Agent skill · Data & Analytics

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.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill scientific-pkg-pyopenms-jackspace-claudeskillz-2ad5a6a3 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 5 KB
Bundled scripts: none
Path: skills/analysis/scientific-pkg-pyopenms-jackspace-claudeskillz-2ad5a6a3/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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: ```bash uv pip install pyopenms ``` Verify installation: ```python import pyopenms print(pyopenms.__version__) ``` ## 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: ```python import pyopenms as ms # Read mzML file exp = ms.MSExperiment() ms.MzMLFile().load("data.mzML", exp) # Access spectra for spectrum in exp: mz, intensity = spectrum.get_peaks() print(f"Spectrum: {len(mz)} peaks") ``` **For detailed file handling**: See `references/file_io.md` ### 2. Signal Processing Process raw spectral data with smoothing, filtering, centroiding, and normalization. Ba

What's inside
Steps it walks through
  1. Overview
  2. Installation
  3. Core Capabilities
  4. 1. File I/O and Data Formats
  5. 2. Signal Processing
  6. 3. Feature Detection
  7. 4. Peptide and Protein Identification
  8. 5. Metabolomics Analysis
  9. Data Structures
  10. Common Workflows
  11. Quick Start: Load and Explore Data
  12. Parameter Management
  13. Export to Pandas
  14. Integration with Other Tools
Ships with 1 file
  • metadata.json
Commands it runs
uv pip install pyopenms
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About this skill
What does the pyopenms skill do?

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.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill scientific-pkg-pyopenms-jackspace-claudeskillz-2ad5a6a3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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