What file formats does the Matchms skill support?

The Matchms skill supports common mass spectrometry formats including mzML, mzXML, MGF, MSP, GNPS-compatible JSON, and USI references.

Can this skill help with chemical structure identification?

Yes, it provides filters to derive InChI, SMILES, and molecular fingerprints, and can repair structural metadata mismatches during processing.

Which similarity metrics are available for spectrum comparison?

It includes CosineGreedy, CosineHungarian, ModifiedCosine (accounting for precursor mass shifts), NeutralLossesCosine, and FingerprintSimilarity.

Is it possible to automate multiple processing steps?

Yes, you can define a SpectrumProcessor pipeline that sequentially applies filters, normalization, and quality checks to entire spectral libraries.

Matchms MS Data Processing

Name: Matchms MS Data Processing
Author: lifangda

bylifangda

•

Data Science & ML

Processes and analyzes mass spectrometry data using Python-based spectral similarity and metadata harmonization.

Matchms is a specialized Claude Code skill designed for researchers and bioinformaticians working with mass spectrometry data in metabolomics and proteomics. It provides a comprehensive toolkit for importing various spectral formats like mzML and MGF, standardizing metadata, and applying advanced peak filters. The skill excels at calculating spectral similarities using metrics such as CosineGreedy and ModifiedCosine, enabling users to match unknown queries against reference libraries. By facilitating the creation of reproducible processing pipelines and chemical annotations, Matchms streamlines the transition from raw spectral data to biological insight.

Key Features

01Multi-format spectral data I/O supporting mzML, MGF, MSP, and JSON

02Diverse spectral similarity metrics including Modified Cosine and Neutral Losses

03Automated processing pipeline construction for reproducible workflows

04Advanced metadata harmonization and chemical structure annotation

05Comprehensive peak filtering, intensity normalization, and quality control

0616 GitHub stars

Use Cases

01Building automated pipelines for large-scale spectral clustering and networking

02Identifying unknown compounds by matching query spectra against reference libraries

03Cleaning and standardizing raw metabolomics datasets for downstream analysis

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lifangda/claude-plugins matchms

For use in Claude.ai and ChatGPT

Download Skill