Skills datacommons-client

📦

datacommons-client

Name: datacommons-client
Author: davila7

Content revision r2 Safe 🌐 Network access🔑 Env variables⚙️ External commands

Query Public Statistics with Data Commons

Finding comparable public statistics across sources can require complex identifiers and API knowledge. This skill guides Data Commons queries, entity resolution, graph exploration, and result analysis.

Supports: Claude Codex Code(CC)

📊 76 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "datacommons-client" from https://skillstore.io/skills/davila7-datacommons-client.md and its manifest at https://skillstore.io/api/skills/davila7-datacommons-client/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/davila7-datacommons-client.md Signed manifest GET /api/skills/davila7-datacommons-client/manifest Signed lockfile GET /api/skills/davila7-datacommons-client/lockfile

Test it

Using "datacommons-client". Compare the latest population observations for California and Texas.

Expected outcome:

Both state names are resolved to selected Data Commons place identifiers.
The response compares the latest available population observations for both states.
Facet details identify the contributing data sources.

Using "datacommons-client". Show the United States unemployment trend and flag missing years.

Expected outcome:

The result lists dated unemployment observations in chronological order.
It identifies missing years and separates observations by source facet.
It summarizes the direction without inventing unavailable values.

Using "datacommons-client". Resolve Springfield as a city before retrieving statistics.

Expected outcome:

Several Springfield candidates may be returned.
The result distinguishes candidates by place type and context.
It requests confirmation before selecting an identifier.

Security Audit

Safe

v9 • 7/23/2026 Open versioned report

All 89 static findings are false positives caused by documented Data Commons API calls, placeholder credentials, Markdown formatting, and DCID resolution examples. No malicious intent, prompt injection, secret exposure, command execution, or unrelated network destination was found in the five reviewed documentation files.

Files scanned

1,352

Lines analyzed

Review items

False positives ignored

Risk Factors

🌐 Network access (36)

🔑 Env variables (3)

references/getting_started.md:14 SKILL.md:217 SKILL.md:218

⚙️ External commands (41)

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Open versioned report

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https://skillstore.io/skills/davila7-datacommons-client/audits/9?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

davila7. (2026). datacommons-client security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-datacommons-client/audits/9

BibTeX citation

@techreport{davila7-davila7-datacommons-client-2026,
  author = {davila7},
  title = {datacommons-client security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/davila7-datacommons-client/audits/9},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "datacommons-client security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
  - name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-datacommons-client/audits/9"
identifiers:
  - type: other
    value: "skillstore:davila7-datacommons-client:audit:9"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended Current

davila7-datacommons-client

Skillstore Score 76

Evidence Confidence High

Skillstore usage 8

Updated

2026-08-21

K-Dense-AI

k-dense-ai-datacommons-client

Skillstore Score 72

Evidence Confidence High

Skillstore usage 7

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Compare regional indicators

Resolve several regions and compare current population, income, or unemployment observations with consistent source facets.

Research historical trends

Retrieve dated observations, identify coverage gaps, and prepare a sourced time series for reporting.

Build public data workflows

Combine entity resolution, graph exploration, batch queries, and Pandas-ready records in a repeatable analysis process.

Try These Prompts

Retrieve one current statistic

Find the latest [statistical variable] for [place]. Resolve the place, verify data availability, and report the source facet.

Compare several places

Compare [statistical variables] across [places] for [date]. Resolve ambiguous names and present a clear table with source details.

Analyze a time series

Retrieve all observations for [variable] in [place]. Sort by date, identify missing periods, and summarize the trend without inventing values.

Query a geographic hierarchy

Query [variable] for every [child place type] within [parent place]. Filter by [facet domain], handle pagination, and explain source limitations.

Best Practices

Resolve names first and confirm ambiguous candidates before requesting observations.
Check variable availability before large queries to avoid empty or incomplete results.
Inspect and filter source facets when comparisons require consistent provenance.

Avoid

Do not accept the first candidate when a place name has multiple matches.
Do not combine observations from different facets without explaining source differences.
Do not assume the latest observation uses the same date for every entity.