How do I find specific variable names like 'UnemploymentRate'?

You can use the built-in variable discovery methods or explore the official Data Commons Statistical Variable Explorer to find the exact DCIDs for the metrics you need.

Can I query data for specific geographic levels like counties?

Absolutely. The skill supports entity expressions that allow you to query hierarchies, such as 'all counties within a specific state' or 'all cities within a country'.

What is Data Commons?

Data Commons is an open knowledge graph that aggregates public data from sources like the Census Bureau, WHO, and World Bank into a unified format for easy access.

Does this skill support data visualization tools?

While the skill focuses on data retrieval, it includes full Pandas support, making it easy to pass retrieved datasets to libraries like Matplotlib or Seaborn.

Do I need an API key to use this skill?

Yes, for the standard datacommons.org instance, you must obtain a free API key from apikeys.datacommons.org and set it as the DC_API_KEY environment variable.

Data Commons Explorer

Name: Data Commons Explorer
Author: Microck

byMicrock

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データサイエンスとML

Accesses and analyzes global public statistical data from the Data Commons knowledge graph directly within your development workflow.

概要

The Data Commons Explorer skill provides Claude with programmatic access to the Data Commons Python API v2, a massive knowledge graph that aggregates data from authoritative sources like the US Census, World Bank, and WHO. This skill enables users to seamlessly query time-series statistical data, explore geographic hierarchies, and resolve entity identifiers such as place names or coordinates into unique Data Commons IDs. With built-in Pandas integration, it allows for sophisticated data analysis, trend visualization, and comparative studies across demographics, economics, health, and environmental sectors without leaving the terminal.

主な機能

Seamless Pandas integration for instant DataFrame generation
81 GitHub stars
Time-series data retrieval for historical trend analysis
Hierarchical querying for regional and administrative data structures
Programmatic access to the Data Commons statistical knowledge graph
Entity resolution for mapping names and coordinates to DCIDs

ユースケース

Comparing economic indicators like GDP or unemployment rates across different nations
Fetching health and environmental datasets for data science and ML research
Analyzing demographic shifts and population trends at state or county levels

概要

主な機能

Seamless Pandas integration for instant DataFrame generation
81 GitHub stars
Time-series data retrieval for historical trend analysis
Hierarchical querying for regional and administrative data structures
Programmatic access to the Data Commons statistical knowledge graph
Entity resolution for mapping names and coordinates to DCIDs

ユースケース

Comparing economic indicators like GDP or unemployment rates across different nations
Fetching health and environmental datasets for data science and ML research
Analyzing demographic shifts and population trends at state or county levels