Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.

Image Generation Skill Overview This skill generates high quality images using structured prompts and a Python script. The workflow includes creating JSON formatted prompts and executing image generation with optional reference images. Core Capabilities Create structured JSON prompts for AIGC image generation Support multiple reference images for style/composition guidance Generate images through automated Python script execution Handle various image generation scenarios (character design, scenes, products, etc.) Workflow Step 1: Understand Requirements When a user requests image generation, identify: Subject/content: What should be in the image Style preferences: Art style, mood, color palette Technical specs: Aspect ratio, composition, lighting Reference images: Any images to guide generation You don't need to check the folder under /mnt/user data Step 2: Create Structured Prompt Generate a structured JSON file in /mnt/user data/workspace/ with naming pattern: {descriptive name}.json Step 3: Execute Generation Call the Python script: Parameters: prompt file : Absolute path to JSON prompt file (required) reference images : Absolute paths to reference images (optional, space separated) output file : Absolute path to output image file (required) aspect ratio : Aspect ratio of the generated image (optional, default: 16:9) [!NOTE] Do NOT read the python file, just call it with the parameters. Character Generation Example User request: "Create a Tokyo street style woman character in 1990s" Create prompt file: /mnt/user data/workspace/asian woman.json Execute generation: With reference images: Common Scenarios Use different JSON schemas for different scenarios. Character Design : Physical attributes (gender, age, ethnicity, body type) Facial features and expressions Clothing and accessories Historical era or setting Pose and context Scene Generation : Environment description Time of day, weather Mood and atmosphere Focal points and composition Product Visualization : Product details and materials Lighting setup Background and context Presentation angle Specific Templates Read the following template file only when matching the user request. [Doraemon Comic](templates/doraemon.md) Output Handling After generation: Images are typically saved in /mnt/user data/outputs/ Share generated images with user using present files tool Provide brief description of the generation result Offer to iterate if adjustments needed Tips: Enhancing Generation with Reference Images For scenarios where visual accuracy is critical, use the image search tool first to find reference images before generation. Recommended scenarios for using image search tool: Character/Portrait Generation : Search for similar poses, expressions, or styles to guide facial features and body proportions Specific Objects or Products : Find reference images of real objects to ensure accurate representation Architectural or Environmental Scenes : Search for location references to capture authentic details Fashion and Clothing : Find style references to ensure accurate garment details and styling Example workflow: 1. Call the image search tool to find suitable reference images: 2. Download the returned image URLs to local files 3. Use the downloaded images as reference images parameter in the generation script This approach significantly improves generation quality by providing the model with concrete visual guidance rather than relying solely on text descriptions. Providers (Gemini / MiniMax / OpenAI compatible) This skill auto selects the provider by environment variables (no CLI change): GEMINI API KEY set → use Gemini (default, unchanged). Otherwise, MINIMAX API KEY set → use MiniMax ( /v1/image generation , model image 01 ). Otherwise, IMAGE GENERATION API KEY set → use an OpenAI compatible Images API. Force one explicitly with IMAGE GENERATION PROVIDER=gemini minimax openai . openai compatible is also accepted as an alias for openai . OpenAI compatible settings: IMAGE GENERATION API KEY (required) IMAGE GENERATION BASE URL (default https://api.openai.com/v1 ) IMAGE GENERATION MODEL (default gpt image 2.5 flare ) IMAGE GENERATION SIZE (optional fixed size override) Text to image calls use POST {base url}/images/generations . Reference image calls use multipart POST {base url}/images/edits ; a relay may support generation without supporting edits. Responses may contain base64 image data, a data URL, or a downloadable URL. Aspect ratios map to 1024x1024 , 1536x1024 , or 1024x1536 unless IMAGE GENERATION SIZE is set. The output extension selects the API output format : .jpg / .jpeg uses jpeg , .webp uses webp , and all other extensions use png . When dall e 2 or dall e 3 is configured instead, the request uses the model's supported dimensions and response format=b64 json ; DALL E output files must use a .png extension. Reference image editing with DALL E models is not supported by this skill; use the default GPT Image model for edits. MiniMax optional overrides: MINIMAX API HOST (default https://api.minimaxi.com ), MINIMAX IMAGE MODEL (default image 01 ). Reference images are sent as the MiniMax subject reference character image. The CLI and prompt file / reference images / output file / aspect ratio arguments are identical for both providers. MiniMax prompt handling (provider internal). Authoring is provider agnostic — write the same structured JSON regardless of which provider is active. MiniMax image 01 consumes a single text string, so the MiniMax path itself sends only the JSON prompt field (the other fields such as style / composition / negative prompt apply to the Gemini path) and enables prompt optimizer so MiniMax expands it server side. MiniMax caps that prompt at 1500 characters; if the prompt field is longer, the script returns an error instead of calling the API. The Gemini path receives the full structured JSON. Notes Always use English for prompts regardless of user's language JSON format ensures structured, parsable prompts Reference images enhance generation quality significantly Iterative refinement is normal for optimal results For character generation, include the detailed character object plus a consolidated prompt field

image-generation