reducedmatrice/badmintoncoachagent/image-generation
Claude Code and agent skill by reducedmatrice/badmintoncoachagent
- community
Checking that the SKILL.md file is still in the repository.
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.
Install
npx vskill@latest install reducedmatrice/badmintoncoachagent/image-generationSKILL.md
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.
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:
- Call the
image_searchtool to find suitable reference images:
`` image_search(query="Japanese woman street photography 1990s", size="Large") ``
- Download the returned image URLs to local files
- Use the downloaded images as
--reference-imagesparameter 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.
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
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/public/image-generation/SKILL.md
Popularity
- Repository stars
- 21
- Repository forks
- 1
- 7-day trend score
- 15
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 7 Aug 2026, 2 months ago.
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