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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.

68

Quality

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%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 library skill: executable snippets per capability, a sensible capability order, and exemplary progressive disclosure into four real reference files. The deductions are minor — slightly redundant navigation, a few undefined example variables, and no inline validation loop.

Suggestions

Drop the closing 'Reference Documentation' section (or the per-section pointers) — listing all four reference files twice is redundant navigation.

Define the example inputs once (e.g., show `spectra = list(load_from_msp('library.msp'))` before the calculate_scores snippet) so the similarity code is copy-paste runnable.

Note the matplotlib dependency for `spectrum.plot()` alongside the existing installation section.

DimensionReasoningScore

Conciseness

Efficient and code-forward with almost no explanation of concepts Claude already knows, but a few trimmable spots remain: the reference files are pointed to twice (per-section "consult references/..." plus the closing "Reference Documentation" list and "Load these references as needed"), and the Overview sentence mildly restates the description. Not a 5 because of that duplicated navigation; not a 3 since no section is genuinely padded.

4 / 5

Actionability

Mostly executable, concrete code for every capability (imports, filters, calculate_scores, Spectrum construction). Minor gaps: example variables like `library_spectra`, `query_spectra`, and `reference_spectrum` are used without definition, `spectrum.plot()` has no note that it needs matplotlib, and the SpectrumProcessor lambda-wrapping pattern is slightly unusual. Not a 5 because those gaps keep it from copy-paste-ready in every snippet; well above the 3 pseudocode anchor.

4 / 5

Workflow Clarity

Sections are sequenced logically (import → filter → similarity → pipelines → spectrum objects → metadata) and the "Common Workflows" section routes to references/workflows.md for the multi-step patterns, with QC-style filters ("require minimum peaks, validate precursor m/z") serving as implicit checkpoints. Not a 5 because no explicit validate-then-proceed loop appears inline; not a 3 since a clear sequence exists and the destructive/batch cap does not apply to this capability-reference skill.

4 / 5

Progressive Disclosure

Clear overview with well-signaled, one-level-deep references that all exist in the bundle: "For detailed importing/exporting documentation, consult `references/importing_exporting.md`", `references/filtering.md`, `references/similarity.md`, `references/workflows.md`. Bulk detail (40+ filters, full similarity catalog, workflow walkthroughs) is correctly split into those files while inline content stays at getting-started altitude; navigation is easy via the closing reference list.

5 / 5

Total

17

/

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 description with a clear what/when pair, natural trigger terms, named algorithms, and explicit boundary guidance against pyopenms. Its only weakness is slightly incomplete coverage of the library's full capability surface (I/O formats, filtering).

DimensionReasoningScore

Specificity

Lists several concrete actions — "comparing mass spectra", "computing similarity scores (cosine, modified cosine)", "identifying unknown compounds from spectral libraries" — even naming specific algorithms. Not a 5 because coverage has minor gaps: importing/filtering formats (MGF, MSP, mzML) and fingerprint similarity are absent, which are core capabilities shown in the body.

4 / 5

Completeness

Explicitly answers both: what — "Spectral similarity and compound identification for metabolomics" — and when — "Use for comparing mass spectra, computing similarity scores..., and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching." This mirrors the anchor-5 example structure with concrete trigger phrases in third-person voice, so it is not the 4 anchor where "when" is only weakly specified.

5 / 5

Trigger Term Quality

Good natural keyword coverage: "spectral matching", "library searching", "metabolite identification", "identifying unknown compounds", plus domain phrases "LC-MS/MS" and "mass spectra". Not a 5 because file extensions (.mgf, .msp, .mzML) and common synonyms ("mass spec", "MS/MS", "GNPS") that users would naturally say are missing.

4 / 5

Distinctiveness Conflict Risk

Clear niche (metabolomics spectral similarity/compound ID) with distinct triggers, and it proactively disambiguates the nearest neighbor: "For full LC-MS/MS proteomics pipelines use pyopenms." Minimal conflict risk; not the 4 anchor because overlap with related skills is explicitly resolved rather than merely minor.

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.

Validation — 14 / 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

Path

backend/cli/skills/chemistry/matchms/SKILL.md

Reviewed

Table of Contents

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