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api-data-fetcher

Fetch macroeconomic data from FRED, World Bank, IMF, BLS, and OECD into pandas DataFrames.

598|128|Updated Jan 26, 2026
Search Tags:#automation#api#data#fetch#pandas#macroeconomics#economic
One-click install
npx skills add https://github.com/meleantonio/awesome-econ-ai-stuff --skill api-data-fetcher-meleantonio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: api-data-fetcher
Source: https://github.com/meleantonio/awesome-econ-ai-stuff/tree/main/_skills/data/api-data-fetcher
Command: npx skills add https://github.com/meleantonio/awesome-econ-ai-stuff --skill api-data-fetcher-meleantonio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill centralizes the repetitive task of collecting macroeconomic data from multiple APIs (FRED, World Bank, IMF, BLS, OECD) into a single, reproducible workflow, delivering clean Python-ready datasets.

Core Features & Use Cases

  • Fetches data from major economic data sources (FRED, World Bank, IMF, BLS, OECD) into pandas DataFrames.
  • Generates well-documented, error-handled Python code and supports automated data updates for ongoing projects.
  • Enables cross-country and time-series analyses by combining indicators with consistent metadata.

Quick Start

Run a prompt to generate a Python data-fetcher script that downloads GDP, unemployment, and inflation from FRED and World Bank for a list of countries.

Frequently Asked Questions about api-data-fetcher

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fetch macroeconomic data from FRED and World Bank into pandas DataFrames?▼

To fetch macroeconomic data into pandas DataFrames, you can generate a Python script that centrally retrieves series from FRED, World Bank, IMF, BLS, and OECD APIs while handling rate limits and cleaning the outputs.

How do I handle API rate limits and errors when downloading economic time-series data?▼

Handling API rate limits and errors during economic data downloads is managed automatically by validating inputs, caching results, and implementing documented error handling within the generated Python data-fetcher scripts.

Can I build reproducible cross-country econometric datasets combining inflation and GDP indicators?▼

Yes, you can build reproducible cross-country econometric datasets by combining inflation, GDP, and unemployment indicators from multiple APIs with consistent metadata for time-series analysis.

Do I need to install fredapi and wbdata to automate macroeconomic data collection in Python?▼

Yes, you need to install fredapi, wbdata, and pandas as underlying dependencies to automate macroeconomic data collection, validate series definitions, and execute the generated Python scripts.

What is the best way to automate fetching US and cross-country macroeconomic data for ongoing analysis?▼

The best way to automate fetching US and cross-country macroeconomic data is to generate a reproducible Python workflow that documents series definitions and supports automated data updates for ongoing econometric projects.

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