matchms

Compare mass spectra and identify compounds using spectral libraries.

13|3|Updated Jun 10, 2026

Search Tags:#analysis #metabolomics #mass spectrometry #compound identification #spectral similarity

Ready to run

npx skills add https://github.com/tassiovale/claude-code-kit --skill matchms-tassiovale

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: matchms
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/matchms
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill matchms-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matchms, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines metabolomics analysis by enabling spectral similarity and compound identification, significantly reducing the time required for metabolite identification and spectral matching.

Core Features & Use Cases

Spectral Similarity: Compare mass spectra, compute similarity scores, and identify unknown compounds from spectral libraries.
Core Use Case: Ideal for metabolite identification, spectral matching, and library searching in metabolomics research.
Features: Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows.

Quick Start

Use the matchms skill to calculate the cosine similarity between two spectra and identify the best match from a library.

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 identify unknown compounds by comparing mass spectra against spectral libraries to compute similarity scores. This Skill calculates cosine and modified cosine scores to find the best library matches for metabolite identification.

How do I calculate spectral similarity for metabolomics analysis?▼

Spectral similarity is calculated by comparing mass spectra and computing similarity scores. The Skill supports cosine and modified cosine scoring, allowing you to filter peaks and standardize metadata for reproducible library searching.

Can I use matchms for full LC-MS/MS proteomics pipelines?▼

No, matchms is designed specifically for metabolomics tasks like spectral matching and compound identification. For full LC-MS/MS proteomics pipelines, you should use pyopenms instead.

What formats can I import for mass spectrometry spectral matching?▼

You can import spectra from various formats for mass spectrometry spectral matching. The Skill standardizes metadata and filters peaks to build reproducible analytical workflows for metabolite identification.

What is the best way to standardize metadata for mass spectrometry library searching?▼

The best way to standardize metadata for mass spectrometry library searching is to import spectra from various formats and apply peak filtering. This ensures reproducible analytical workflows when computing spectral similarities.