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matchms

Processes, cleans, compares, and searches tandem mass spectra with matchms. Use for MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity, library matching, score matrices, and molecular-similarity networks. Use pyopenms instead for LC-MS feature detection or proteomics pipelines.

Quality

94%

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Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

92%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.

An exemplary skill body: executable code throughout, a validated multi-step workflow with failure-mode handling, and clean one-level-deep progressive disclosure into six real reference files. The only trim opportunity is the Purpose and Scope section duplicating the frontmatter description and the lengthy citation protocol.

Suggestions

Compress the 'Use matchms for' / 'Do not use matchms as a replacement for' bullet lists, which restate the frontmatter description almost verbatim, into a single short scope sentence.

Consider moving the full citation-fetching protocol (arXiv API details, version-suffix rules) into a reference file and keeping a two-line citation reminder in SKILL.md to reclaim context tokens.

DimensionReasoningScore

Conciseness

The body is dense with version-pinned, non-obvious knowledge (removed APIs, StackedSparseArray outputs, tolerance semantics) and avoids explaining concepts Claude already knows, but the 'Purpose and Scope' bullets ('Use matchms for: ...' / 'Do not use matchms as a replacement for: ...') largely restate the frontmatter description, and the ~16-line citation protocol is a trimmable add-on — fitting the level 4 'minor instances that could be trimmed' anchor rather than 'every token earns its place'.

4 / 5

Actionability

Guidance is fully executable: pinned install commands with a verification one-liner, a complete runnable Quick Start (load, harmonize, filter, score, ranked output extraction), a working pair() snippet, a Spectrum construction snippet, and a parameterized CLI invocation — copy-paste ready and covering the common cases per the level 5 anchor.

5 / 5

Workflow Clarity

The 8-step Operating Workflow is clearly sequenced with explicit validation checkpoints ('Drop invalid spectra explicitly', 'Estimate len(references) * len(queries) before scoring', 'Validate top hits visually and chemically'), handles the known failure mode (filters returning None), and is backed by a 'Non-Negotiable Checks' checklist, satisfying the level 5 anchor including validation for the batch all-pairs operation.

5 / 5

Progressive Disclosure

The body is a well-signaled overview pointing one level deep into six reference files, each introduced with a one-line scope under 'Read only the reference needed for the task', with all cited paths (references/importing_exporting.md, filtering.md, similarity.md, workflows.md, migration.md, sources.md, scripts/library_search.py) verified to exist and bulk detail correctly split out of SKILL.md.

5 / 5

Total

19

/

20

Passed

Description

92%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 concrete third-person capabilities, an explicit 'Use for' trigger clause, and active disambiguation against pyopenms. The only gap is missing file-format trigger terms (MGF, MSP, mzML, mzXML) that users would naturally mention when asking for spectral library work.

Suggestions

Add the supported file formats (MGF, MSP, mzML, mzXML) to the trigger clause so users asking to 'match my MGF file against an MSP library' hit the skill's natural keywords.

Consider mentioning analog/neutral-loss search or 'spectral networking' phrasing users may use, since 'molecular-similarity networks' alone may not match how users phrase those requests.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions ('Processes, cleans, compares, and searches tandem mass spectra', 'MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity, library matching, score matrices, and molecular-similarity networks') with comprehensive coverage of the package's capabilities, matching the top anchor rather than the 'minor gaps' level 4 anchor.

5 / 5

Completeness

It explicitly answers both 'what' ('Processes, cleans, compares, and searches tandem mass spectra with matchms') and 'when' ('Use for MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity, library matching, score matrices, and molecular-similarity networks') with concrete trigger phrases, exactly matching the level 5 anchor.

5 / 5

Trigger Term Quality

Natural terms like 'tandem mass spectra', 'MS/MS', 'spectral similarity', and 'library matching' are present, but the description omits the file formats users naturally mention (MGF, MSP, mzML), so it falls short of the level 5 anchor's 'including synonyms and file extensions' while exceeding level 3's 'missing common variations'.

4 / 5

Distinctiveness Conflict Risk

It occupies a clear niche (tandem mass spectrometry with matchms) and actively disambiguates against the nearest competing skill ('Use pyopenms instead for LC-MS feature detection or proteomics pipelines'), giving minimal conflict risk as the level 5 anchor requires.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/matchms/SKILL.md

Reviewed

Table of Contents

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