.+?)\], and builds the distribution files.
data/style-library.json	Compiled, machine-readable library output that agents consume at runtime.
agents/skills/gpt-image-2-style-library/SKILL.md	API documentation and integration guide for the published npm skill.
package.json	Defines the gpt-image-2-style-library package metadata and entry points for npm distribution.

Summary

• The template engine in awesome-gpt-image-2 transforms markdown documentation into executable prompt generators through a three-stage pipeline.
• Source control happens in docs/templates.md, using bracketed placeholders like [产品类型] for variable substitution.
• Parsing logic in scripts/generate-style-skill.mjs uses the regex \[(.+?)\] to identify and replace tokens with runtime values.
• Distribution occurs through data/style-library.json and the gpt-image-2-style-library npm package, enabling agent frameworks to consume templates programmatically.
• The system supports both human-friendly plain-text workflows and structured JSON outputs for automated image generation pipelines.

Frequently Asked Questions

How does the template engine handle variable substitution?

The engine scans template strings using the regular expression \[(.+?)\] to identify placeholder tokens enclosed in square brackets. When engine.fill() or getTemplate() is called, it performs a direct key-value replacement, substituting each bracketed token with the corresponding value from the provided options object.

What is the difference between plain-text and JSON templates in the repository?

Plain-text templates produce human-readable prompt strings suitable for direct copying into ChatGPT or similar interfaces—they contain bracketed placeholders like [平台] that get replaced with simple string values. JSON templates return structured objects with nested fields for style, content, and constraints, designed specifically for programmatic consumption by AI agents and automated workflows.

Where does the compiled template library get stored after processing?

After scripts/generate-style-skill.mjs runs, the compiled templates are stored in data/style-library.json. This file serves as the bundled artifact that gets packaged into the gpt-image-2-style-library npm module, allowing agents to import templates without parsing raw markdown at runtime.

Can I use the template engine outside of Claude Code or Cursor?

Yes. Because the system publishes a standard npm package (gpt-image-2-style-library), you can install it in any Node.js environment. The package exports functions like getTemplate() and getJSONTemplate() that work in standard JavaScript runtimes, CI/CD pipelines, or custom agent frameworks, provided you have the data/style-library.json available or install the published package.

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