A comprehensive diagnostic tool for 1688 merchants to perform cross-store comparative analysis across shop, category, and offer levels.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install 1688-multi-shop-compare
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 1688-multi-shop-compare 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 1688 Multi-Shop Comparison Analysis?
The 1688-multi-shop-compare skill is a specialized Openclaw Skills module designed for merchants managing multiple 1688 storefronts. It moves beyond simple data listing by implementing a sophisticated three-tier diagnostic framework: Shop Level, Category Level, and Offer Level. By analyzing the structural differences between stores, it identifies specific operational roles—such as high-ticket wholesale vs. high-traffic retail—and evaluates their health relative to industry benchmarks.
This skill automates the extraction of trade indices, traffic trends, and buyer demographics to pinpoint exactly which offers are underperforming and which represent growth opportunities. It is specifically built to help users eliminate internal competition between their own stores and leverage cross-store operational experience for improved conversion and retention.
1688 Multi-Shop Comparison Analysis Use Cases
- Analyzing the operational health and role positioning of multiple 1688 stores simultaneously.
- Identifying internal competition or category gaps across a shop portfolio.
- Diagnosing specific offer issues (low conversion, high refund rates) with actionable fix suggestions.
- Evaluating cross-store buyer demographics to optimize regional targeting and customer synergy.
How 1688 Multi-Shop Comparison Analysis Works
- The skill retrieves the binding list of all shops associated with the user's account to secure AccessKeys (AK).
- If four or more shops are detected, an interactive card allows the user to select the specific scope and analysis focus (e.g., focus on merchandise vs. geography).
- It concurrently fetches full operational data for all selected shops, including core metrics, top-performing offers, and traffic trends.
- The system performs a horizontal comparison based on a 7-day or 30-day window to ensure data parity.
- It applies a tiered diagnostic logic: identifying shop roles, analyzing category distributions, and finally identifying high-risk or high-potential specific products.
- Generates a structured report with data-backed conclusions and prioritized action items.
1688 Multi-Shop Comparison Analysis Setup
To use this Openclaw Skills module, ensure you have the CLI environment configured and your 1688 accounts bound.
# Get the list of bound shops and their AccessKeys
python3 cli.py get_bindlist
# Run a diagnostic for a specific shop (usually handled automatically by the agent)
python3 cli.py get_shop_data --ak <YOUR_SHOP_AK> --date_type RECENT_7
1688 Multi-Shop Comparison Analysis Data Schema & Taxonomy
The skill organizes data into several distinct dimensions for cross-store mapping:
| Dimension | Data Source | Usage |
|---|---|---|
| trade_index | seller_trade_code_index | Core KPIs like GMV, conversion rate, and AOV. |
| core_metrics | get_core_metrics | Peer comparison and ranking data. |
| traffic_trend | get_traffic_trend | Daily UV/PV trends and click-through rates (CTR). |
| abnormal_offer | seller_import_abnormal_offer | List of products requiring immediate attention. |
| top_offer_* | seller_top_offer | Best performing products by payment, UV, or repurchase. |
| customer_detail | seller_customer_detail | Identification of top recurring buyers and geographic distribution. |
1688 Multi-Shop Comparison Analysis Advanced Features
- Intelligent Role Labeling: Automatically classifies shops as 'Wholesale-focused', 'Traffic-drivers', or 'Profit-centers' based on buyer behavior.
- Dynamic Action Cards: Generates one-click interactive cards for immediate optimization of identified abnormal offers.
- Concurrent Data Collection: Executes API calls in parallel across multiple shop IDs to minimize latency during large-scale portfolio audits.
- Category Synergy Analysis: Identifies if products in one store should be cross-listed or optimized based on the success of similar categories in a sister store.
SKILL.md
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