datacommons-client

Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.

Quality

86%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

High

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Quality

Content

86%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a well-structured, highly actionable API client guide that offloads detail to real reference files and leads with executable examples. Tightening the Overview and use-case bullets, and adding API error-handling guidance in the workflow, would push it to the top band.

Suggestions

Trim the Overview paragraph and "Primary use cases" bullets that restate knowledge Claude already has, and reduce inline code in Core Capabilities to one example per endpoint since full docs live in the reference files.

Add a brief error-handling/validation note to the Typical Workflow (e.g., check response status, handle empty candidate lists from resolve) so the sequence has explicit feedback checkpoints.

Consolidate "Finding Statistical Variables" and "Working with Pandas" into the relevant reference files or the getting_started guide to reduce SKILL.md length while keeping the overview navigable.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient and API-specific with concrete code, but the Overview paragraph and some "Primary use cases" bullets restate context Claude can infer, and inline Core Capabilities examples partially duplicate the per-endpoint reference files.	4 / 5
Actionability	Provides copy-paste-ready, executable code for installation, all three endpoints, the typical workflow, and Pandas conversion, covering the common cases thoroughly.	5 / 5
Workflow Clarity	The "Typical Workflow" gives a clear four-step sequence (resolve → discover variables → query → process) with code, but lacks explicit error-handling or validation checkpoints for API failures, keeping it just below 5.	4 / 5
Progressive Disclosure	SKILL.md acts as a clear overview with well-signaled, one-level-deep references to observation.md, node.md, resolve.md, and getting_started.md, all of which exist in references/; detail is appropriately split for easy navigation.	5 / 5
	Total	18 / 20 Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is clear, specific, and third-person, explicitly covering both what the skill does and when to use it with concrete Data Commons trigger terms. Minor gains are possible by adding a few more synonyms or format cues, but it is otherwise strong.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions — "querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities" — with only minor coverage gaps, fitting the "lists several specific actions" anchor rather than the fully comprehensive 5.	4 / 5
Completeness	Explicitly answers both what ("programmatic access to public statistical data ... querying ... entity resolution") and when ("Use this skill when working with demographic data, economic indicators ...") with concrete trigger phrases, matching the top anchor.	5 / 5
Trigger Term Quality	Strong natural keywords users would say ("demographic data, economic indicators, health statistics, population statistics, GDP figures, unemployment rates") with good coverage, but a few synonyms or file/format variations are missing, keeping it just below 5.	4 / 5
Distinctiveness Conflict Risk	Names the Data Commons niche explicitly and repeatedly with distinct statistical-graph triggers, yielding minimal conflict risk with other skills.	5 / 5
	Total	18 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning

	Total	15 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/datacommons-client/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

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