Is it suitable for historical trend analysis?

Absolutely. The Observation endpoint specifically supports fetching full time-series data for any available statistical variable across various timeframes.

Can this skill process data for visualization?

Yes, the skill integrates with Pandas, allowing Claude to convert query results into DataFrames for cleaning, analysis, and preparation for visualization.

What is the Data Commons Client skill?

It is a specialized capability for Claude Code that allows it to query the Data Commons API for public statistical data, including economic, demographic, and environmental metrics.

What types of entities can be resolved?

The skill can resolve place names, geographic coordinates, and Wikidata IDs into Data Commons IDs (DCIDs) to ensure accurate data retrieval from the knowledge graph.

Do I need an API key to use this skill?

Yes, for the default datacommons.org instance, an API key is required. You can set it via the DC_API_KEY environment variable or pass it during client initialization.

Data Commons Client

Name: Data Commons Client
Author: jimmc414

byjimmc414

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324

Data Science & ML

Accesses global statistical data from Data Commons for demographic, economic, and environmental analysis.

About

The Data Commons Client skill enables Claude to programmatically interact with the Data Commons knowledge graph, providing access to a massive repository of aggregated public data from authoritative sources like the Census Bureau, WHO, and World Bank. It facilitates querying time-series statistics, exploring entity relationships, and resolving place names or coordinates into unique identifiers. This skill is essential for researchers and developers needing reliable, structured data on population trends, GDP, health metrics, and climate change for data-driven applications or scientific discovery.

Key Features

Comprehensive access to global economic, demographic, and environmental datasets
Entity resolution for mapping names and coordinates to DCIDs
Statistical observation queries for historical time-series data
Knowledge graph exploration for navigating geographic hierarchies
Native Pandas integration for seamless data analysis and processing
324 GitHub stars

Use Cases

Retrieving climate and environmental statistics for specific geographic coordinates
Comparing healthcare access or disease prevalence between different administrative regions
Analyzing historical unemployment and GDP trends across different countries

About

Key Features

Comprehensive access to global economic, demographic, and environmental datasets
Entity resolution for mapping names and coordinates to DCIDs
Statistical observation queries for historical time-series data
Knowledge graph exploration for navigating geographic hierarchies
Native Pandas integration for seamless data analysis and processing
324 GitHub stars

Use Cases

Retrieving climate and environmental statistics for specific geographic coordinates
Comparing healthcare access or disease prevalence between different administrative regions
Analyzing historical unemployment and GDP trends across different countries