Skills datacommons-client
📊
datacommons-client
Safe ⚙️
External commands🌐
Network access🔑
Env variables
Query public statistics from Data Commons
Also available from: K-Dense-AI
Accessing demographic, economic, and health data from multiple global sources requires navigating complex APIs. This skill provides complete guidance for using the Data Commons Python client to query population statistics, unemployment rates, GDP figures, and other public datasets through a unified knowledge graph.
Supports: Claude Codex Code(CC)
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Test it
Using "datacommons-client". Get the latest population for California and Texas
Expected outcome:
- California: 39,144,585 people (2024)
- Texas: 29,145,505 people (2024)
- Data source: US Census Bureau
Using "datacommons-client". Show US unemployment trend from 2010 to 2023
Expected outcome:
- 2010: 9.6%
- 2015: 5.3%
- 2019: 3.7%
- 2020: 8.1% (pandemic impact)
- 2023: 3.6%
Using "datacommons-client". Get median income for all Florida counties
Expected outcome:
- Collier County: $78,000
- St. Johns County: $76,500
- Miami-Dade County: $62,000
- Data source: American Community Survey
Security Audit
Safev5 • 1/17/2026
Pure documentation skill with no executable code. All 284 static analyzer findings are false positives caused by misinterpreting markdown code blocks (```bash, ```python fences) as Ruby/shell backtick execution, and documentation examples as security vulnerabilities. This skill only contains markdown files teaching legitimate Data Commons API usage for accessing public statistical data.
6
Files scanned
1,554
Lines analyzed
3
findings
5
Total audits
Risk Factors
⚙️ External commands (200)
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🌐 Network access (46)
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🔑 Env variables (4)
Audited by: claude View Audit History →
Quality Score
45
Architecture
100
Maintainability
85
Content
20
Community
100
Security
91
Spec Compliance
What You Can Build
Economic trend analysis
Compare unemployment rates and income metrics across multiple states or countries over time
Demographic research
Access population statistics, age distributions, and health indicators for academic studies
Regional statistics lookup
Retrieve verified statistics for specific geographic regions to inform decisions
Try These Prompts
Basic population query
Get the latest population count for California and Texas using Data Commons
Time series analysis
Show me the unemployment rate time series for the United States from 2010 to 2023
Geographic hierarchy
Query median household income for all counties in Florida for the year 2020
Multi-variable comparison
Compare population, median age, and unemployment rate for California, New York, and Texas
Best Practices
- Always resolve place names to DCIDs before querying to ensure accuracy
- Use Pandas integration (to_observations_as_records) for data analysis
- Filter by data source (filter_facet_domains) when consistency matters
Avoid
- Hardcoding DCIDs without verifying they exist in the knowledge graph
- Making individual queries for each entity when batch queries are more efficient
- Ignoring data source provenance when combining data from multiple facets
Frequently Asked Questions
Does this skill work with Claude Code?
Yes. This skill supports Claude, Codex, and Claude Code. The Data Commons Python client works in any Python environment.
What are the rate limits for the Data Commons API?
Rate limits vary by API key tier. Free tier has lower limits; paid tiers offer higher throughput for production use.
Can I combine Data Commons data with my own datasets?
Yes. Use coordinate or Wikidata ID resolution to map your data to DCIDs, then merge using Pandas DataFrames.
Is my data safe when using this skill?
Yes. This skill only reads public data. Your queries and API key stay local; only request data from datacommons.org.
Why am I getting empty results for my query?
The entity or variable may not exist in Data Commons. Use fetch_available_statistical_variables to check available data.
How does Data Commons differ from other data sources?
Data Commons normalizes data from multiple authoritative sources (census, WHO, World Bank) into a unified knowledge graph.