name: image-2 version: 1.1.0 description: "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." metadata: openclaw: emoji: "🎨" homepage: "https://clawhub.ai/gpt/image-2" always: false skillKey: "image-2" requires: env: - OPENAI_API_KEY primaryEnv: OPENAI_API_KEY install: - kind: node package: openai bins: []

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:
2. Append professional photography/art terms based on context
3. Add lighting, composition, and mood details if not specified

4. Specify output format and dimensions if needed

5. Call the API — Use generateImage() with the enhanced prompt: javascript const result = await generateImage(enhancedPrompt, { size, quality, style });

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

7. Save to ./generated-images/ by default
8. 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)

professional quality, high resolution, sharp details

Context-Based Additions

Size Recommendations

Style Presets

Quick style references for common requests:

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

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

这个 Skill 质量不错，文档写得很详细，图片生成流程完整，使用起来比较省心。初始化配置方便，支持批量生成和参考图上传，轮询任务带进度显示。价格策略也比较灵活。不足之处是依赖项只有一个 requests，缺少使用验证机制，新手遇到问题时可能需要自行排查。