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.
90
87%
Does it follow best practices?
Impact
95%
4.52xAverage score across 3 eval scenarios
Risky
Do not use without reviewing
Quality
Discovery
89%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong description with excellent trigger term coverage and clear 'when to use' guidance. The main weakness is the vague opening 'Work with' which doesn't specify concrete actions the skill enables. The description would benefit from listing specific capabilities like querying, retrieving, or visualizing data.
Suggestions
Replace 'Work with Data Commons' with specific action verbs like 'Query and retrieve statistical data from Data Commons' or 'Access, query, and analyze public datasets via Data Commons'
Add concrete capabilities such as 'resolve geographic entities', 'compare statistics across regions', or 'retrieve time-series data' to strengthen specificity
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Data Commons platform) and mentions data types (demographic, economic, health, environmental) but lacks concrete actions. Uses vague 'Work with' instead of specific verbs like 'query', 'retrieve', 'analyze', or 'export'. | 2 / 3 |
Completeness | Clearly answers both what (platform for accessing public statistical data) and when ('Use this skill when working with demographic data, economic indicators...'). Has explicit 'Use this skill when' clause with multiple trigger scenarios. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'demographic data', 'economic indicators', 'health statistics', 'population statistics', 'GDP figures', 'unemployment rates', 'disease prevalence'. These are terms users would naturally use when seeking this data. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with 'Data Commons' as a specific platform name and unique combination of public statistical data sources. Unlikely to conflict with general data analysis skills due to the specific platform focus and data types mentioned. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with excellent actionability and progressive disclosure. The code examples are executable and the workflow is clearly sequenced. The main weakness is some verbosity in explanatory text that could be trimmed, particularly the overview paragraph and tips section which explain things Claude would already understand.
Suggestions
Remove or significantly trim the overview paragraph explaining what Data Commons aggregates - Claude doesn't need this context
Condense the 'Tips for Effective Use' section - most of these are either obvious or already demonstrated in the examples above
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is reasonably efficient but includes some unnecessary explanations (e.g., 'Data Commons aggregates data from census bureaus...') and could be tightened. The tips section at the end contains advice Claude would likely infer. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples throughout. Installation commands, API patterns, and workflow examples are all concrete and immediately usable. | 3 / 3 |
Workflow Clarity | The 'Typical Workflow' section provides a clear 4-step sequence with code for each step. The progression from resolution → discovery → query → processing is explicit and well-structured. | 3 / 3 |
Progressive Disclosure | Excellent structure with clear overview in main file and well-signaled one-level-deep references to detailed documentation in `references/` directory. Each endpoint has its own reference file clearly linked. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
71add64
- Reviewed
Table of Contents
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