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

> Integrate the gpt-image-2-style-library npm package into custom agents. Install globally, deploy the skill with the CLI, and import the JSON or invoke directly from your agent runtime for seamless integration.

- Repository: [苍何/awesome-gpt-image-2](https://github.com/freestylefly/awesome-gpt-image-2)
- Tags: how-to-guide
- Published: 2026-08-31

---

**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.

---

## 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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) – normalized representation of every category, style, scene, and template | [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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.

---

## Step 1: Install the Package

Two installation paths exist depending on your agent ecosystem.

### Option A: Global NPM Install (Universal)

```bash

# 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:

```bash
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.

---

## Step 2: Understand the Data Schema

The master file [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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 |

---

## Step 3: Load and Query the Library in Your Agent

### Basic Integration (Node.js)

Import the JSON directly and build matching logic:

```javascript
// 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

```

---

## Step 4: Handle Ambiguous Intents with Multi-Candidate Selection

When user intent spans multiple categories, present ranked options:

```javascript
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.

---

## Step 5: Maintain the Skill Across Updates

The generation pipeline `scripts/generate-style-skill.mjs` rebuilds skill files whenever [`style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/style-library.json) changes. Refresh your installation by running:

```bash
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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/package.json) and ensure your agent always uses current style definitions.

---

## 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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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.

---

## Summary

- **Install** via `npm install -g` or `npx skills add` to deploy into standard agent directories
- **Import** [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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

---

## 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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/references/style-library.md) and skill files from the updated [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json).