A sophisticated data-driven scoring tool designed to evaluate product viability for the Shangpin Zhaipei home furnishing brand across major e-commerce and social platforms.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install 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 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 Product Picker?
Product Picker is a high-performance analytical tool designed for the digital home furnishing sector. It automates the complex process of product selection by gathering and processing data from Xiaohongshu, Jingdong, and Taobao. By leveraging Openclaw Skills, this tool provides a standardized framework for scoring products based on social engagement and e-commerce conversion metrics, specifically tailored for brands focusing on high-end customization and digital delivery.
The skill goes beyond simple data scraping; it incorporates a multi-dimensional corporate evaluation system that assesses brand alignment, home scene integration, and market potential. This ensures that every selected product fits the strategic positioning of the enterprise while providing actionable insights through automated reporting and communication workflows.
Product Picker Use Cases
- Evaluating new product entries for custom home furnishing catalogs based on real-time market data.
- Automating cross-platform competitive analysis between social media trends and actual e-commerce sales.
- Assessing the 'landing' suitability of furniture products within high-end, tech-driven interior design scenarios.
- Streamlining corporate decision-making by generating instant performance reports delivered via enterprise communication tools.
How Product Picker Works
- Data Collection: Scrapes engagement metrics from Xiaohongshu and pricing/sales volume from Jingdong and Taobao for at least three candidate products.
- Data Cleaning: Parses raw strings into numerical values and filters results to ensure brand and category keywords match strictly.
- Index Calculation: Computes an Interaction Index for social platforms and a Conversion Index for e-commerce platforms using weighted formulas.
- Normalization: Applies Min-Max scaling to standardize scores across different platform magnitudes into a 0-100 range.
- Weighted Scoring: Aggregates platform scores (50% Xiaohongshu, 25% JD, 25% Taobao) to form a market performance baseline.
- Corporate Alignment: Adds a secondary evaluation layer (50% weight) focusing on brand tone, scene integration, consumer profile, and export potential.
- Reporting: Generates a detailed Markdown report and an Excel spreadsheet, then automatically transmits the report via Feishu attachment.
Product Picker Setup
To utilize this within the ecosystem of Openclaw Skills, ensure your environment is configured for file system access and Feishu API integration.
# Install the product-picker skill
openclaw install product-picker
# Set up the required environment variables for Feishu delivery
export FEISHU_SENDER_ID="your_id_here"
Product Picker Data Schema & Taxonomy
The skill organizes its output in a structured directory for easy retrieval and auditing.
| Data Type | Location | Description |
|---|---|---|
| Assessment Report | ~/.openclaw/media/ai-choise/product-picker/*.md |
The final Markdown report containing scoring logic and decisions. |
| Data Spreadsheet | ~/.openclaw/media/ai-choise/product-picker/*.xlsx |
Detailed raw data and calculations for deeper manual analysis. |
| PPT Presentation | Sub-agent output | Optional presentation generated from the Markdown report. |
Product Picker Advanced Features
- Multi-agent collaboration: Automatically triggers the md-to-nanobanana-ppt skill to convert evaluation reports into professional presentations.
- Veto Logic: Implements a 'one-vote veto' system to automatically reject products with low ratings, category conflicts, or excessive installation complexity.
- Automated Delivery: Strictly enforces report delivery via Feishu attachments, ensuring stakeholders receive data immediately without manual file transfers.
- Dynamic Weighting: Balances social 'seeding' influence against hard e-commerce conversion data to provide a holistic view of product success.
SKILL.md
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