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datacommons-client

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

1|Updated Jan 14, 2026
Search Tags:#knowledge graph#data retrieval#api client#statistical data#data commons#public datasets
One-click install
npx skills add https://github.com/Sologa/codex-pipeline --skill datacommons-client-sologa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datacommons-client
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/datacommons-client
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill datacommons-client-sologa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides programmatic access to Data Commons, a vast repository of public statistical data, enabling users to easily query and analyze demographic, economic, health, and environmental information.

Core Features & Use Cases

  • Query Statistical Data: Retrieve time-series data for various entities and variables (e.g., population, GDP, unemployment rates).
  • Explore Knowledge Graph: Navigate entity relationships, discover properties, and understand data connections.
  • Entity Resolution: Translate human-readable names or coordinates into Data Commons IDs (DCIDs) for precise querying.
  • Use Case: A researcher needs to compare the median income and unemployment rates across all US states for the last five years. This Skill can resolve state names to DCIDs, fetch the required statistical variables for the specified date range, and return the data in a structured format for analysis.

Quick Start

Use the datacommons-client skill to fetch 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 for demographic and economic indicators?▼

You retrieve public statistical data by querying Data Commons to fetch time-series observations for demographic and economic indicators, resolving specific entities like US states into Data Commons IDs for precise structured results.

What is the Data Commons knowledge graph and how do I explore it?▼

The Data Commons knowledge graph maps relationships between statistical entities. You explore it by navigating entity connections, discovering properties, and understanding data hierarchies using the Python API client.

How do I resolve human-readable names into Data Commons IDs for time-series analysis?▼

You resolve human-readable names or coordinates into Data Commons IDs (DCIDs) using entity resolution features, enabling precise querying of time-series statistical variables across specified date ranges.

Can I query global health and environmental indicators using the Data Commons Python API?▼

Yes, you can query global health and environmental indicators using the Data Commons Python API to retrieve statistical observations and perform hierarchical data queries from worldwide public datasets.

Does the datacommons-client support querying unemployment rates and median income across US states?▼

Yes, datacommons-client supports querying unemployment rates and median income across US states by resolving state names to DCIDs and fetching required statistical variables for structured analysis.

What are the limitations of using Data Commons for hierarchical data queries?▼

Limitations of Data Commons hierarchical data queries depend on the availability and granularity of public datasets, requiring accurate entity resolution to DCIDs to avoid fetching incomplete statistical observations.

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