Do I need an API key to use this skill?

Yes, an API key is required to access the main Data Commons instance. You can request one at apikeys.datacommons.org and set it via the DC_API_KEY environment variable.

What types of data are available through this skill?

You can access a vast range of data including population counts, median income, disease prevalence, CO2 emissions, unemployment rates, and education statistics.

What is the Data Commons Client skill?

It is a specialized capability for Claude Code that allows developers to programmatically query and analyze public statistical data from the Data Commons knowledge graph using the Python API.

Can I use this skill with Pandas?

Absolutely. This skill includes specific methods to convert statistical query results directly into Pandas DataFrames, making it ideal for data science workflows.

Data Commons Client

Name: Data Commons Client
Author: BbgnsurfTech

byBbgnsurfTech

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数据科学与机器学习

Provides programmatic access to global statistical datasets including demographic, economic, and environmental indicators via the Data Commons API.

关于

The Data Commons Client skill empowers Claude to interact with the Data Commons knowledge graph, a unified repository of public data aggregated from authoritative sources like the US Census, WHO, and World Bank. It enables users to resolve geographic entities, explore complex relationship nodes, and fetch time-series statistical observations directly within their development workflow. This skill is particularly useful for data scientists and developers who need to integrate verified public statistics into applications, perform regional trend analysis, or automate data collection for research purposes without manually scraping multiple government websites.

主要功能

3 GitHub stars
Seamlessly convert statistical responses into Pandas DataFrames for analysis
Query time-series statistical data for global entities and regions
Resolve place names, coordinates, and Wikidata IDs to Data Commons identifiers
Explore geographic hierarchies and entity relationships in the knowledge graph
Filter and discover available variables across economic, health, and environmental domains

使用场景

Analyzing historical economic trends like GDP and unemployment across different states or countries
Building data-driven applications that require real-time demographic or health prevalence statistics
Standardizing geographic entity identifiers across disparate datasets using DCID resolution

关于

主要功能

3 GitHub stars
Seamlessly convert statistical responses into Pandas DataFrames for analysis
Query time-series statistical data for global entities and regions
Resolve place names, coordinates, and Wikidata IDs to Data Commons identifiers
Explore geographic hierarchies and entity relationships in the knowledge graph
Filter and discover available variables across economic, health, and environmental domains

使用场景

Analyzing historical economic trends like GDP and unemployment across different states or countries
Building data-driven applications that require real-time demographic or health prevalence statistics
Standardizing geographic entity identifiers across disparate datasets using DCID resolution