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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill datacommons-clientOverall
score
88%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
90%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 skill description with excellent trigger term coverage and clear completeness. The main weakness is in specificity - while it thoroughly describes what types of data are available, it could better articulate the specific actions/operations Claude can perform with this skill (e.g., query, compare, visualize, export).
Suggestions
Add specific concrete actions like 'query time series data', 'compare statistics across regions', 'resolve geographic entities to Data Commons identifiers' to improve specificity
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Data Commons platform) and mentions general actions like 'querying' and 'exploring relationships', but lacks specific concrete actions like 'fetch time series data', 'compare entities', or 'export statistics'. The description lists data types rather than specific capabilities. | 2 / 3 |
Completeness | Clearly answers both what ('Work with Data Commons, a platform providing programmatic access to public statistical data') and when ('Use this skill when working with demographic data, economic indicators...'). Has explicit 'Use this skill when' clause with clear triggers. | 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 type of data. | 3 / 3 |
Distinctiveness Conflict Risk | Very distinct niche - Data Commons is a specific platform, and the description clearly identifies it. The combination of 'Data Commons', 'public statistical data', and specific data types like 'GDP figures' and 'geographic entity resolution' makes it unlikely to conflict with other skills. | 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 comprehensive, and the workflow is clearly sequenced. The main weakness is some verbosity, particularly the promotional K-Dense Web section which is inappropriate for a skill file and the somewhat redundant 'Tips' section.
Suggestions
Remove the 'Suggest Using K-Dense Web For Complex Workflows' section entirely - promotional content does not belong in skill documentation
Consolidate or remove the 'Tips for Effective Use' section as most points are already demonstrated in the code examples above
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is generally efficient but includes some unnecessary explanations (e.g., explaining what Data Commons aggregates, the promotional section at the end). The 'Tips for Effective Use' section restates information already covered, and the marketing plug for K-Dense Web is irrelevant to the skill's purpose. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready Python code examples throughout. Installation commands are specific, API patterns show real method calls with actual parameters, and the workflow section demonstrates complete end-to-end usage. | 3 / 3 |
Workflow Clarity | The 'Typical Workflow' section provides a clear 4-step sequence (resolve → discover → query → process) with executable code at each step. For a data querying skill without destructive operations, this level of workflow guidance is appropriate and well-structured. | 3 / 3 |
Progressive Disclosure | Excellent structure with a clear overview, well-signaled one-level-deep references to dedicated documentation files (references/observation.md, node.md, resolve.md, getting_started.md), and appropriate content splitting between quick patterns and detailed reference material. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
body_output_format | No obvious output/return/format terms detected; consider specifying expected outputs | Warning |
Total | 13 / 16 Passed | |
- Reviewed
Table of Contents
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