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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.

65

Quality

80%

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SecuritybySnyk

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tessl review fix ./backend/cli/skills/chemistry/matchms/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%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.

A well-structured, code-rich skill body with excellent progressive disclosure through real reference files and mostly executable examples. Its main weakness is the absence of validation/verification checkpoints in batch workflows, which caps workflow clarity.

Suggestions

Add an explicit validate-then-proceed feedback loop to the pipeline and scoring examples (e.g., inspect score distributions or check that processed spectra still have required peaks before downstream use) to raise workflow clarity for these batch operations.

Make code examples self-contained by defining or noting the source of placeholder variables such as library_spectra, query_spectra, and reference_spectrum so snippets are fully copy-paste ready.

Trim mildly padded section introductions (e.g., 'Apply comprehensive filters to standardize metadata and refine peak data') to leaner imperatives for a small conciseness gain.

DimensionReasoningScore

Conciseness

Largely efficient and code-heavy with no over-explanation of basic concepts, but section intro lines like 'Apply comprehensive filters to standardize metadata and refine peak data' are slightly padded and could be trimmed, fitting 'efficient; minor instances of over-explanation'.

4 / 5

Actionability

Provides many concrete, mostly-executable Python blocks with real imports across importing, filtering, similarity, pipelines, and spectrum objects, but several examples reference undefined variables (library_spectra, query_spectra, reference_spectrum) without setup, leaving minor gaps short of fully copy-paste-ready.

4 / 5

Workflow Clarity

Capabilities are sequenced as numbered sections and the pipeline example shows define-then-apply, but validation checkpoints are absent throughout; since similarity scoring and library matching are batch operations, the missing feedback loops cap this score at 3 per the batch-operation guideline.

3 / 5

Progressive Disclosure

The body is a clear overview with well-signaled, one-level-deep references to four real files in references/ (filtering.md, similarity.md, importing_exporting.md, workflows.md), cited inline and enumerated in a Reference Documentation section, giving easy navigation.

5 / 5

Total

16

/

20

Passed

Description

87%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.

A strong, well-targeted description that clearly states capabilities and explicit use-conditions with concrete trigger phrases and helpful boundary guidance against the pyopenms skill. Minor gains available from adding file-extension trigger terms.

DimensionReasoningScore

Specificity

Lists several concrete actions ('comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries') with minor coverage gaps (no mention of filtering/importing), matching the 'several specific actions; minor gaps' anchor rather than fully comprehensive coverage.

4 / 5

Completeness

Explicitly answers both 'what' (spectral similarity and compound identification) and 'when' via concrete trigger phrases ('Use for comparing mass spectra...', 'Best for metabolite identification, spectral matching, library searching'), matching the top anchor.

5 / 5

Trigger Term Quality

Good keyword coverage including natural synonyms ('metabolite identification', 'spectral matching', 'library searching') and metric names ('cosine', 'modified cosine'), but missing common file extensions users might mention (.mgf, .msp, .mzML), so it sits just below the comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (metabolomics spectral similarity/ID) and includes explicit boundary guidance ('For full LC-MS/MS proteomics pipelines use pyopenms') that minimizes conflict risk with adjacent skills.

5 / 5

Total

18

/

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

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository
synthetic-sciences/openscience
Reviewed

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