techwavedev Imagen - AI Image Generation Skill
Overview
This skill generates images using Google Gemini's image generation model (gemini-3-pro-image-preview). It enables seamless image creation during any Claude Code session - whether you're building frontend UIs, creating documentation, or need visual representations of concepts.
Cross-Platform: Works on Windows, macOS, and Linux.
When to Use This Skill
Automatically activate this skill when:
- User requests image generation (e.g., "generate an image of...", "create a picture...")
- Frontend development requires placeholder or actual images
- Documentation needs illustrations or diagrams
- Visualizing concepts, architectures, or ideas
- Creating icons, logos, or UI assets
- Any task where an AI-generated image would be helpful
How It Works
- Takes a text prompt describing the desired image
- Calls Google Gemini API with image generation configuration
- Saves the generated image to a specified location (defaults to current directory)
- Returns the file path for use in your project
Usage
Python (Cross-Platform - Recommended)
# Basic usage
python scripts/generate_image.py "A futuristic city skyline at sunset"
# With custom output path
python scripts/generate_image.py "A minimalist app icon for a music player" "./assets/icons/music-icon.png"
# With custom size
python scripts/generate_image.py --size 2K "High resolution landscape" "./wallpaper.png"
Requirements
GEMINI_API_KEYenvironment variable must be set- Python 3.6+ (uses standard library only, no pip install needed)
Output
Generated images are saved as PNG files. The script returns:
- Success: Path to the generated image
- Failure: Error message with details
Examples
Frontend Development
User: "I need a hero image for my landing page - something abstract and tech-focused"
-> Generates and saves image, provides path for use in HTML/CSS
Documentation
User: "Create a diagram showing microservices architecture"
-> Generates visual representation, ready for README or docs
UI Assets
User: "Generate a placeholder avatar image for the user profile component"
-> Creates image in appropriate size for component use
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Imagen"
Storing Results
After completing work, store AI agent orchestration decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
--type decision --project <project> \
--tags imagen ai-agents
Multi-Agent Collaboration
This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
--project <project>
Control Tower Integration
Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.
Blockchain Identity
Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.