Agent skill · research science · aiskillstore
matchms
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
Why this skill is useful
Provides domain-specific knowledge for metabolomics, including spectral similarity calculations and data processing workflows that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Matchms Overview Matchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows. Core Capabilities 1. Importing and Exporting Mass Spectrometry Data Load spectra from multiple file formats and export processed data: Supported formats: mzML and mzXML (raw mass spectrometry formats) MGF (Mascot Generic Format) MSP (spectral library format) JSON (GNPS-compatible) metabolomics-USI references Pickle (Python serialization) For detailed importing/exporting documentation, consult references/importingexporting.md. 2. Spectrum Filtering and Processing Apply comprehensive filters to standardize metadata and refine peak data: Filter categories: Metadata processing: Harmonize compound names, derive chemical structures, standardize adducts, correct charges Peak filtering: Normalize intensities, select by m/z or intensity, remove precursor peaks Quality control: Require minimum peaks, validate precursor m/z, ensure metadata completeness Chemical annotation: Add fingerprints, derive InChI/SMILES, repair structural mismatches Matchms provides 40+ filters. For the complete filter reference, consult references/filtering.md. 3. Calculating Spectral Similarities Compare spectra using various similarity metrics: Available similarity functions: CosineGreedy/CosineHungarian: Peak-based cosine similarity with different matching algorithms ModifiedCosine: Cosine similarity accounting for precursor mass differences NeutralLossesCosine: Similarity based on neutral loss patterns FingerprintSimilarity: Molecular structure similarity using fingerprints MetadataMatch: Compare user-defined metadata fields PrecursorMzMatch/ParentMassMatch: Simple mass-based filtering For detailed similarity function documentation, consult references/similarity.md. 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/matchms--2b2afb