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Import, process and compare mass spectrometry spectra (mzML/MGF/MSP) with filters, similarity metrics and reproducible pipelines for metabolomics workflows.
MatchMS provides a comprehensive Python toolkit for mass spectrometry data processing: importing/exporting mzML, MGF, MSP and JSON, harmonising metadata, filtering peaks, calculating spectral similarity (cosine, modified cosine, neutral-loss), and building reproducible processing pipelines for metabolomics. Concrete use cases include library searching, QC filtering, and large-scale similarity-based clustering.
Use this skill when you need to preprocess raw LC/MS or GC/MS spectra, normalise and filter peaks, perform library matching or compute pairwise spectral similarities to identify compounds or annotate unknowns. It is suitable for batch pipelines, research reproducibility, and integrating with GNPS-style workflows.
Likely used by Python-capable agents and environments (Codex/Copilot/Claude Code) that can run or author Python analysis pipelines.
MatchMS is a mass spectrometry data processing Python library skill. The SKILL.md is well-structured with clear sections, code examples, and references to supplementary docs. No scripts were included, so only static analysis was performed. No security concerns — no network calls, no shell execution, no credentials. The skill is niche: targets metabolomics researchers working with MS data, a small audience relative to general agent skill users.
Clean, well-documented skill with no security issues. Low usefulness score reflects the narrow audience (metabolomics/MS researchers) rather than quality. Architecture is good with proper frontmatter and separation of concerns via references/. Code quality is high with clear examples and installation instructions. Minor deduction on architecture for heavy delegation to references/ files that may not be available.
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