An advanced analytical tool for extracting and summarizing key performance indicators from Juss Sports APP order data based on product dimensions and keywords.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install app-order-prod-key-stats
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 app-order-prod-key-stats 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 Juss Sports APP Order Key Indicators Statistics?
This skill is a robust data analysis tool specifically engineered for the Juss Sports APP ecosystem. It enables developers and analysts to query high-level metrics such as order counts, unpaid transaction volume, refund totals, and unique user engagement directly from the data warehouse. By utilizing Openclaw Skills, teams can automate the extraction of complex business intelligence without writing manual SQL for every query.
The tool is designed with a focus on flexibility and precision, allowing for dynamic grouping by order titles or detailed descriptions. It provides a standardized way to access Juss Sports order data while maintaining strict security protocols through read-only access and fixed query templates.
Juss Sports APP Order Key Indicators Statistics Use Cases
- Analyzing sales performance for specific high-profile events like F1 or basketball tournaments.
- Comparing payment versus refund ratios across different business sectors such as Ticket Orders, Venue Bookings, or the Points Mall.
- Generating detailed product-level reports by filtering for specific keywords in order descriptions.
- Monitoring user conversion trends over specific time ranges to evaluate marketing effectiveness.
How Juss Sports APP Order Key Indicators Statistics Works
- The user provides query parameters including a time range, optional keywords, and the desired statistics dimension (Title or Detail).
- The skill validates the input and maps business block names to their corresponding database identifiers.
- A dynamic SQL query is constructed using a predefined template that ensures high-performance aggregation and window functions for percentage calculations.
- A Python environment executes the query against the Juss Sports data warehouse using secure, read-only credentials.
- The raw data is processed into a structured Pandas DataFrame and formatted into a clean Markdown table.
- The final output is presented as an actionable report with an integrated summary of the findings.
Juss Sports APP Order Key Indicators Statistics Setup
To get started with this skill, ensure your environment has Python installed along with the required libraries. This is a common requirement for many Openclaw Skills that handle data processing.
# Install required Python packages
pip install mysql-connector-python pandas tabulate
# Configure your database environment variables
export JIUSHI_DB_PASSWORD='your_db_password'
Juss Sports APP Order Key Indicators Statistics Data Schema & Taxonomy
The skill interacts primarily with the juss_dw.app_j_order table. Below is the organization of the data it processes:
| Attribute | Description |
|---|---|
| order_title | The primary name of the product or service ordered |
| order_desc | Detailed description of the order items |
| order_state | Status codes (e.g., PAY_SUCCESS, ORDER_REFUND_ALL, PAY_WAIT) |
| pay_amount | Transaction value in cents (automatically converted to Yuan) |
| refunded_amount | Amount returned to the user in cents |
| user_id | Unique identifier for the customer |
| create_time | Timestamp of the order creation |
| order_type | Business block identifier (e.g., TICKET_ORDER, SWIM_ORDER) |
Juss Sports APP Order Key Indicators Statistics Advanced Features
- Multi-keyword filtering support using OR logic and LIKE pattern matching for flexible data discovery.
- Automatic sales proportion calculation using SQL window functions to ensure total percentages always sum to 100%.
- Dimension switching that allows toggling between high-level title grouping and granular description-level analysis.
- Comprehensive mapping for localized business units, translating user-friendly Chinese names into system-level English keys.
- Built-in security layer that strictly prohibits non-SELECT operations to protect the integrity of the data warehouse.
SKILL.md
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