A professional-grade interface for querying Datayes and Tonglian macro-economic and industry indicators.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install datayes-macro
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 datayes-macro 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 Datayes Macro?
Datayes Macro is a specialized skill designed for AI agents to retrieve and analyze macroeconomic and industry time-series data. By leveraging Openclaw Skills, users can seamlessly fetch critical financial indicators such as GDP, CPI, PPI, PMI, and M2 directly into their development or research workflows.
The skill interfaces with the Datayes/Tonglian ecosystem through an optimized Python execution layer, ensuring that data is pulled accurately without the need for manual HTTP request handling. It provides a robust framework for financial analysts and developers to access national and industrial data points with high precision and minimal overhead.
Datayes Macro Use Cases
- Tracking national economic health through GDP, unemployment, and trade balance indicators.
- Analyzing monetary policy shifts by monitoring M2, social financing, and interest rates.
- Conducting industry-specific research by fetching production volume and pricing data.
- Automating financial report generation with the latest monthly or quarterly macro data.
- Comparing year-on-year (YoY) and month-on-month (MoM) growth across different economic sectors.
How Datayes Macro Works
- The AI agent identifies a request for macro-economic data (e.g., "What was China's M2 growth last month?").
- The skill executes a search command using a local Python script to find candidate indicators matching the user's query.
- Based on relevance (prioritizing national data and monthly frequencies), the most appropriate indicator ID is selected.
- A query or detail command is issued to the Datayes API to fetch the specific time-series data points.
- The data is returned in a structured format, including units, frequency, and source, for the agent to present to the user.
Datayes Macro Setup
To get started with this skill in your Openclaw Skills environment, follow these steps:
1. Obtain Datayes Token
Log in to your account at Datayes and generate an API token from the dashboard.
2. Configure Environment Variables
Set your token in your terminal session or environment configuration:
# macOS / Linux
export DATAYES_TOKEN='your-token'
# Windows PowerShell
$env:DATAYES_TOKEN = "your-token"
3. Requirements
Ensure python3 is installed. No additional third-party Python libraries are required as the skill uses the standard library for maximum compatibility.
Datayes Macro Data Schema & Taxonomy
The skill returns data in a structured JSON format to ensure compatibility within Openclaw Skills.
| Field | Description | Example |
|---|---|---|
indicator_name |
The full name of the economic metric | M2 Money Supply YoY |
frequency |
Data update cycle | Monthly (M), Quarterly (Q) |
unit |
The measurement unit | Percentage (%), CNY 100M |
data_points |
List of date-value pairs | [{"date": "20240131", "value": 8.7}] |
source |
Official data provider | National Bureau of Statistics |
Datayes Macro Advanced Features
- Intelligent search filtering: Automatically refines keywords to reduce noise in indicator discovery.
- Smart Defaults: Prioritizes national data and monthly frequency when user specifications are ambiguous.
- Secure Proxying: Validates hostnames to ensure the DATAYES_TOKEN is never sent to untrusted domains.
- High-Performance Execution: Uses native Python scripts for efficient data fetching without heavy dependency chains.
- JSON-Native Output: Supports
--jsonflags for all commands to facilitate seamless multi-agent automation.
SKILL.md
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