An AI agent skill to retrieve, transform, and analyze over 800,000 U.S. and international macroeconomic time-series directly from the Federal Reserve Bank of St. Louis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install fred-api-al
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 fred-api-al 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 FRED Economic Data Skill?
The FRED Economic Data skill connects your AI agents directly to the Federal Reserve Bank of St. Louis (FRED) API, offering access to over 800,000 global economic indicators. As a dedicated integration designed for Openclaw Skills, this capability allows agents to retrieve critical macroeconomic data points, including gross domestic product (GDP), consumer price index (CPI), unemployment rates, and interest rates, translating natural language queries into exact tool calls.
By leveraging this integration within the Openclaw Skills ecosystem, developers can build domain-specific financial agents. Instead of manually parsing web search results or calculating percentage changes, the skill leverages FRED's native server-side calculations to present accurate, seasonal, and historic transformations natively while strictly preserving data integrity.
FRED Economic Data Skill Use Cases
- Tracking real-time updates for key macroeconomic indicators including real GDP growth, CPI inflation, and national unemployment rates.
- Analyzing historical yield curve movements by pulling Federal Reserve Treasury interest rates and daily bond yields.
- Investigating monetary supply variations, international exchange rates, and U.S. trading balance configurations.
- Building automated reporting tools that dynamically map, cite, and cross-reference multiple historical time-series over a defined date range.
How FRED Economic Data Skill Works
- Discovery Phase: The agent uses keyword searches via
fred_series_searchto find relevant economic datasets and matches them against high-popularity indicator IDs. - Metadata Verification: The agent calls
fred_seriesto check measurement frequency, seasonal adjustment options, and native data units. - Observation Retrieval: The agent queries
fred_series_observationswith customized date windows, limits, and transformation rules. - Data Transformation: Rather than performing calculations within the agent's logic, the underlying API transforms levels into year-over-year, annualized, or percentage changes.
- Citation Generation: The agent formats output citations precisely, referencing the observation date, data series ID, retrieval date, and the direct FRED URL.
FRED Economic Data Skill Setup
To activate this integration in your environment and expose its capabilities to Openclaw Skills, follow these configuration steps:
1. Obtain your FRED API Key
Sign up at FRED API to obtain your personal access key.
2. Configure Environment Variables
Configure your MCP server with the retrieved API key. This key remains hidden from the agent logic for security.
export FRED_API_KEY="your_secure_fred_api_key_here"
3. Server Integration
Ensure your agent system imports the corresponding fred-mcp configuration module. The skill will automatically map its logical functions to the initialized environment variables.
FRED Economic Data Skill Data Schema & Taxonomy
The FRED Economic Data skill structure organizes retrieved time-series observations, metadata, and category assignments into standard objects:
Series Metadata Schema
| Field Name | Type | Description |
|---|---|---|
id |
String | The unique FRED identifier (e.g., GDPC1, UNRATE) |
title |
String | Full name of the economic time-series |
units |
String | Measurement units (e.g., Billions of Dollars, Percent) |
frequency |
String | Data measurement interval (e.g., Monthly, Quarterly) |
last_updated |
String | Timestamp of the last official revision |
Observations Schema
| Field Name | Type | Description |
|---|---|---|
date |
String | The exact observation point (format: YYYY-MM-DD) |
value |
String | Numeric observation value (represented as . if missing) |
realtime_start |
String | Revision vintage start window boundary |
realtime_end |
String | Revision vintage end window boundary |
FRED Economic Data Skill Advanced Features
- Server-Side Unit Transformations: Use parameter transforms (
pc1for YoY,pcafor annualized rate) to calculate changes via FRED's high-fidelity servers. - Generic Passthrough Tooling: Call custom endpoints via
fred_requestto gather vintage datasets, revisions, and regional category releases. - Automatic Missing-Data Handling: Gracefully maps missing values (
.) without causing computational exceptions or halting active pipelines in Openclaw Skills. - Caching and Rate Limiting Support: Automatically respects the 120 requests/minute budget, minimizing duplicate queries through robust session-level data storage.
SKILL.md
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