Agent skill · Data & Analytics

fred-economic-data

Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.

LeonChaoXgithub.com/LeonChaoXGitHub ↗
claude-codeships scriptsMIT
Install
npx skills add LeonChaoX/qinyan-academic-skills --skill fred-economic-data --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 10
SKILL.md size: 12 KB
Bundled scripts: yes
Path: skills/15-金融与经济数据/fred-economic-data/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 759
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# FRED Economic Data Access ## Overview Access comprehensive economic data through FRED (Federal Reserve Economic Data), a database maintained by the Federal Reserve Bank of St. Louis containing over 800,000 economic time series from over 100 sources. **Key capabilities:** - Query economic time series data (GDP, unemployment, inflation, interest rates) - Search and discover series by keywords, tags, and categories - Access historical data and vintage (revision) data via ALFRED - Retrieve release schedules and data publication dates - Map regional economic data with GeoFRED - Apply data transformations (percent change, log, etc.) ## API Key Setup **Required:** All FRED API requests require an API key. 1. Create an account at https://fredaccount.stlouisfed.org 2. Log in and request an API key through the account portal 3. Set as environment variable: ```bash export FRED_API_KEY="your_32_character_key_here" ``` Or in Python: ```python import os os.environ["FRED_API_KEY"] = "your_key_here" ``` ## Quick Start ### Using the FREDQuery Class ```python from scripts.fred_query import FREDQuery # Initialize with API key fred = FREDQuery(api_key="YOUR_KEY") # or uses FRED_API_KEY env var # Get

What's inside
Steps it walks through
  1. Overview
  2. API Key Setup
  3. Quick Start
  4. Using the FREDQuery Class
  5. Direct API Calls
  6. Popular Economic Series
  7. API Endpoint Categories
  8. Series Endpoints
  9. Categories Endpoints
  10. Releases Endpoints
  11. Tags Endpoints
  12. Sources Endpoints
  13. GeoFRED Endpoints
  14. Data Transformations
Ships with 9 files
  • references/api_basics.md
  • references/categories.md
  • references/geofred.md
  • references/releases.md
  • references/series.md
  • references/sources.md
  • references/tags.md
  • scripts/fred_examples.py
  • scripts/fred_query.py
Commands it runs
export FRED_API_KEY="your_32_character_key_here"
uv run python scripts/fred_examples.py
More from qinyan-academic-skills
All skills →
About this skill
What does the fred-economic-data skill do?

Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.

How do I install it?

Run `npx skills add LeonChaoX/qinyan-academic-skills --skill fred-economic-data --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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