An advanced AI market research tool that identifies winning cross-border products using real-time trends, social media heat, and precise profit modeling.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ecommerce-product-picker
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 ecommerce-product-picker 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 Ecommerce Product Picker?
The Ecommerce Product Picker is a high-performance intelligence tool designed for cross-border sellers and entrepreneurs. It leverages Openclaw Skills to bridge the gap between market trends and actionable product selection. By analyzing data from Amazon BSR, AliExpress, and domestic platforms like Taobao, it provides sellers with a data-backed roadmap for high-potential items.
Beyond simple scrapers, this skill integrates social sentiment from TikTok and Xiaohongshu (RED) with a rigorous financial model. It calculates everything from 1688 wholesale procurement costs to FBA fees and logistics, providing an estimated monthly profit range based on realistic Best Seller Rank (BSR) conversion models.
Ecommerce Product Picker Use Cases
- Identifying trending 'winning products' for Amazon FBA or dropshipping stores.
- Analyzing specific niches like beauty, 3C electronics, or pet supplies to find underserved sub-categories.
- Validating product ideas found on social media (TikTok/Xiaohongshu) against actual sales data.
- Performing competitive price analysis and profit margin stress-testing before inventory purchase.
How Ecommerce Product Picker Works
- Category Extraction: The user provides a target niche, or the agent defaults to high-velocity categories like beauty or home goods.
- Trend Scraping: Using browser and search tools, the agent crawls Amazon BSR, Google Trends, and AliExpress rising charts.
- Social Heat Analysis: It measures viral potential by analyzing views and engagement on TikTok and social commerce platforms.
- Sales Volume Modeling: The agent applies specific algorithms to convert BSR and domestic sales data into high/low monthly volume estimates.
- Financial Calculation: It aggregates sourcing costs, platform fees (15%+), and logistics to output a net margin and suggested retail price.
- Final Reporting: A structured Top 3 recommendation list is generated with live links and risk assessments.
Ecommerce Product Picker Setup
To enable the Ecommerce Product Picker within your environment, ensure your agent has access to the required browser tools.
# Install the Openclaw Skills package
npm install @openclaw/ecommerce-product-picker
# Configure the tool within your AI agent's skill manifest
# Ensure 'browser' and 'web_search' capabilities are enabled
Ecommerce Product Picker Data Schema & Taxonomy
The skill structures output using a comprehensive product profile. Below is the taxonomy for the generated market reports:
| Feature | Description |
|---|---|
| Domestic Volume | Benchmarked monthly sales from Taobao/Tmall/JD live streams. |
| Cross-border Est. | Monthly volume based on Amazon BSR conversion logic. |
| Social Heat | Total notes/views and average likes on TikTok/Xiaohongshu. |
| Cost Breakdown | 1688 procurement + logistics ($3-6) + platform fees (15%). |
| Profit Metrics | Suggested retail price (Cost x 2.8), GP%, and Monthly Profit. |
Ecommerce Product Picker Advanced Features
- BSR Conversion Modeling: Uses category-specific mathematical models to estimate units sold (e.g., BSR <1000 ≈ 500-2000+ units/month).
- API Extensibility: Supports integration with professional tools like Jungle Scout or Helium 10 for enhanced data precision.
- Seasonal Weighting: Adjusts sales volume predictions based on upcoming seasonal peaks (e.g., Q4 holidays or Prime Day).
- Multi-Platform Cross-Referencing: Simultaneously validates trends across Western (Amazon) and Eastern (1688/Taobao) ecosystems via Openclaw Skills.
SKILL.md
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