Image-2 Skill

GPT-4o Image Generation & Editing Skill - Create, edit, transform, and analyze images using GPT-4o native image-2 API. Supports text-to-image, inpainting, outpainting, style transfer, background removal, and intelligent image analysis. Ideal for marketing, product photos, illustrations, UI mockups, and visual content creation.

Create, edit, transform, and analyze images with GPT-4o's native image generation API

When to Use This Skill

Use this skill whenever the user needs to:

• Generate images from text descriptions ("画一张...", "生成图片...", "create an image of...")
• Edit existing images with natural language ("把背景去掉", "add a sunset", "换成蓝色")
• Create variations of an image ("生成几个变体", "make 4 variations")
• Analyze/describe images ("这张图是什么", "describe this image", "提取文字")
• Remove backgrounds ("去除背景", "remove background")
• Style transfer ("变成水彩风格", "make it look like Van Gogh")
• Create marketing visuals ("设计海报", "make a social media post")
• Product photography ("产品图", "product shot on white background")
• UI/UX mockups ("界面设计", "app mockup", "website screenshot")

Core Workflows

Workflow 1: Text-to-Image Generation

When the user describes an image they want to create:

1. Enhance the prompt — Automatically add quality boosters:

   • Append professional photography/art terms based on context
   • Add lighting, composition, and mood details if not specified
   • Specify output format and dimensions if needed

2. Call the API — Use generateImage() with the enhanced prompt:

   javascript

   const result = await generateImage(enhancedPrompt, { size, quality, style });
   

3. Save and present — Download the image to the project directory and show the user:

   • Save to ./generated-images/ by default
   • Return the file path and a brief description

Workflow 2: Image Editing

When the user wants to modify an existing image:

1. Locate the source image — Find the image file path from the conversation context
2. Parse the edit intent — Understand what changes the user wants
3. Call the edit API — Use editImage() with the source and instruction:

   javascript

   const result = await editImage(imagePath, editInstruction, { mask: maskPath });
   

4. Present the result — Show the edited image and describe what changed

Workflow 3: Image Analysis

When the user asks about an image:

1. Get the image — From file path or URL
2. Analyze with GPT-4o Vision — Use describeImage():

   javascript

   const result = await describeImage(imageSource, question);
   

3. Report findings — Present the analysis in a structured format

Workflow 4: Batch Generation

When the user needs multiple images:

1. Parse the batch request — Understand variations needed
2. Generate in parallel — Call generateImage() for each variant
3. Organize results — Save with descriptive filenames

Prompt Enhancement Rules

When generating images, automatically enhance the user's prompt:

Quality Boosters (always append unless user specifies quality)

text

professional quality, high resolution, sharp details

Context-Based Additions

User Intent	Auto-Add
Product photo	"studio lighting, clean background, commercial photography"
Portrait	"professional portrait photography, natural lighting"
Social media	"eye-catching, vibrant colors, modern design"
Illustration	"detailed illustration, professional artist quality"
Logo/branding	"clean vector style, scalable, minimal details"
Architecture	"architectural visualization, realistic rendering"
Food	"appetizing, food styling, professional food photography"
UI mockup	"clean design, modern interface, pixel-perfect"

Size Recommendations

Use Case	Recommended Size
Social media post	1024x1024 (square)
Story/vertical	1024x1792
Banner/landscape	1792x1024
Product listing	1024x1024
Presentation	1792x1024
Wallpaper	1792x1024

Style Presets

Quick style references for common requests:

Preset Name	Style Description
product	Clean white background, studio lighting, commercial photography
lifestyle	Natural setting, warm lighting, aspirational mood
minimalist	Simple composition, negative space, clean lines
vintage	Retro color grading, film grain, nostalgic mood
futuristic	Neon accents, dark background, sci-fi aesthetic
watercolor	Soft edges, pastel palette, artistic brush strokes
3d-render	Octane render, realistic materials, dramatic lighting
anime	Japanese animation style, vibrant, expressive
sketch	Pencil drawing, hand-drawn, artistic
flat-design	Vector style, bold colors, geometric shapes

API Reference

generateImage(prompt, options)

Generate a new image from text description.

Parameters:

• prompt (string) — Image description (auto-enhanced by this skill)
• options (object):
  • size — 1024x1024 | 1024x1792 | 1792x1024 (default: 1024x1024)
  • quality — standard | hd (default: standard)
  • style — vivid | natural (default: vivid)
  • model — gpt-image-2 | dall-e-3 (default: gpt-image-2)
  • saveTo — File path to save the image (default: ./generated-images/)

Returns: { success, url, localPath, revisedPrompt }

editImage(imagePath, prompt, options)

Edit an existing image with natural language instructions.

Parameters:

• imagePath (string) — Path to the source image
• prompt (string) — Edit instruction
• options (object):
  • mask — Path to mask image (white = edit area, black = keep)
  • size — Output size
  • model — gpt-image-2 | dall-e-3 (default: gpt-image-2)

Returns: { success, url, localPath }

generateVariations(imagePath, options)

Generate creative variations of an existing image.

Parameters:

• imagePath (string) — Path to the source image
• options (object):
  • count — Number of variations 1-4 (default: 2)
  • size — Output size

Returns: { success, variations: [{ url, localPath }] }

describeImage(imageSource, question)

Analyze an image using GPT-4o Vision.

Parameters:

• imageSource (string) — File path or URL of the image
• question (string|null) — Specific question about the image (default: general description)

Returns: { success, description }

downloadImage(url, savePath)

Download a generated image to local storage.

Parameters:

• url (string) — Image URL from generation API
• savePath (string|null) — Local file path (default: auto-generated in ./generated-images/)

Returns: { success, localPath }

Error Handling

Error	Cause	Resolution
Invalid API key	OPENAI_API_KEY not set or invalid	Check environment variable
Content policy violation	Prompt violates safety guidelines	Rephrase the prompt
Rate limit exceeded	Too many requests	Wait and retry with backoff
Image too large	Source image exceeds size limit	Resize to under 4MB
Timeout	Generation took too long	Simplify prompt or retry

Best Practices

1. Always enhance prompts — Don't pass raw user input directly to the API
2. Save locally — Download generated images; URLs expire after 1 hour
3. Use appropriate sizes — Match the output size to the use case
4. Prefer gpt-image-2 — Better quality and text rendering than dall-e-3
5. Batch thoughtfully — Generate 2-4 images max per request to avoid rate limits
6. Describe edits clearly — Be specific about what to change and where

Changelog

v1.1.0

• Added GPT-4o native image generation support (gpt-image-2 model)
• Added automatic prompt enhancement workflow
• Added image download and local save functionality
• Added style presets for quick reference
• Added batch generation workflow
• Improved error handling and documentation

v1.0.0

• Initial release with DALL-E 3 support
• Basic generate, edit, variations, and describe functions

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Tags: image-generation AI-art GPT-4o image-2 gpt-image-2 visual-creation marketing product-photos illustration design openai dall-e image-editing background-removal style-transfer ui-mockup