matchms

Process mass spectrometry data from MGF, MSP, mzML, and JSON formats.

8|Updated Jan 13, 2026
One-click install
npx skills add https://github.com/hxk622/TokenDance --skill matchms-hxk622
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: matchms
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/chemistry/matchms
Command: npx skills add https://github.com/hxk622/TokenDance --skill matchms-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matchms, rdkit-pypi, numpy, pandas, scipy, scikit-learn, pyopenms, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies and accelerates the analysis of mass spectrometry data, enabling faster compound identification and spectral comparison.

Core Features & Use Cases

  • Spectral Comparison: Compute similarity scores between mass spectra using various algorithms (e.g., Cosine, Modified Cosine).
  • Compound Identification: Match unknown spectra against spectral libraries for identification.
  • Data Processing: Import, filter, and standardize mass spectrometry data from multiple file formats.
  • Use Case: A researcher has a list of unknown mass spectra and wants to identify the compounds by comparing them against a known spectral library. This Skill can automate that matching process and provide a ranked list of potential identifications.

Quick Start

Use the matchms skill to compare the spectrum in 'unknown.mgf' against the library in 'library.msp'.

Frequently Asked Questions about matchms

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

FAQPage Schema
How do I identify unknown compounds from mass spectrometry data?

You can compare mass spectra by computing similarity scores using algorithms like CosineGreedy, ModifiedCosine, and FingerprintSimilarity. This calculates spectral similarity to help match and identify unknown compounds against reference libraries.

What mass spectrometry file formats are supported for importing spectral data?

Supported mass spectrometry file formats for importing spectral data include MGF, MSP, mzML, and JSON. The Skill processes these formats by applying filters to standardize the data before calculating similarity scores and generating identification reports.

How do I calculate spectral similarity scores between two mass spectra?

You can calculate spectral similarity scores using algorithms such as CosineGreedy, ModifiedCosine, and FingerprintSimilarity. These methods compute similarity between mass spectra to facilitate compound matching and identification.

Can I build reproducible pipelines for mass spectrometry data processing?

Yes, you can build reproducible processing pipelines for mass spectrometry data. The Skill supports importing data from multiple file formats, applying filters for standardization, calculating similarity scores, and generating detailed identification reports for your metabolomics analysis.

Does this mass spectrometry tool support metabolomics data standardization?

Yes, this tool supports metabolomics data standardization by applying filters to imported mass spectrometry data. It processes data from MGF, MSP, mzML, and JSON formats to ensure consistent standardization before spectral matching and compound identification.