CtrlK
BlogDocsLog inGet started
Tessl Logo

metabolomics-workbench-database

Access NIH Metabolomics Workbench (4,200+ studies) via REST API. Query metabolites, RefMet nomenclature, MS/NMR data, m/z search, study metadata for metabolomics and biomarker discovery.

57

Quality

66%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/metabolomics-workbench-database/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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 content is well-structured with executable REST examples and a genuine one-level reference file, but it carries redundant boilerplate sections (When to Use, Key Features, Implementation Details) that pad the token budget without adding value. Workflow clarity is strong but lacks explicit validation feedback loops.

Suggestions

Collapse the duplicated 'When to Use' sections and remove generic boilerplate ('Documentation-first workflow', 'Example run plan') to tighten conciseness toward 4-5.

Add an explicit validate/retry step in workflows for large result sets (e.g. check response status and handle pagination per the existing 'Handle Pagination' best practice).

Replace the 'See ## Overview above' placeholder in Implementation Details with a concrete note or remove the section entirely.

DimensionReasoningScore

Conciseness

Mostly efficient with executable code blocks, but the opening 'When to Use', 'Key Features', 'Dependencies', 'Example Usage', and 'Implementation Details' sections restate the description and add generic filler ('Documentation-first workflow with no packaged script requirement') that could be trimmed.

3 / 5

Actionability

Provides concrete, executable requests.get() examples across all six capability areas plus three worked workflows, with only minor gaps (e.g. no error handling or auth notes, though none is required).

4 / 5

Workflow Clarity

The three Common Workflows give clear sequenced steps with concrete URLs, and Best Practices act as implicit checkpoints, though there are no explicit validate/retry feedback loops for batch API calls.

4 / 5

Progressive Disclosure

The body is a well-organized overview that defers the full API spec to references/api_reference.md (a real, one-level-deep file), with inline examples for the common cases; minor gaps are the duplicate 'When to Use' sections and a redundant Implementation Details pointer.

4 / 5

Total

15

/

20

Passed

Description

66%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 specific and domain-distinct with strong concrete capabilities, but it omits an explicit 'when to use' trigger clause, which caps completeness. Trigger terms are solid but could add synonyms like 'mass spectrometry'.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when querying metabolomics data, standardizing metabolite names with RefMet, or searching compounds by m/z.'

Include synonyms such as 'mass spectrometry' and 'metabolomics database' to broaden natural trigger coverage.

Add a third person voice consistency check; the current phrasing is already third person, so preserve that as the description grows.

DimensionReasoningScore

Specificity

Lists several concrete actions (query metabolites, RefMet nomenclature, MS/NMR data, m/z search, study metadata) tied to a named data source, with minor coverage gaps but no significant over-claims.

4 / 5

Completeness

The description answers 'what' clearly with concrete capabilities, but has no explicit 'Use when...' clause or equivalent trigger guidance, which caps completeness at 3 per the rubric.

3 / 5

Trigger Term Quality

Good natural keyword coverage (metabolites, m/z search, study metadata, biomarker discovery) including the domain's vocabulary, though it lacks common synonyms or extensions like 'mass spectrometry'.

4 / 5

Distinctiveness Conflict Risk

The NIH Metabolomics Workbench niche with RefMet and m/z search is mostly distinct from other skills, with only minor overlap risk against adjacent biomedical database skills.

4 / 5

Total

15

/

20

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

14

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.