A natural language interface for retrieving structured macroeconomic and commodity price data from the Eastmoney database.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mx-macro-data
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 mx-macro-data 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 mx-macro-data?
The mx-macro-data skill allows developers to access a vast repository of global economic information through simple text-based queries. By leveraging the Eastmoney database, it provides detailed datasets covering national accounts, monetary finance, fiscal revenue, and industrial operations. This skill is designed for the Openclaw Skills ecosystem to streamline the transition from raw research questions to clean, structured CSV data.
Built for professional analysts and automated agents, it handles the complexities of API authentication and data formatting. It ensures high data integrity by requiring specific metrics rather than vague generalizations, making it an essential tool for evidence-based market analysis and policy interpretation.
mx-macro-data Use Cases
- Retrieving historical GDP growth rates and industrial value-added data for specific regions.
- Monitoring inflation trends via CPI and PPI indicators across different countries.
- Tracking specific commodity price movements, such as gold, copper, or rare earth oxides.
- Analyzing monetary supply (M1/M2) and social financing scales for financial modeling.
- Automating the collection of employment data and manufacturing PMI for economic forecasting.
How mx-macro-data Works
- The user inputs a natural language query specifying a metric, region, and time dimension.
- The skill validates the input to ensure specific commodities or indicators are named, preventing ambiguous results.
- It communicates with the Eastmoney database using a secure API key to fetch the relevant time-series data.
- The system automatically categorizes the retrieved data into frequency-based CSV files (yearly, quarterly, monthly, etc.).
- A comprehensive description text file is generated to document data sources, units, and statistical metadata.
- For complex tasks, an orchestrator can perform gap detection and iterative backfilling to ensure 100% data coverage.
mx-macro-data Setup
First, obtain an API key from the Eastmoney service. Configure your environment variables as follows:
# Add to ~/.zshrc or ~/.bashrc
export EM_API_KEY="your_api_key_here"
source ~/.zshrc
Next, install the required Python dependencies to enable data fetching:
pip3 install httpx --user
mx-macro-data Data Schema & Taxonomy
The skill organizes output into a dedicated directory with a structured naming convention:
| File Type | Naming Pattern | Contents |
|---|---|---|
| CSV Data | mx_macro_data_<QueryID>_<Frequency>.csv |
Structured time-series data (UTF-8 encoded). |
| Description | mx_macro_data_<QueryID>_description.txt |
Source information, unit definitions, and row counts. |
Frequency types include yearly, quarterly, monthly, weekly, and daily depending on the indicator availability.
mx-macro-data Advanced Features
- Smart Relative Time Parsing: Supports queries like 'past three years' or 'last month' without needing absolute dates.
- Hierarchical Region Expansion: Automatically handles macro-region descriptors like 'East China' or 'All Provinces'.
- Iterative Backfilling Protocol: Supports up to 5 retries for missing data points to ensure dataset completeness.
- Frequency-Specific Auditing: Separately validates and merges data across different time scales to maintain statistical accuracy.
SKILL.md
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