How to Integrate the **gpt-image-2-style-library** NPM Package into Custom Agents

You can integrate the gpt-image-2-style-library npm package into custom agents by installing it globally, running the built-in CLI to deploy the skill into standard agent directories, then importing the JSON data or invoking the skill directly from your agent runtime.

The gpt-image-2-style-library package transforms the complete GPT‑Image‑2 style library—categories, templates, style tags, scene tags, and curated example cases—into a reusable agent skill that any LLM‑driven agent can consume. This guide walks through the exact implementation steps, from installation to runtime usage, based on the official freestylefly/awesome-gpt-image-2 repository.

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What the Package Provides

The repository ships three tightly coupled components that work together to expose style data to agents:

Component	Description	Key Source
Style Data	data/style-library.json – normalized representation of every category, style, scene, and template	data/style-library.json
Agent Skill	agents/skills/gpt-image-2-style-library/ – skill manifest, reference docs, and CLI installer	agents/skills/gpt-image-2-style-library/SKILL.md
Generation Scripts	Build pipeline that converts JSON into skill files	scripts/generate-style-skill.mjs

When installed, the skill lands in standard local skill folders (~/.codex/skills, ~/.claude/skills, ~/.agents/skills), making it discoverable by compatible agent runtimes.

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Step 1: Install the Package

Two installation paths exist depending on your agent ecosystem.

Option A: Global NPM Install (Universal)

# Add CLI to your PATH

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

# Deploy skill to all known agent directories

gpt-image-2-style-library install all

Option B: Skills CLI Wrapper (Recommended)

For Claude Code, Codex, Cursor, and similar runtimes:

npx skills add freestylefly/awesome-gpt-image-2 \
  --skill gpt-image-2-style-library \
  --agent claude-code codex \
  --global --yes --copy

Both methods copy generated skill files into the target directories. The CLI source at agents/skills/gpt-image-2-style-library/bin/install.mjs handles the actual file placement.

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Step 2: Understand the Data Schema

The master file data/style-library.json serves as the single source of truth. Its top-level structure includes:

• version – schema version for compatibility checks
• categories – UI, poster, illustration, and other output types
• styles – visual style descriptors
• scenes – contextual scene tags
• templates – complete prompt templates with metadata

Each template contains:

Field	Purpose
id	Unique identifier
anchor / cover	Visual references
title / description	Localized human-readable labels
category	Bounding category value
styles / scenes / tags	Classification arrays
useWhen	When to apply this template
guidance	Best-practice composition rules
pitfalls	Common mistakes to avoid
exampleCases	Reference case IDs

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Step 3: Load and Query the Library in Your Agent

Basic Integration (Node.js)

Import the JSON directly and build matching logic:

// Load the bundled style library
import styleData from 'gpt-image-2-style-library/data/style-library.json' assert { type: 'json' };

// Match user intent to category
function findTemplate(intent) {
  const lowered = intent.toLowerCase();
  const category = styleData.categories.find(c =>
    lowered.includes(c.title.en.toLowerCase()) ||
    lowered.includes(c.title.zh)
  );

  if (!category) return null;

  return styleData.templates.find(t => t.category === category.value);
}

// Assemble production-ready prompt
function buildPrompt(template, subject) {
  return [
    `Subject: ${subject}`,
    `Composition: ${template.guidance.en.join(' ')}`,
    `Style: ${template.styles.join(', ')}`,
    `Scene: ${template.scenes.join(', ')}`,
    `Constraints: ${template.pitfalls.en.join(' ')}`,
    `Aspect Ratio: 16:9`,
  ].join('\n');
}

// Execute
const intent = 'Create an infographic about data pipelines';
const template = findTemplate(intent);

if (template) {
  console.log(buildPrompt(template, 'Data pipelines'));
  console.log('Examples:', template.exampleCases);
}

Output:

Subject: Data pipelines
Composition: Define 3-5 modules, information flow, visual hierarchy, and short labels. Use color groups, arrows, icons, and clean spacing to reduce clutter.
Style: Infographic, Charts
Scene: Education, Tech
Constraints: Avoid long paragraphs inside the image. Limit module count before adding visual detail.
Aspect Ratio: 16:9
Examples: 334,1,8

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Step 4: Handle Ambiguous Intents with Multi-Candidate Selection

When user intent spans multiple categories, present ranked options:

function findTopTemplates(intent, limit = 3) {
  const lowered = intent.toLowerCase();
  
  return styleData.templates
    .filter(t => t.tags.some(tag => lowered.includes(tag.toLowerCase())))
    .sort((a, b) => a.useWhen.en.length - b.useWhen.en.length)
    .slice(0, limit);
}

// Usage
const candidates = findTopTemplates('Design a product landing page');
candidates.forEach((t, i) => {
  console.log(`${i + 1}. ${t.title.en} — ${t.description.en}`);
});

This pattern lets your agent ask clarifying questions or default to the highest-confidence match.

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Step 5: Maintain the Skill Across Updates

The generation pipeline scripts/generate-style-skill.mjs rebuilds skill files whenever style-library.json changes. Refresh your installation by running:

npm run generate:style-skill   # Manual rebuild

npm run predev                 # Runs automatically before dev server

npm run prebuild               # Runs automatically before production build

These scripts are defined in the root package.json and ensure your agent always uses current style definitions.

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Runtime Invocation for Compatible Agents

Once installed via the CLI, agents following the skills spec auto-register the skill under name gpt-image-2-style-library. Your agent can then handle natural-language requests like:

"Create an infographic about RAG architecture"

The skill executes its workflow from SKILL.md:

1. Detect language
2. Identify output type from intent
3. Match against categories → styles → scenes → example cases
4. Return template with guidance, pitfalls, and case references

No manual JSON manipulation required—the runtime handles skill invocation transparently.

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Summary

• Install via npm install -g or npx skills add to deploy into standard agent directories
• Import data/style-library.json directly for programmatic access to categories, styles, scenes, and templates
• Query using category matching, tag filtering, or keyword search against template metadata
• Build prompts by combining template guidance, pitfalls, and exampleCases with user subject
• Refresh skill files automatically using npm run generate:style-skill when upstream data changes

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Frequently Asked Questions

How do I install the gpt-image-2-style-library package for multiple agent runtimes?

Run gpt-image-2-style-library install all after global npm installation, or use npx skills add with multiple --agent flags. The CLI at bin/install.mjs copies skill files into ~/.codex/skills, ~/.claude/skills, ~/.cursor/skills, and other recognized directories simultaneously.

What data format does the style library use?

The package exposes a versioned JSON schema in data/style-library.json containing arrays of categories, styles, scenes, and fully-specified templates. Each template includes localized titles and descriptions, usage guidance, pitfalls, and linked example case IDs.

Can I use the library without the skills runtime?

Yes. Import the JSON directly: import styleData from 'gpt-image-2-style-library/data/style-library.json'. This bypasses skill discovery and lets you implement custom matching logic, prompt builders, or UI components using the raw data.

How do I update the skill when the repository adds new templates?

Run npm run generate:style-skill from the package directory, or execute npm run predev/prebuild which trigger the generation script automatically. This re-executes scripts/generate-style-skill.mjs to rebuild references/style-library.md and skill files from the updated data/style-library.json.

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