A professional AI-driven suite for e-commerce image generation, featuring model replacement, virtual try-ons, and product background editing.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install weshop-openapi-skill
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 weshop-openapi-skill 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 WeShop AI Image & Fashion Generation?
The WeShop Agent OpenAPI Integration provides a sophisticated interface for high-end image manipulation and generation tasks. Specifically designed for fashion and e-commerce, it enables AI agents to execute complex visual edits such as swapping models, changing poses, and expanding image edges with natural-looking content. By utilizing Openclaw Skills, developers can programmatically access state-of-the-art visual AI that maintains garment integrity while transforming the surrounding context.
This skill serves as a bridge to the WeShop OpenAPI, allowing for seamless integration of specialized agents like aimodel and virtualtryon into automated content pipelines. It is an essential tool for those looking to scale visual content production without the overhead of traditional photography and manual editing.
WeShop AI Image & Fashion Generation Use Cases
- Automating fashion model replacement to show apparel on different ethnicities and body types.
- Generating high-quality product lifestyle images from simple still-life photos for e-commerce listings.
- Implementing virtual try-on workflows where clothing from one image is mapped onto a model in another.
- Expanding product images to specific aspect ratios for social media using AI-powered edge filling.
- Batch processing background removal and color replacement for large product catalogs.
How WeShop AI Image & Fashion Generation Works
- The workflow begins by uploading a local image to the WeShop asset server to generate a publicly accessible URL.
- Users select a specialized agent (such as aiproduct or aipose) based on the specific transformation required.
- Detailed parameters are defined, including masking rules that tell the AI which parts of the image to protect (like the garment) and which to change.
- A run request is submitted to the API, which returns a unique execution handle.
- The system polls the execution status endpoint until the AI generation is complete.
- The final generated images are retrieved from the result payload and delivered to the user.
WeShop AI Image & Fashion Generation Setup
To use this integration within Openclaw Skills, you must obtain an API key from the WeShop developer portal. Configure your environment by setting the following variable:
export WESHOP_API_KEY='your_api_key_value'
Ensure your network configuration allows HTTPS requests to openapi.weshop.ai. Note that the API key should be used directly in the Authorization header without a 'Bearer' prefix.
WeShop AI Image & Fashion Generation Data Schema & Taxonomy
The skill follows a structured data contract for both requests and responses to ensure reliability across different Openclaw Skills implementations.
| Component | Description |
|---|---|
| Unified Envelope | All responses include a success boolean and a meta.executionId for tracking. |
| Agent Config | Defines the specific AI model being used (e.g., aimodel v1.0). |
| Input Object | Contains the originalImage URL and optional taskName. |
| Params Object | Houses agent-specific instructions like maskType, locationId, and textDescription. |
| Execution Result | Provides a status (Pending, Running, Success) and an array of resulting image URLs. |
WeShop AI Image & Fashion Generation Advanced Features
- Support for sophisticated segmentation masks including auto-apparel, auto-human, and custom user-defined masks.
- Flexible pose control allowing agents to keep the original pose, adopt a reference pose, or generate a free-form pose.
- High-volume batch generation allowing up to 16 image variations per single execution request.
- Resolution and aspect ratio controls for professional output standards including 1K, 2K, and 4K options.
- Integration of callback URLs for asynchronous notification upon task completion, ideal for large-scale automation.
SKILL.md
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