Agent skill · research science · aiskillstore
pyopenms
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.
Why this skill is useful
Provides domain-specific commands and workflows for mass spectrometry data analysis that the AI wouldn't reliably generate on its own.
What it needs
Requires uv installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
PyOpenMS Overview PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides/proteins, and performing quantitative analysis. Installation Install using uv: Verify installation: Core Capabilities PyOpenMS organizes functionality into these domains: 1. File I/O and Data Formats Handle mass spectrometry file formats and convert between representations. Supported formats: mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, consensusXML, idXML Basic file reading: For detailed file handling: See references/fileio.md 2. Signal Processing Process raw spectral data with smoothing, filtering, centroiding, and normalization. Basic spectrum processing: For algorithm details: See references/signalprocessing.md 3. Feature Detection Detect and link features across spectra and samples for quantitative analysis. For complete workflows: See references/featuredetection.md 4. Peptide and Protein Identification Integrate with search engines and process identification results. Supported engines: Comet, Mascot, MSGFPlus, XTandem, OMSSA, Myrimatch Basic identification workflow: For detailed workflows: See references/identification.md 5. Metabolomics Analysis Perform untargeted metabolomics preprocessing and analysis. Typical workflow: 1. Load and process raw data 2. Detect features 3. Align retention times across samples 4. Link features to consensus map 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/pyopenms