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matchms

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

75

Quality

93%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

87%

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

A well-organized, actionable reference skill with lean code examples and sound progressive disclosure. Its main gap is workflow clarity: the body describes capabilities per section rather than presenting a validated, end-to-end analysis workflow inline.

Suggestions

Add one concrete end-to-end workflow inline (load -> filter -> score -> inspect best matches) with an explicit validation/checkpoint step, rather than only deferring workflows to references/workflows.md.

Remove the duplicate "Reference Documentation" section at the end since each reference is already linked in its respective capability section, reducing token redundancy.

For the SpectrumProcessor pipeline, add a brief validation note (e.g., checking that processed spectra retain required peaks) so the sequence has a feedback checkpoint.

DimensionReasoningScore

Conciseness

The body is lean: short section intros, executable code, and tight bullet lists of filter categories and similarity functions, without explaining what mass spectrometry or libraries are. The only minor redundancy is the reference list repeated in the closing section, but every token largely earns its place.

3 / 3

Actionability

Each capability section provides complete, executable code with real imports, concrete function calls, and parameters (e.g., CosineGreedy(), select_by_relative_intensity(..., intensity_from=0.01)), making examples copy-paste ready.

3 / 3

Workflow Clarity

Per-capability sequences are clear (e.g., the SpectrumProcessor define-then-apply pipeline), but there is no end-to-end workflow with explicit validation checkpoints, and the multi-step analysis workflows are deferred to references/workflows.md rather than sequenced inline.

2 / 3

Progressive Disclosure

SKILL.md is a clear overview with well-signaled, one-level-deep references to real files (references/filtering.md, similarity.md, importing_exporting.md, workflows.md), and content is appropriately split with easy navigation.

3 / 3

Total

11

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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.

A strong, third-person description that names concrete capabilities, includes natural trigger terms, and gives explicit when-to-use guidance plus routing boundary. It is concise without padding or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "comparing mass spectra, computing similarity scores (cosine, modified cosine)," "identifying unknown compounds from spectral libraries," "spectral matching, library searching" — rather than vague language.

3 / 3

Completeness

Explicitly answers what ("Spectral similarity and compound identification for metabolomics") and when ("Use for comparing mass spectra..." and "Best for metabolite identification, spectral matching, library searching"), with an explicit trigger clause.

3 / 3

Trigger Term Quality

Covers natural terms a metabolomics user would say — "mass spectra," "cosine," "spectral matching," "library searching," "metabolite identification" — with good variation rather than jargon-only phrasing.

3 / 3

Distinctiveness Conflict Risk

The metabolomics MS/MS niche is distinct, and routing guidance ("For full LC-MS/MS proteomics pipelines use pyopenms") reduces conflict with adjacent skills.

3 / 3

Total

12

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
foryourhealth111-pixel/Vibe-Skills
Reviewed

Table of Contents

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