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

85%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

High

Do not use without reviewing

Quality

Content

70%

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

The content is highly actionable with executable examples and a clean progressive-disclosure structure pointing to real reference files. Its weaknesses are minor verbosity (repeated explanations of endpoint purposes Claude could infer) and a workflow that lacks explicit validation checkpoints despite involving resolution-then-query sequences.

Suggestions

Tighten the body by removing restated endpoint purposes already conveyed by the headings and the 'Overview', and consolidate the overlapping fetch examples across 'Common patterns', 'Typical Workflow', and 'Working with Pandas'.

Add explicit validation checkpoints to the Typical Workflow, e.g. confirm resolved DCIDs are non-empty before querying and handle empty/missing observation responses before converting to DataFrames.

Trim background exposition such as 'Data Commons aggregates data from census bureaus, health organizations, environmental agencies...' which restates facts inferable from the platform name and description.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient with executable code, but it includes some content Claude already knows (e.g. 'Data Commons aggregates data from census bureaus...'), restates endpoint purposes already clear from headings, and the per-endpoint 'Common patterns' plus the standalone 'Working with Pandas' and 'Typical Workflow' sections overlap in the same fetch examples, so it could be tightened.	2 / 3
Actionability	It provides concrete, copy-paste-ready Python snippets for each endpoint (fetch, fetch_property_labels, fetch_dcids_by_name, etc.) with real parameters and return handling, plus exact install and API-key commands, fully meeting the executable-code anchor.	3 / 3
Workflow Clarity	The 'Typical Workflow' lists a clear four-step sequence, but it lacks explicit validation checkpoints or error-recovery feedback loops for a data-query workflow (e.g. verifying DCIDs were resolved before querying, handling empty responses), capping it at 2 per the destructive/batch-operation guidance.	2 / 3
Progressive Disclosure	The body is a well-organized overview that signals one-level-deep references to real files (references/observation.md, node.md, resolve.md, getting_started.md, all confirmed present) with clear navigation, matching the clear-overview-plus-well-signaled-references anchor.	3 / 3
	Total	10 / 12 Passed

Description

100%

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 strong: it clearly states the platform and purpose, provides an explicit 'Use when' trigger clause, and lists concrete use cases with natural keywords. It is concise and avoids vague fluff while remaining clearly distinguishable from other data skills.

Dimension	Reasoning	Score
Specificity	The description lists multiple concrete actions such as 'querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities', matching the anchor for listing several specific concrete actions.	3 / 3
Completeness	It explicitly states what the skill does ('providing programmatic access to public statistical data') and when to use it via the explicit 'Use this skill when working with...' clause, answering both what and when.	3 / 3
Trigger Term Quality	It covers natural terms a user would say — 'demographic data, economic indicators, health statistics, environmental data', 'population statistics, GDP figures, unemployment rates', and 'entity resolution' — giving good coverage of common phrasings.	3 / 3
Distinctiveness Conflict Risk	The niche is clearly scoped to Data Commons public statistical datasets with distinct triggers (statistical data from global sources, statistical entities), making it unlikely to trigger for unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/databases/datacommons-client/SKILL.md
Commit: 52845c3

Reviewed: about 9 hours ago

Table of Contents

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