api-data-fetcher_skill

Overview

This skill fetches economic data from major public APIs (FRED, World Bank, IMF, BLS, OECD) and generates clean, documented Python code to download and prepare time series and panel datasets. It focuses on reproducible workflows: environment-variable API key handling, error handling, and clear series documentation. The output is ready-to-use Pandas DataFrames or CSVs for analysis and modeling.

How this skill works

Tell the skill what indicators, time range, countries, frequency, and output format you need. It selects the best API for each request, produces Python code that installs or imports required packages, reads API keys from environment variables, applies basic error handling and logging, and returns cleaned DataFrames. For bulk tasks it suggests rate limiting, caching, and merges multi-source series into a consistent panel.

When to use it

• Downloading macro time series (GDP, inflation, unemployment) for empirical analysis
• Building cross-country panels from World Bank or OECD indicators
• Automating periodic data updates for reports or dashboards
• Combining FRED financial series with country-level indicators
• Preparing cleaned CSVs or Pandas DataFrames for research or teaching

Best practices

• Store API keys in environment variables; never hardcode them
• Add simple rate limiting and retry logic for bulk downloads
• Cache raw responses locally to avoid repeated API calls and speed re-runs
• Document each series with source, code, frequency, and units
• Align frequencies and handle missing values explicitly when merging sources

Example use cases

• Fetch a set of US macro series from FRED into a single time-indexed DataFrame and save to CSV
• Download GDP per capita and population for a list of countries from the World Bank as an annual panel
• Combine FRED interest rates with OECD unemployment rates for cross-source analysis
• Automate weekly refresh of a dashboard dataset with retries and local caching
• Generate reproducible sample code for students to reproduce empirical exercises