Image-2 Skill

> 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

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

```javascript

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

```

1. 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 });

```

1. 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);

```

1. 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)

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

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