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jimmc414
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datacommons-client

Query statistical observations and explore the knowledge graph via the Data Commons API.

557|98|Updated Nov 7, 2025
Search Tags:#knowledge graph#data analysis#statistics#api client#data commons#public data
One-click install
npx skills add https://github.com/jimmc414/Kosmos --skill datacommons-client-jimmc414
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datacommons-client
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/datacommons-client
Command: npx skills add https://github.com/jimmc414/Kosmos --skill datacommons-client-jimmc414

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides programmatic access to a vast repository of public statistical data, enabling users to query, analyze, and integrate diverse datasets without manual data collection.

Core Features & Use Cases

  • Statistical Data Queries: Retrieve time-series data for population, economic, health, and environmental indicators.
  • Knowledge Graph Exploration: Discover entity relationships, properties, and navigate geographic hierarchies.
  • Entity Resolution: Translate entity names, coordinates, or external IDs into Data Commons IDs (DCIDs) for seamless querying.
  • Use Case: Analyze historical unemployment rates for the USA, or compare median household incomes across different California counties.

Quick Start

Use the datacommons-client skill to get the latest population data for California and Texas.

Frequently Asked Questions about datacommons-client

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I retrieve public statistical data like population or unemployment rates using an API client?▼

You can retrieve public statistical data by using the Python client library to query the Data Commons API, facilitating time-series data retrieval for demographic, economic, health, and environmental indicators from global public sources.

What is the best way to explore the knowledge graph of public datasets programmatically?▼

Exploring the knowledge graph programmatically involves querying the Data Commons API to discover entity relationships, navigate geographic hierarchies, and resolve external IDs or coordinates into Data Commons IDs.

Can I use Python to compare median household incomes across different geographic regions?▼

Yes, you can use the Python client to query the Data Commons API for time-series statistical observations, enabling you to compare economic indicators like median household incomes across geographic entities such as California counties.

Does the Data Commons API support time-series analysis for environmental statistics?▼

The Data Commons API supports time-series analysis by allowing you to retrieve statistical observations for environmental indicators, enabling programmatic access to historical environmental data without manual collection.

How do I resolve entity names or coordinates into Data Commons IDs for seamless querying?▼

Entity resolution translates entity names, external IDs, or geographic coordinates into Data Commons IDs (DCIDs), enabling seamless and accurate querying of statistical observations within the knowledge graph.

What are the limitations of using a Python API client for hierarchical data querying?▼

Hierarchical data querying relies entirely on the Data Commons API's available public sources and entity resolution accuracy, meaning your analysis is constrained by the coverage and granularity of the statistical data within the knowledge graph.

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