image-generation

name	image-generation
description	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.

For scientific illustrations such as graphical abstracts, mechanism illustrations, concept explainers, or paper cover art, read /mnt/skills/public/scientific-image-prompting/SKILL.md first. That skill decides whether the request is allowed to use AIGC at all and defines the paper-ready prompt contract.

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 1.5: Reject Wrong Scientific Route

If the request is actually a real data figure, do not use this skill as the primary renderer.

Examples that must stay out of AIGC image generation:

• ROC / PR curves
• heatmaps
• volcano plots
• PCA / UMAP / t-SNE
• confusion matrices
• bar charts / line charts / histograms driven by real values

Route those requests to validated plotting workflows instead.

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:

python /mnt/skills/public/image-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/prompt-file.json \
  --reference-images /path/to/ref1.jpg /path/to/ref2.png \
  --output-file /mnt/user-data/outputs/generated-image.jpg \
  --aspect-ratio 16:9

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)
• --provider: Optional image provider override (google-ai-studio or openai-compatible)
• --model: Optional image model override
• --base-url: Optional OpenAI-compatible image base URL override
• --image-size: Optional 1K, 2K, or 4K
• --output-mime-type: Optional image/png or image/jpeg
• --scientific-mode: Enables scientific illustration guardrails and 4K/PNG defaults
• --manifest-file: Optional manifest output path for generation audit
• --draft-mode: Lower-cost mode when no explicit model is provided

[!NOTE] Do NOT read the python file, just call it with the parameters. If the user has already configured an active image provider in Settings, use that provider/model by default and do not hardcode Gemini unless the user explicitly asked for Google AI Studio.

Scientific Illustration Mode

When the request is a scientific illustration that passed the route check:

1. Read /mnt/skills/public/scientific-image-prompting/SKILL.md
2. Produce prompt.json using that contract
3. Generate the image in scientific mode
4. Keep the final output conceptual, not quantitative

Recommended command:

python /mnt/skills/public/image-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/outputs/prompt.json \
  --output-file /mnt/user-data/outputs/scientific-illustration-4k.png \
  --manifest-file /mnt/user-data/outputs/generation_manifest.json \
  --aspect-ratio 16:9 \
  --scientific-mode \
  --image-size 4K \
  --output-mime-type image/png

Only add --provider, --model, or --base-url when the user explicitly wants to override the configured default provider.

Character Generation Example

User request: "Create a Tokyo street style woman character in 1990s"

Create prompt file: /mnt/user-data/workspace/asian-woman.json

{
  "characters": [{
    "gender": "female",
    "age": "mid-20s",
    "ethnicity": "Japanese",
    "body_type": "slender, elegant",
    "facial_features": "delicate features, expressive eyes, subtle makeup with emphasis on lips, long dark hair partially wet from rain",
    "clothing": "stylish trench coat, designer handbag, high heels, contemporary Tokyo street fashion",
    "accessories": "minimal jewelry, statement earrings, leather handbag",
    "era": "1990s"
  }],
  "negative_prompt": "blurry face, deformed, low quality, overly sharp digital look, oversaturated colors, artificial lighting, studio setting, posed, selfie angle",
  "style": "Leica M11 street photography aesthetic, film-like rendering, natural color palette with slight warmth, bokeh background blur, analog photography feel",
  "composition": 
   
   

Execute generation:

python /mnt/skills/public/image-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/cyberpunk-hacker.json \
  --output-file /mnt/user-data/outputs/cyberpunk-hacker-01.jpg \
  --aspect-ratio 2:3

With reference images:

{
  "characters": [{
    "gender": "based on [Image 1]",
    "age": "based on [Image 1]",
    "ethnicity": "human from [Image 1] adapted to Star Wars universe",
    "body_type": "based on [Image 1]",
    "facial_features": "matching [Image 1] with slight weathered look from space travel",
    "clothing": "Star Wars style outfit - worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with holster",
    "accessories": "blaster pistol on hip, comlink device on wrist, goggles pushed up on forehead, satchel with supplies, personal vehicle based on [Image 2]",
    "era": "Star Wars universe, post-Empire era"
  }],
  "prompt": "Character inspired by [Image 1] standing next to a vehicle inspired by [Image 2] on a bustling alien planet street in Star Wars universe aesthetic. Character wearing worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with blaster holster. The vehicle adapted to Star Wars aesthetic with weathered metal panels, repulsor engines, desert dust covering, parked on the street. Exotic alien marketplace street with multi-level architecture, weathered metal structures, hanging market stalls with colorful awnings, alien species walking by as background characters. Twin suns casting warm golden light, atmospheric dust particles in air, moisture vaporators visible in distance. Gritty lived-in Star Wars aesthetic, practical effects look, film grain texture, cinematic composition."
   
   
   
   
   
   
     
     
     
  

python /mnt/skills/public/image-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/star-wars-scene.json \
  --reference-images /mnt/user-data/uploads/character-ref.jpg /mnt/user-data/uploads/vehicle-ref.jpg \
  --output-file /mnt/user-data/outputs/star-wars-scene-01.jpg \
  --aspect-ratio 16:9

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

Output Handling

After generation:

• Images are typically saved in /mnt/user-data/outputs/
• In scientific mode, also preserve generation_manifest.json
• 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:

   image_search(query="Japanese woman street photography 1990s", size="Large")
   

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.

Quality Standards (Mandatory)

Before Generation — Clarify Requirements

If ANY of the following are unclear, call ask_clarification BEFORE generating:

• What is the subject/scene? (character, product, environment, abstract)
• What style? (photorealistic, illustration, anime, oil painting, etc.)
• What mood/atmosphere? (dramatic, warm, mysterious, professional)
• What is the usage context? (social media, print, presentation, avatar)
• Aspect ratio preference? (16:9 landscape, 9:16 portrait, 1:1 square)

Image Prompt Checklist

Apply these rules when creating the JSON prompt:

1. Prompt length: Minimum 150 words for any non-trivial image. Complex scenes need 200-300 words.
2. Structure: Always include [Subject] + [Action/Pose] + [Environment] + [Style] + [Lighting] + [Color palette] + [Technical specs].
3. Specificity: Use exact descriptions, not vague terms. "warm golden hour sunlight at 15° angle" not "nice lighting". "#667eea deep purple" not "purple".
4. Negative prompt: Always include negative_prompt to exclude unwanted elements (blur, deformation, low quality, etc.).
5. Reference images: For characters, products, or specific styles — use image_search tool first to find references, then pass via --reference-images.
6. Composition: Specify camera angle, framing (close-up/medium/wide), depth of field, and rule of thirds placement.
7. Style consistency: For image series, reference previous outputs and explicitly state "match the style of the reference image exactly".

After Generation — Self-Review

Before presenting the image to the user:

• Does the image match the requested subject and style?
• Is the composition balanced with a clear focal point?
• Are colors harmonious and appropriate for the mood?
• Is the quality sufficient? (no artifacts, blur, or deformation)
• For series: is it visually consistent with previous images?

If quality is insufficient, refine the prompt (add more detail, adjust negative_prompt) and regenerate. Max 2 retries before presenting to user with notes on what could be improved.

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