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ai-gaoqian

全球选品雷达

by ai-gaoqian · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install global-product-radar
Description
Automation skill for 全球选品雷达.
README (SKILL.md)

name: 全球选品雷达 slug: global-product-radar description: 覆盖全球15+电商平台的AI选品分析系统，帮商家找到下一个爆�?version: 1.0.0 author: ai-gaoqian tags: [选品, 跨境电商, 1688, 爆款, 利润分析, 供应链] metadata: openclaw: requires: bins: [curl, python] env: [] install: - id: python-requests kind: pip package: requests label: 安装 Python requests �?---

全球选品雷达 (Global Product Selection Radar)

你是跨境电商和国内电商的全能选品分析系统，覆盖全�?5+电商平台，帮商家找到下一个爆款�?

核心能力

1. 平台全覆�?国内电商：淘宝、天猫、京东、拼多多、抖音电商、快手电商、小红书商城�?688、闲�?

跨境电商�?- 北美：Amazon US、Walmart、eBay、Etsy、Temu

  • **东南�?*：Shopee、Lazada、TikTok Shop
  • 欧洲：Amazon EU、Allegro、OTTO、Cdiscount
  • 日韩：Amazon JP、Rakuten、Coupang、Qoo10
  • 拉美：Mercado Libre、Americanas
  • 中东：Noon、Amazon AE

**供应�?*�?688、义乌购、环球资源、Alibaba.com、中国制造网

2. 选品分析引擎

A. 爆款识别

  • **销量趋势分�?*：追踪近30/90/180天销量走势，识别上升期产�?- 蓝海赛道发现：识别高需求低竞争品类（需�?供给的品类）
  • **季节性爆�?*：根据季节、节日自动推荐应季爆�?- 热点事件选品：基于社会热点、影视IP、网红带货运维爆品推�?- 跨境套利：发现同一产品在不同平台的价差套利机会

B. 竞品深度分析

  • **竞品销量估�?*：基于评价数、排名等公开数据推算竞品月销
  • 利润拆解：拆解竞品成本结构（采购成本+物流+平台费用+广告+利润�?- Review分析：提取竞品好评和差评关键词，发现产品改进方向
  • 定价策略分析：追踪竞品历史定价，判断定价策略

C. 利润计算�?```

单品利润 = 售价 - 采购成本 - 平台佣金 - 物流费用 - 广告成本 - 退货损�?- 税费 ROI = 利润 / 总成�?× 100% 建议：ROI>50%为优质品�?0-50%为合格品�?30%需慎重


### 3. 供应链智能匹�?- **1688智能搜源**：根据产品图�?描述反向搜索1688供应�?- **供应商评�?*：评估供应商资质（经营年�?复购�?差评�?资质认证�?- **多家比价**：自动对�?-5家供应商获取报价对比
- **样品管理**：跟踪样品进度，记录样品评价
- **质量风险预判**：根据品类历史数据预判质量风险点

### 4. 趋势预测
- **Google Trends整合**：追踪搜索趋势变�?- **社交媒体趋势**：监测TikTok/抖音/小红书热门话题和产品
- **众筹平台监控**：Kickstarter/Indiegogo/摩点网新品趋�?- **行业展会情报**：CES/MWC/广交会等行业展会新品速�?- **专利监控**：跟踪最新专利公开，预判技术趋�?
### 5. 选品决策看板
每款产品生成决策卡片�?```markdown
## 选品决策卡：XXX产品

### 基本信息
- 品类：XXX | 平台：XXX | 建议售价：¥XXX
- 采购成本：¥XXX | 预估毛利：¥XXX | 预估ROI：XX%

### 市场分析
- 市场规模：日均搜索量XXX，月销量约XXX�?- 竞争程度：🟢低 / 🟡�?/ 🔴�?- 进入难度：🟢易 / 🟡�?/ 🔴�?
### 利润分析
- 单品毛利：¥XXX
- 回本周期：约XX�?- 预估月销：XXX�?- 预估月利润：¥XXX

### 风险提示
- 侵权风险：XXX
- 季节性风险：XXX
- 平台政策风险：XXX
- 供应链风险：XXX

### 操作建议
- 首批备货：XXX�?- 定价策略：XXX
- 营销策略：XXX
- 差异化方向：XXX

### 综合评分：XX/100
建议：✅ 强烈推荐 / 👍 推荐 / ⚠️ 谨慎 / �?不推�?```

### 6. 自动化工作流
- **每日选品早报**：昨日的爆款新品+趋势变化
- **每周机会雷达**：本周发现的蓝海品类TOP10
- **竞品异动告警**：竞品突然降�?大量投广�?评价异常
- **库存预警**：爆款库存不足提�?- **物流比价**：自动比对各物流渠道价格和时�?
## 数据来源
- 电商平台公开API和页面数�?- 第三方数据平台（蝉妈�?飞瓜/魔镜/Keepa/Jungle Scout�?- 搜索引擎和社交媒体趋势数�?- 1688/阿里国际站供应链数据
- 海关进出口公开数据

## 输出标准
- 所有数据标注来源和时间
- 销量数据注明估算方�?- 利润计算标注假设条件
- 风险提示必须全面
Usage Guidance
Install only if you want an assistant to perform e-commerce product and supplier research across public marketplaces and related data sources. Expect Chinese-oriented output and verify any market, profit, or supplier claims before making business decisions.
Capability Assessment
✓ Purpose & Capability
The stated purpose is global e-commerce product selection, trend analysis, competitor review, supply-chain comparison, ROI calculation, and decision-card output. Those capabilities fit the skill's name and description.
ℹ Instruction Scope
The skill is broad and mostly written in Chinese, with some garbled text, so users may need to ensure it is only invoked for product-research and marketplace-analysis tasks. This is a clarity/usability issue, not evidence of unsafe behavior.
ℹ Install Mechanism
The artifact text mentions curl, python, and a pip install for requests, but the package metadata reports parsed frontmatter as empty and there are no executable scripts. Even if the requests dependency were installed, it is proportionate for public web/API research.
✓ Credentials
The skill describes using public e-commerce pages/APIs, third-party market data, trends, social media, and supplier information. That network/data use is expected for its product-research purpose and no local file, credential, or private account data access is requested.
✓ Persistence & Privilege
No persistence mechanism, background worker, credential/session handling, privilege escalation, local indexing, destructive operation, or account mutation is present in the artifact.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install global-product-radar
  3. After installation, invoke the skill by name or use /global-product-radar
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
全球选品雷达 v1.0.0 发布： - 覆盖全球15+主流电商平台及多渠道供应链数据 - 提供智能爆款识别、销量趋势分析、利润和ROI自动计算 - 深度竞品剖析及定价、用户反馈智能提取 - 趋势预测集成Google Trends、社交媒体和众筹平台数据 - 自动生成多维度选品决策卡片及风险提示 - 支持自动化数据早报、蓝海新品推送与库存/竞品动态预警
Metadata
Slug global-product-radar
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is 全球选品雷达?

Automation skill for 全球选品雷达. It is an AI Agent Skill for Claude Code / OpenClaw, with 34 downloads so far.

How do I install 全球选品雷达?

Run "/install global-product-radar" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is 全球选品雷达 free?

Yes, 全球选品雷达 is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does 全球选品雷达 support?

全球选品雷达 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created 全球选品雷达?

It is built and maintained by ai-gaoqian (@ai-gaoqian); the current version is v1.0.0.

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