How to Use awesome‑gpt‑image‑2: Best Practices for the GPT‑Image‑2 Prompt Engine

Set your environment variables, use the style‑library skill to select structured templates, keep prompts under 6000 characters, and call the /api/generate-image endpoint with proper JWT authentication to generate production‑ready images via the GPT‑Image‑2 model.

The awesome‑gpt‑image‑2 repository is a full‑stack prompt‑as‑code engine that transforms natural language intentions into optimized prompts for the GPT‑Image‑2 model. According to the source code maintained by freestylefly, it combines a curated gallery of 500+ image cases, a deterministic agent skill for prompt construction, and a credit‑managed generation API. This guide covers the architectural patterns, practical usage guidelines, and code examples you need to implement awesome‑gpt‑image‑2 effectively.

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Architecture Overview: Three Core Pillars

The awesome‑gpt‑image‑2 codebase is organized around three integrated components that handle discovery, generation, and prompt engineering.

Pillar	Function	Key Source File
Web UI & Gallery	Browse cases, user authentication, initiate generation	README.md
Generation API	Auth validation, credit reservation, CIYUAN service call	api/generate-image.js
Style Library & Agent Skill	Taxonomy of templates, deterministic prompt assembly	data/style-library.json & agents/skills/gpt-image-2-style-library/SKILL.md

Understanding how these layers interact ensures you configure and extend the system correctly.

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Authentication and Environment Setup

Required Environment Variables

Before running awesome‑gpt‑image‑2, populate your environment with all keys listed in the README. These variables are consumed across the stack:

# Frontend (Vite) — exposed to browser

VITE_SUPABASE_URL=https://your-project.supabase.co
VITE_SUPABASE_ANON_KEY=eyJ...

# Server‑only — never expose to client

SUPABASE_SERVICE_ROLE_KEY=eyJ...
CIYUAN_API_KEY=sk-...
STRIPE_SECRET_KEY=sk_test_...

Critical security practice: Keep SUPABASE_SERVICE_ROLE_KEY, CIYUAN_API_KEY, and Stripe secrets server‑only. The frontend uses src/supabaseClient.js (Vite env vars), while serverless functions access api/_lib/supabase.js with elevated privileges.

Supabase Client Initialization

The frontend client is instantiated in src/supabaseClient.js:

import { createClient } from '@supabase/supabase-js'

const supabaseUrl = import.meta.env.VITE_SUPABASE_URL
const supabaseAnonKey = import.meta.env.VITE_SUPABASE_ANON_KEY

export const supabase = createClient(supabaseUrl, supabaseAnonKey)

Server‑side helpers in api/_lib/supabase.js handle JWT validation via getAuthContext and provide reserveGeneration, reserveCreditGeneration, completeReservation, and releaseReservation functions for the credit flow.

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Credit Reservation and Generation Flow

The generation pipeline in api/generate-image.js follows a strict transactional pattern to prevent double‑spending credits.

Step‑by‑Step Flow

1. Authenticate — getAuthContext validates the Authorization: Bearer <jwt> header
2. Reserve — reserveGeneration (or reserveCreditGeneration for admin batch jobs) creates a row in user_generation_reservations via Postgres RPC
3. Generate — generateImage POSTs to the CIYUAN endpoint /v1/images/generations
4. Finalize — completeReservation on success, releaseReservation on failure

Error Handling Patterns

The handler function in api/generate-image.js returns structured status codes:

• 502 / 503 — upstream CIYUAN service errors
• 402 — CREDITS_REQUIRED when balance insufficient
• 400 — prompt exceeds 6000‑character limit

Validate your integration by running npm run dev locally and hitting /api/generate-image with a minimal payload before deploying.

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Prompt Construction with the Style Library

The style library is the deterministic engine that converts user intent into optimized prompts. It is defined in data/style-library.json and consumed by the agent skill documented in SKILL.md.

Structure of the Style Library

The JSON defines four hierarchical levels:

• Categories — broad domains (e.g., "UI/UX", "Illustration", "Photography")
• Styles — aesthetic approaches within a category
• Scenes — environmental contexts
• Templates — complete prompt structures with guidance, pitfalls, and exampleCases

The Six‑Block Prompt Structure

According to agents/skills/gpt-image-2-style-library/SKILL.md, every generated prompt must contain:

1. Subject — what to depict
2. Layout/Composition — spatial arrangement
3. Style — visual aesthetic reference
4. Aspect Ratio — output dimensions
5. Positive Constraints — specific requirements from guidance
6. Negative Constraints — pitfalls to avoid

Skill Workflow

The agent skill executes deterministically:

1. Detect request language
2. Map intent → category → style → scene
3. Select highest‑scoring template (or present 2‑3 options for user choice)
4. Assemble the six‑block prompt with localized output

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Practical Usage Guidelines

Practice	Implementation	Benefit
Prompt length	Trim to ≤ 6000 characters before submission	Prevents 400 Bad Request errors enforced in generate-image.js
Credit monitoring	Poll /api/me for balance; use reserveCreditGeneration for batch jobs	Avoids 402 errors, enables deterministic budgeting
Template selection	Prefer highest‑scoring template; offer 2‑3 options when ambiguous	Improves relevance, reduces model hallucination
Guidance embedding	Always include guidance block and pitfalls as negative constraints	Cleaner, more controllable images
Localization	Match final prompt language to request (auto‑detected by skill)	Consistent with user expectations
Batch generation	Reuse template, vary subject, palette, scene	Style consistency across series
Pre‑deployment testing	Run npm run dev, test auth → reservation → image return	Catches integration bugs early

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Installation and CLI Usage

Install the Style‑Library Skill

The skill package includes a global CLI installer:

# Global install (recommended)

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

# Install into all local agents (Codex, Claude, etc.)

gpt-image-2-style-library install all

The install script is defined in agents/skills/gpt-image-2-style-library/bin/install.mjs.

Generate Prompts from Command Line

# Example: UI screenshot system for finance dashboard

gpt-image-2-style-library \
  "Create a UI screenshot of a finance dashboard with dark theme, showing charts and a navigation bar"

The skill selects the UI Screenshot System template (tpl-ui) and outputs a copy‑able six‑block prompt.

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Direct API Integration

For programmatic access, call the generation endpoint with proper JWT authentication:

import fetch from 'node-fetch';

const payload = {
  prompt: "UI screenshot system: finance dashboard, dark theme, 3 charts, top navigation, 16:9",
  caseId: 17  // Template ID from style-library.json
};

const res = await fetch('https://gpt-image2.canghe.ai/api/generate-image', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${process.env.VITE_SUPABASE_ANON_KEY}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify(payload)
});

const data = await res.json();
if (data.ok) {
  console.log('Base64 image:', data.image);  // data:image/jpeg;base64,...
} else {
  console.error('Generation error:', data.error);
}

The handler returns a complete base64‑encoded JPEG data URI on success.

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Extending the Style Library

To add custom templates:

1. Edit data/style-library.json — add new object under templates

2. Regenerate skill reference:

   npm run generate:style-skill

3. Deploy (Vercel rebuilds automatically)

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Summary

• Environment configuration is foundational — separate server‑only and client‑exposed variables per src/supabaseClient.js and api/_lib/supabase.js
• Credit flow is transactional — authenticate, reserve, generate, commit/release via functions in api/generate-image.js
• Prompt engineering is deterministic — use the style‑library skill to enforce the six‑block structure from SKILL.md
• Operational limits — 6000‑character prompt cap, credit balance checks, and proper error handling for 402/502/503 statuses
• Extensibility — modify data/style-library.json, run npm run generate:style-skill, and redeploy

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Frequently Asked Questions

How do I resolve CREDITS_REQUIRED errors in awesome‑gpt‑image‑2?

Check your balance via /api/me and ensure you're not exceeding free‑generation limits. For automated workflows, use the admin endpoint reserveCreditGeneration instead of the standard reserveGeneration function to bypass restrictions.

What is the maximum prompt length for the generate‑image endpoint?

The hard limit is 6000 characters, enforced by validation logic in api/generate-image.js. Exceeding this returns a 400 Bad Request error — trim verbose descriptions before submission.

How does the style‑library skill choose between multiple matching templates?

The skill selects the highest‑scoring template based on intent→category→style→scene mapping. When confidence is ambiguous, it presents 2–3 options for user selection, as specified in agents/skills/gpt-image-2-style-library/SKILL.md step 4.

Can I use awesome‑gpt‑image‑2 without the web UI?

Yes — install the gpt-image-2-style-library CLI globally, or call the /api/generate-image endpoint directly with a JWT token and JSON payload containing prompt and optional caseId.

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