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Virtual Try On
by
wangyang-youloft
· GitHub ↗
· v1.0.0
· MIT-0
148
Downloads
0
Stars
0
Active Installs
1
Versions
Install in OpenClaw
/install virtual-try-on
Description
Convert clothing images into professional e-commerce photos by virtually dressing AI models with up to four garment images for online retail use.
Usage Guidance
This skill will send any submitted image URLs and an API key to an external service (api.ngmob.com). Before installing or using it: (1) ask the author to declare the required env var (API_KEY) in the skill metadata and explain the key's required scope/permissions; (2) verify the service provenance (official homepage, company, support/contact info) and confirm api.ngmob.com is the legitimate endpoint; (3) avoid uploading private/sensitive images; use public or anonymized examples; (4) create a limited-scope API key (least privilege) and monitor its usage; (5) ask the author to document data retention, privacy, and whether images are stored or used to train models; and (6) prefer skills with consistent owner metadata and non-placeholder author fields. The current metadata inconsistencies (missing required credential declaration, no homepage, mismatched owner IDs/placeholder author) are the primary reasons for caution.
Capability Analysis
Package: virtual-try-on (xpi)
Version: 1.0.0
Description: Transform clothing images into e-commerce product photos with AI models wearing the garments
The package is a declarative configuration for an AI-powered 'Virtual Try-On' workflow. It defines API endpoints, request structures, and polling mechanisms for interacting with the Pixify AI service (api.ngmob.com). It contains no executable code, shell scripts, or logic for exfiltrating sensitive data.
Capability Assessment
Purpose & Capability
The skill description and SKILL.md describe an API-based virtual try-on service (api.ngmob.com), which reasonably requires an API key. However, the registry metadata declares no required environment variables or primary credential while the manifest and SKILL.md explicitly use Authorization: Bearer {{API_KEY}} / $API_KEY. Additionally there is no homepage/source and owner IDs/authorship are inconsistent (manifest/_meta/registry show mismatched or placeholder values), reducing provenance and trust.
Instruction Scope
Instructions are scoped to sending user-provided clothing image URLs to https://api.ngmob.com and polling for results — this matches the declared purpose. However the instructions require an API_KEY (curl examples) that is not declared elsewhere, and they will transmit user images to an external service (data exfiltration/privacy risk if images are sensitive). The skill does not document data retention, privacy, or what is sent beyond the image URLs.
Install Mechanism
This is an instruction-only skill with no install spec and no code files, so it does not write code to disk or download external binaries. That lowers installation risk.
Credentials
The manifest and SKILL.md expect an API key (Authorization: Bearer {{API_KEY}} / $API_KEY) but requires.env/primary credential fields are empty. This mismatch is problematic: the skill will fail or will silently rely on an implicitly provided key. The skill asks for a high-sensitivity secret (API_KEY) without declaring scope, usage, or least-privilege recommendations.
Persistence & Privilege
always is false and disable-model-invocation is not set; the skill does not request persistent system-wide privileges or modify other skills. There is no install-time persistence specified.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install virtual-try-on - After installation, invoke the skill by name or use
/virtual-try-on - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of the virtual-try-on skill:
- Transform up to 4 clothing images into AI-generated product photos on professional models.
- Uses the Pixify engine, combining garment analysis and model try-on generation.
- Ideal for fashion e-commerce, design visualization, and rapid catalog creation.
- Clearly outlined API usage with sample workflows and status polling.
- No dependency on upload order; workflow automatically combines garment components.
Metadata
Frequently Asked Questions
What is Virtual Try On?
Convert clothing images into professional e-commerce photos by virtually dressing AI models with up to four garment images for online retail use. It is an AI Agent Skill for Claude Code / OpenClaw, with 148 downloads so far.
How do I install Virtual Try On?
Run "/install virtual-try-on" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Virtual Try On free?
Yes, Virtual Try On is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Virtual Try On support?
Virtual Try On is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Virtual Try On?
It is built and maintained by wangyang-youloft (@wangyang-youloft); the current version is v1.0.0.
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