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virtual-try-on虚拟试穿

Agent Skill

virtual-try-on 用于处理图像、截图、视觉识别或图片素材相关工作,适合在 OpenClaw 中需要让 Agent 分析图片、整理视觉素材或辅助图像流程时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

4,339

周安装

179

GitHub Stars

公开资料未说明

下载量

1,418
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:virtual-try-on(虚拟试穿)
来源仓库:https://github.com/wangyang-youloft/virtual-try-on
安装命令:
openclaw skills install virtual-try-on
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install virtual-try-on

简介

通过使用最多四张服装图像进行虚拟服装人工智能模型,将服装图像转换为专业的电子商务照片,以供在线零售使用。

SKILL.md

Skills.md

Skill Name

virtual-try-on


Description

Virtual Try-On — Transform clothing images into professional e-commerce product photos with AI models wearing the garments. Upload up to 4 clothing/garment images and receive a high-quality product photo ready for online retail platforms.

It leverages the Pixify engine to process your inputs through:

  • Garment Analysis (image_to_text_gpt5)
  • Model Try-On Generation (nano_banana_pro)

Service Overview

  • 🌐 Product Website / Console:

https://ai.ngmob.com *(For product access, workflow management, and obtaining your API Key)*

  • 🔗 API Base URL:

https://api.ngmob.com *(Used strictly for API requests and workflow execution)*


Use Cases

  • Fashion E-Commerce: Generate product photos for online clothing stores
  • Design Visualization: See how designs look on models before production
  • Catalog Creation: Quickly create professional product catalogs
  • Multi-Variant Display: Generate product photo variations
  • Retail Preparation: Prepare images for marketplace listings

Inputs

NameTypeRequiredDescription
Clothing Image 1string (URL)Top/Shirt — Clothing item image (shirt, blouse, jacket, etc.)
Clothing Image 2string (URL)Bottom/Pants — Clothing item image (pants, skirt, shorts, etc.)
Clothing Image 3string (URL)Accessories/Shoes — Accessory or footwear image (shoes, bag, hat, etc.)
Clothing Image 4string (URL)Additional Item — Additional clothing or accessory image (optional)

⚠️ Important: Clothing Component Types

  • Image 1 (Required): Top/shirt/jacket or any upper body garment
  • Image 2 (Optional): Bottom/pants/skirt or any lower body garment
  • Image 3 (Optional): Accessories/shoes or footwear
  • Image 4 (Optional): Additional clothing items or accessories
  • Upload order doesn't matter - workflow automatically identifies and combines components

How to Use

When the user requests to execute this workflow, follow these steps:

1. Collect Input Parameters

Gather the required inputs from the user:

  • At least 1 clothing/garment image (required)
  • Up to 3 additional clothing images (optional)

2. Call the Workflow API

curl -X POST https://api.ngmob.com/api/v1/workflows/2IIk3Z6NKuPZP7moonEI/run \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": {
      "Clothing Image 1": "https://example.com/shirt.png",
      "Clothing Image 2": "https://example.com/pants.png",
      "Clothing Image 3": "https://example.com/jacket.png",
      "Clothing Image 4": "https://example.com/accessories.png"
    }
  }'

3. Poll Task Status (Recommended: every 3–5 seconds)

Use the returned task_id to query task status:

curl https://api.ngmob.com/api/v1/workflows/executions/{task_id} \
  -H "Authorization: Bearer $API_KEY"

Preview

Input Clothing Images

Clothing 1Clothing 2Clothing 3Clothing 4
Clothing 1Clothing 2Clothing 3Clothing 4

Generated E-Commerce Product Photos

Product Photo 1Product Photo 2Product Photo 3
Result 1Result 2Result 3

Example (Recommended)

{
  "Clothing Image 1": "https://example.com/shirt.png",
  "Clothing Image 2": "https://example.com/pants.png",
  "Clothing Image 3": "https://example.com/jacket.png",
  "Clothing Image 4": "https://example.com/accessories.png"
}

What happens:

  1. The workflow analyzes each clothing image's design and characteristics
  2. AI models are dressed with your garments
  3. A professional e-commerce product photo is generated
  4. You receive an image ready for online retail platforms

🤖 Generated with Pixify

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

73.19%
按下载量换算1,038

安全审计

VirusTotal

通过

ClawScan

可疑

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通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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