A comprehensive image generation skill for Openclaw Skills that automates Midjourney rendering, 4K upscaling, and atmospheric scene transformations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install airilab
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install airilab using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Airilab AI Image Generation Skill?
Airilab is a professional-grade integration designed to provide AI agents with direct access to high-end image generation and task management capabilities. This skill encapsulates complex multi-step SaaS workflows into single conversational commands, allowing agents to handle authentication, project selection, and asynchronous job tracking without user intervention. By leveraging this tool within the Openclaw Skills framework, developers can enable agents to generate Midjourney-quality visuals, perform detail-enhanced upscaling, and execute rapid atmospheric conversions.
Built for reliability, the skill features a dedicated background worker that polls for job completion and pushes results back to the user interface. It streamlines the creative process by managing all technical overhead, from SMS-based login to persistent project context, making it an essential component for any creative or design-focused AI agent.
Airilab AI Image Generation Skill Use Cases
- Creating high-quality architectural concept renders and visual effects using Midjourney.
- Enhancing low-resolution images into 4K assets with improved texture and clarity.
- Transforming day scenes into night or switching seasonal atmospheres (e.g., sunny to snowy) for design proposals.
- Managing high-volume image generation jobs asynchronously via automated background workers.
How Airilab AI Image Generation Skill Works
- The AI agent detects an image-related intent, such as a request for MJ rendering or 4K upscaling.
- The skill performs a prerequisite check to ensure the user is logged in and a project context is active.
- Upon intent confirmation, the skill maps the request to a specific workflow (ID 0 for MJ, 16 for Upscale, or 13 for Atmosphere).
- The task is submitted to the Airilab API, and the unique jobId is stored in a local SQLite database.
- A background worker (scheduler/worker.py) polls the API status until the task is marked as completed.
- Once finished, the skill fetches the result URLs and provides them to the user through the Openclaw Skills interface.
Airilab AI Image Generation Skill Setup
To get started with Airilab for Openclaw Skills, follow these installation steps:
# Install required Python dependencies
python -m pip install -r ~/.openclaw/skills/airilab/requirements.txt
# Run the post-installation script to initialize workers
~/.openclaw/skills/airilab/scripts/post-install.sh
# Perform a health check to verify token and project status
~/.openclaw/skills/airilab/scripts/health.sh
Airilab AI Image Generation Skill Data Schema & Taxonomy
The skill organizes its configuration and task data within a persistent runtime directory, typically located at ~/.openclaw/skills/airilab or defined by the AIRILAB_HOME environment variable.
| Path | Purpose |
|---|---|
config/.env |
Stores session tokens and persistent authentication data. |
config/project_config.json |
Maintains the active project ID and team context. |
scheduler/jobs.db |
Tracks the status and metadata of asynchronous image tasks. |
scheduler/worker.log |
Records logs for the background job manager. |
Airilab AI Image Generation Skill Advanced Features
- Multi-language creative descriptions supporting English, Chinese, and French for global workflows.
- Dedicated background worker for reliable long-polling of high-latency rendering tasks.
- Robust error handling for missing tokens, project timeouts, and API failures.
- Seamless multi-image support allowing up to 4 concurrent renders per MJ request.
SKILL.md
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