A specialized image generation skill leveraging xAI Aurora to produce high-resolution visuals from text descriptions and reference images.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install grok-imagine-openapi-skill
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install grok-imagine-openapi-skill using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Grok Imagine skill is a robust integration designed for the xAI Aurora model, providing developers and AI agents the ability to generate high-fidelity imagery directly through an API. As part of the growing ecosystem of Openclaw Skills, it bridges the gap between text-based reasoning and visual creation, allowing for batch processing of up to 16 images in a single execution.
Built on the WeShop OpenAPI infrastructure, this skill prioritizes security and performance. It enables seamless image synthesis by supporting both pure text-to-image prompts and image-to-image workflows using reference URLs, making it an essential tool for automated content creation and design prototyping.
To integrate this into your environment, ensure you have a valid API key from the WeShop platform.
export WESHOP_API_KEY='your_api_key_here'
Note: This skill communicates exclusively with openapi.weshop.ai. Ensure your network configuration allows HTTPS access to this domain.
The skill utilizes a structured request/response format for reliable image generation.
| Field | Type | Description |
|---|---|---|
| textDescription | string | The primary prompt describing the desired image. |
| batchCount | integer | Number of images (1-16) to generate in one run. |
| images | array | Optional list containing a single reference image URL. |
Results are organized by executionId with the following status taxonomy:
data.executions[*].result[*].image.Loading
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