What is Data Commons?

Data Commons is an open knowledge graph that aggregates data from global sources like the US Census, World Bank, and UN into a single, unified, and queryable API.

Can I use this data for data science projects?

Absolutely. This skill is designed to work with the Data Commons Python API and includes support for Pandas DataFrames, making it ideal for data analysis and visualization.

Do I need an API key to use this skill?

Yes, for the default Data Commons instance, you need an API key from apikeys.datacommons.org, which should be set via the DC_API_KEY environment variable.

Is historical data available through this skill?

Yes, the Observation endpoint allows you to fetch full time-series data for statistical variables, enabling historical trend analysis.

What kind of geographic entities can I resolve?

You can resolve place names, ZIP codes, Wikidata IDs, and latitude/longitude coordinates into Data Commons IDs (DCIDs) to query specific localized data.

Data Commons Client

Name: Data Commons Client
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Accesses and queries global statistical data from Data Commons to provide programmatic insights into demographics, economics, and environmental indicators.

About

The Data Commons Client skill empowers Claude to interact with the massive Data Commons knowledge graph, which aggregates authoritative data from sources like the US Census, World Bank, and WHO. It facilitates complex statistical queries, geographic entity resolution, and hierarchical exploration—such as retrieving data for all counties within a specific state. By providing seamless integration with Pandas, this skill allows users to fetch, process, and analyze real-world data points like unemployment rates, GDP, and health statistics directly within their development workflow.

Key Features

Native Pandas integration for immediate data analysis and manipulation
Query time-series statistical data across thousands of global variables
Access to metadata and provenance from authoritative global data sources
Navigate complex geographic hierarchies and entity relationships within the knowledge graph
Resolve place names, coordinates, and Wikidata IDs to unique Data Commons identifiers
1 GitHub stars

Use Cases

Generating automated economic reports or trend analysis based on official GDP and labor figures
Analyzing correlations between environmental factors and public health statistics using structured knowledge graph queries
Mapping and comparing demographic data across different geographic levels like cities, states, or countries

About

Key Features

Native Pandas integration for immediate data analysis and manipulation
Query time-series statistical data across thousands of global variables
Access to metadata and provenance from authoritative global data sources
Navigate complex geographic hierarchies and entity relationships within the knowledge graph
Resolve place names, coordinates, and Wikidata IDs to unique Data Commons identifiers
1 GitHub stars

Use Cases

Generating automated economic reports or trend analysis based on official GDP and labor figures
Analyzing correlations between environmental factors and public health statistics using structured knowledge graph queries
Mapping and comparing demographic data across different geographic levels like cities, states, or countries