matchms

Compare mass spectra against reference libraries using similarity metrics.

Updated Jan 11, 2026
One-click install
npx skills add https://github.com/mv-debug/kyco --skill matchms-mv-debug
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: matchms
Source: https://github.com/mv-debug/kyco/tree/main/.claude/skills/matchms
Command: npx skills add https://github.com/mv-debug/kyco --skill matchms-mv-debug

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

MatchMS simplifies processing of mass spectrometry data, enabling robust spectral similarity analysis and reliable compound identification by comparing unknown spectra to curated libraries.

Core Features & Use Cases

  • Importing and exporting common MS data formats (mzML, MGF, MSP, JSON)
  • Spectrum processing and metadata harmonization
  • Calculating multiple spectral similarity metrics (CosineGreedy, ModifiedCosine, NeutralLossesCosine, FingerprintSimilarity)
  • Building reproducible processing pipelines and workflows
  • Metadata derivation (InChI/SMILES, fingerprints) for chemical annotation
  • Use Case: identify unknown metabolites by library matching in LC-MS/MS datasets

Quick Start

Install matchms and run a simple example to compute cosine similarity against a reference spectrum.

Frequently Asked Questions about matchms

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify unknown metabolites using mass spectrometry library matching?

Spectral similarity scoring works by comparing experimental mass spectra against reference libraries using metrics like CosineGreedy, ModifiedCosine, and Fingerprint-based comparisons. MatchMS processes these spectra to derive robust similarity scores for reliable compound identification.

Can I import mzML and MGF files for metabolomics data processing?

Yes, mzML and MGF files can be imported for metabolomics data processing. This Skill supports importing and exporting common MS data formats including mzML, MGF, MSP, and JSON, alongside spectrum processing and metadata harmonization.

What is the best way to compute cosine similarity for LC-MS/MS spectra?

Computing cosine similarity for LC-MS/MS spectra is best handled by using specific similarity metrics. This Skill supports CosineGreedy, ModifiedCosine, and NeutralLossesCosine algorithms to calculate robust spectral similarity scores for compound identification.

Do I need Python to run matchms for spectral similarity analysis?

Yes, Python is required to run matchms for spectral similarity analysis. You need a Python environment with the matchms library installed to build reproducible processing pipelines and execute similarity computations.

How do I derive InChI and SMILES metadata for chemical annotation?

To derive InChI and SMILES metadata for chemical annotation, use this Skill's spectrum processing and metadata harmonization features. It supports metadata derivation including InChI, SMILES, and fingerprints to annotate compounds during library matching workflows.