Token导航 LogoToken导航TokenDH.com
研究检索需要联网clawhub未标认证来源可访问clear审计提醒

online-price-comparison网上价格比较

Agent Skill

online-price-comparison 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

3,326

周安装

140

GitHub Stars

1

下载量

1,165
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:online-price-comparison(网上价格比较)
来源仓库:https://github.com/archroad/online-price-comparison
安装命令:
openclaw skills install online-price-comparison
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 online-price-comparison

简介

人工智能驱动的购物助手。通过文本或图像搜索产品,并在 Amazon、Google Shopping 和品牌商店中找到最优惠的价格。

SKILL.md

name
shopgeni
description
>
version
1.0.0
user-invocable
true
tags
[shopping, fashion, price-comparison, recommendations, ai]
metadata
openclaw
requires
bins
emoji
🛍️
os
[darwin, linux]

ShopGeni — AI Shopping Assistant

ShopGeni exposes two AI-powered shopping capabilities:

  1. Item Recommendation — natural-language and visual product search
  2. Price Comparison — find the best price for a product across Amazon, Google Shopping, and brand stores

1. Item Recommendation

Search for products using natural language or a product image.

Triggers: "find me a dress", "show sneakers under $100", "recommend a blue denim jacket", "what shoes match this outfit?"

Text search:

python3 $SKILL_PATH/scripts/shopgeni.py \
  --query "blue running shoes under $120"

Visual search (image file):

python3 $SKILL_PATH/scripts/shopgeni.py \
  --image "/path/to/photo.jpg" \
  --query "find similar products"

2. Price Comparison

Find the best price for a specific product across multiple stores.

Triggers: "find best price for X", "compare prices for X", "where can I buy X cheapest"

Text query:

python3 $SKILL_PATH/scripts/shopgeni.py \
  --query "find best price for Nike Air Force 1 white"

From a product URL: Extract the product name and brand from the URL yourself, then pass as --query. Do not pass URLs to the script — the backend searches by keyword.

Query construction: Build the richest possible query from available product attributes — include brand, product name, and any of: style number, color, gender, category. More specific queries yield better results. Examples:

  • "find best price for Reebok Women's Zignition Running Shoes Black/White"
  • "find best price for Nike Air Force 1 Low Men's Sneaker White style 100074219"
  • "find best price for Adidas Response Super Women's Running Shoes"

With image URL (recommended for better accuracy): Pass --image-url with the product image URL. The backend uses it for visual similarity ranking. If the user searched for products first, use the image field from that recommendation:

python3 $SKILL_PATH/scripts/shopgeni.py \
  --query "find best price for Reebok Women's Zignition Running Shoes Black/White" \
  --image-url "https://example.com/product-image.jpg"

From a local image file:

python3 $SKILL_PATH/scripts/shopgeni.py \
  --image "/path/to/product.jpg" \
  --query "find best prices for this product"

Follow-up Queries

Pass --thread-id to continue a conversation:

python3 $SKILL_PATH/scripts/shopgeni.py \
  --query "show me similar ones in red" \
  --thread-id "previous-thread-uuid"

Response JSON

{
  "intent": "item | price_comparison",
  "content": "assistant response text",
  "thread_id": "uuid",
  "recommendations": [
    {
      "id": "...",
      "name": "Product Name",
      "brand": "Brand",
      "merchant": "Store Name",
      "price": "$99.99",
      "image": "https://...",
      "category": "shoes",
      "product_url": "https://www.beyondstyle.us/prod?id=..."
    }
  ],
  "price_comparison": {
    "candidates": [
      {
        "name": "Product Name",
        "price": "$89.99",
        "source": "Amazon",
        "buy_url": "https://..."
      }
    ]
  }
}

Display Guidelines

  • Item recommendations: Show as a markdown table or bullet list with name, brand, price, and link
  • Price comparison: Show as a ranked table: Rank | Store | Price | Link
  • Always show the content field as the assistant's main response
  • Save thread_id from the response if the user may want to follow up

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

85.03%
按下载量换算991

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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

继续浏览同类 Skills