imf-ra-data

What problem does it solve?

Manually searching for IMF data series, mapping indicator codes, and formatting outputs for economic analysis is time-consuming and prone to errors. This Skill automates the entire retrieval workflow for any IMF database, eliminating manual code lookup and formatting work.

Core Features & Use Cases

• Automated Data Retrieval: Fetches single series or multi-country panels from all major IMF databases (WEO, IFS, BOPS, GFS, DOTS, FSI, etc.) and Haver sources using the internal Python SDK, with automatic handling of LIVE vs vintage database versions.
• Smart Output Formatting: Generates refreshable Excel files with enriched metadata, or raw wide/long CSV/Excel formats, with layout auto-selected based on the number of indicators, countries, and time periods.
• Built-in Guardrails: Validates dimension values, retries transient API failures, handles large country panels via automatic key chunking, and enforces safe query policies to avoid overly broad data pulls.
• Use Case: An economic researcher needing 10 years of nominal GDP and inflation data for G7 countries from the WEO database can request the data in natural language and receive a formatted, metadata-rich Excel file in seconds.

Quick Start

Use the imf-ra-data skill to fetch 2020 to 2024 nominal GDP and inflation data for the United States, Germany, Japan, the United Kingdom, France, and Italy from the WEO database and save it as a refreshable Excel file.