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matchms

Process, clean, compare, and search 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.

80

Quality

100%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

100%

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

The body is a dense, version-locked overview: executable code, a sequenced workflow with validation checkpoints, and well-signaled one-level-deep references that all resolve to real bundle files. It earns top marks across all four dimensions.

DimensionReasoningScore

Conciseness

The body assumes Claude's competence (no basic mass-spectrometry explanations) and every section adds version-specific, non-obvious knowledge; breaking-change and time-sensitive material is concentrated in the API Guardrails and migration reference rather than scattered. It is long but dense, not padded.

3 / 3

Actionability

Multiple complete, executable code blocks (load_and_process + calculate_scores, pair scoring, Spectrum construction) and a concrete library_search.py CLI invocation are copy-paste ready with version-correct APIs.

3 / 3

Workflow Clarity

The 8-step Operating Workflow is explicitly sequenced with validation checkpoints (step 4 drop invalid spectra, step 6 estimate pair count before scoring, step 8 validate top hits), reinforced by a Non-Negotiable Checks list covering batch/destructive risks.

3 / 3

Progressive Disclosure

SKILL.md is a focused overview with clearly signaled, one-level-deep references; a labeled References section lists all six reference files and the bundled script, each verified present in the bundle, with no nested-reference chains.

3 / 3

Total

12

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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.

The description is specific, trigger-rich, and self-contained, with a strong what/when structure and an explicit contrast against pyopenms that bounds its niche. It earns top marks across all four dimensions.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Process, clean, compare, and search tandem mass spectra') and enumerates specific capabilities (MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity, library matching, score matrices, molecular-similarity networks), matching the 'multiple specific concrete actions' anchor.

3 / 3

Completeness

Explicitly answers both 'what' ('Process, clean, compare, and search tandem mass spectra with matchms') and 'when' ('Use for MS/MS file I/O, metadata harmonization, peak filtering…') with an explicit trigger clause.

3 / 3

Trigger Term Quality

Natural domain terms a mass-spectrometry user would say ('tandem mass spectra', 'MS/MS', 'spectral similarity', 'library matching') are well covered, and an explicit 'Use for…' trigger clause is present.

3 / 3

Distinctiveness Conflict Risk

A clear MS/MS-similarity niche is sharpened by an explicit negative carve-out ('Use pyopenms instead for LC-MS feature detection or proteomics pipelines'), making it unlikely to trigger for the wrong skill.

3 / 3

Total

12

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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