metabolomics-workbench-database
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
84
77%
Does it follow best practices?
Impact
99%
1.57xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/metabolomics-workbench-database/SKILL.mdQuality
Discovery
82%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, domain-specific description with excellent specificity and trigger terms for the metabolomics research community. The main weakness is the lack of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The technical terminology is appropriate for the target audience.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about metabolomics data, metabolite identification, mass spectrometry searches, or needs to access Metabolomics Workbench studies.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata' - these are distinct, actionable capabilities. | 3 / 3 |
Completeness | Clearly describes WHAT it does (query various metabolomics data types via REST API) but lacks an explicit 'Use when...' clause. The purpose is implied through 'for metabolomics and biomarker discovery' but doesn't provide explicit trigger guidance. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'metabolites', 'RefMet', 'MS/NMR', 'm/z searches', 'metabolomics', 'biomarker discovery', 'NIH Metabolomics Workbench' - good coverage of domain-specific terms. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with specific domain (NIH Metabolomics Workbench), specific data types (RefMet, MS/NMR, m/z), and clear niche - unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid API reference skill with excellent actionability through executable code examples and good progressive disclosure. The main weaknesses are moderate verbosity in introductory sections and missing validation/error handling in workflows, which is important for API interactions that can fail or return unexpected data.
Suggestions
Remove or condense the 'Overview' and 'When to Use This Skill' sections - Claude doesn't need explanations of what the Metabolomics Workbench is or when to use an API skill
Add response validation to workflows (e.g., 'Check response.status_code == 200 before parsing JSON', 'Verify expected keys exist in response')
Add error handling examples for common API failure modes (rate limiting, invalid identifiers, empty results)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains some unnecessary explanation (e.g., 'comprehensive NIH Common Fund-sponsored platform hosted at UCSD' in overview) and the 'When to Use This Skill' section restates what's obvious from the title. However, the code examples are reasonably lean. | 2 / 3 |
Actionability | Provides fully executable Python code examples with real API endpoints throughout. All examples are copy-paste ready with actual URLs and proper requests library usage. | 3 / 3 |
Workflow Clarity | Workflows are clearly sequenced with numbered steps, but lack validation checkpoints. No error handling, response validation, or feedback loops for when API calls fail or return unexpected results. | 2 / 3 |
Progressive Disclosure | Well-structured with clear sections, appropriate use of headers, and explicit reference to detailed API documentation in 'references/api_reference.md'. Content is appropriately split between overview and detailed reference. | 3 / 3 |
Total | 10 / 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
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