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api-data-fetcher

Fetch economic time-series data from external APIs into Pandas DataFrames.

203|27|Updated Apr 19, 2026
Search Tags:#api#data fetching#python#pandas#economic indicators#fred#world bank
One-click install
npx skills add https://github.com/franklee16/academic-research-skills --skill api-data-fetcher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: api-data-fetcher
Source: https://github.com/franklee16/academic-research-skills/tree/main/data-sourcing/api-data-fetcher
Command: npx skills add https://github.com/franklee16/academic-research-skills --skill api-data-fetcher

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It eliminates the manual effort of locating, downloading, and normalizing economic datasets by programmatically fetching time-series and indicator data from major sources.

Core Features & Use Cases

  • Automated API data collection: Retrieves data from FRED, World Bank, BLS, OECD, and Yahoo Finance workflows via generated Python code.
  • Clean, documented outputs: Produces Pandas-friendly structures (e.g., DataFrames) with basic error handling and clear series/indicator definitions.
  • Common research use cases: Supports macroeconomic indicator downloads, multi-source dataset building, scheduled data updates, and cross-country panel preparation.

Quick Start

Tell the skill to generate Python code to fetch FRED series for GDP and unemployment for a specified date range, then save the cleaned results to CSV.

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 economic indicators from FRED and World Bank APIs into a Pandas DataFrame?▼

You can fetch macroeconomic data by running generated Python code that uses appropriate client packages to pull FRED and World Bank time-series, returning cleaned results as Pandas DataFrames with error handling.

Can I build a cross-country panel dataset using World Bank API data?▼

Yes, you can build cross-country panels by fetching development indicators from the World Bank API, combining multiple series into a structured Pandas DataFrame ready for comparative analysis.

How do I securely configure API keys when downloading macroeconomic time-series?▼

Configure API keys safely using environment variables when downloading macroeconomic time-series, keeping credentials out of your codebase while authenticating requests to external data APIs.

Does this approach to API data fetching work with BLS, OECD, and Yahoo Finance sources?▼

Yes, the automated API data collection approach supports BLS, OECD, and Yahoo Finance workflows via generated Python code, retrieving time-series and formatting outputs as Pandas-friendly structures.

What's the best way to automate periodic dataset refreshes across multiple economic data sources?▼

Automate periodic dataset refreshes by generating robust Python scripts that fetch updated time-series from multiple economic APIs, handling errors and normalizing outputs into Pandas DataFrames.

Why do I need Pandas for formatting fetched economic time-series data?▼

Pandas is needed to format fetched economic time-series as DataFrames, providing a clean, documented structure with clear indicator definitions that makes the data ready for analysis.

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