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pyOpenMS Mass Spectrometry Workflows

A ready-to-run skill for processing, quantifying, and annotating proteomics and metabolomics LC-MS/MS data with pyOpenMS.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 8/10Last updated: Aug 9, 2026

What it does

This skill wraps pyOpenMS (the Python bindings for OpenMS) into an end-to-end mass spectrometry toolkit: read and convert mzML/mzXML/MGF, run signal-processing chains (Gaussian/SGolay smoothing, PeakPickerHiRes centroiding, normalization, S/N thresholds), detect features for either metabolomics (MassTraceDetection → ElutionPeakDetection → FeatureFindingMetabo) or centroided proteomics data, align retention times across samples, link consensus features, build label-free quant matrices, group adducts, annotate against HMDB by accurate mass, and FDR-filter peptide/protein identifications.

The centerpiece is a set of ~17 parameterized CLI scripts in scripts/ — the skill explicitly tells the agent to run a script before writing new code. Exports cover GNPS (FBMN), SIRIUS, mzTab, and pandas DataFrames. A dedicated section documents pyOpenMS 3.5.0 API breakages (e.g. FeatureFinder("centroided") removal, PeptideIdentificationList requirements, lowercase rt/mz columns), which prevents a lot of copy-paste failures from older tutorials.

Who it's for

  • Proteomics and metabolomics researchers and graduate students working with LC-MS/MS
  • Core-facility analysts running repeatable quantification pipelines
  • Developers porting OpenMS/TOPP workflows to Python
  • Not useful without MS domain knowledge and real spectra files.

Example uses

  1. Inspect and detect: run inspect_ms_data.py sample.mzML --spectra-csv spectra.csv to summarize spectrum counts and RT/m/z ranges, then detect_features_metabo.py sample.mzML --out-csv features.csv for untargeted metabolomics features.
  2. Multi-sample quantification study: align_link_quantify.py s1.mzML s2.mzML s3.mzML --out-prefix study, followed by consensus_to_matrix.py study.consensusXML --out quant.csv --normalize median for a median-normalized intensity matrix.
  3. Peptide chemistry and ID post-processing: mass_calculator.py --peptide "PEPTIDEM(Oxidation)K" --charges 1 2 3 --isotopes 5 for charged m/z and isotope patterns, and process_identifications.py search.idXML --fasta db.fasta --fdr 0.01 to export 1% FDR hits.

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/pyopenms folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/pyopenms/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills && mkdir -p ~/.claude/skills && cp -r /tmp/scientific-agent-skills/skills/pyopenms ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and confirm you have Python 3.9+ and uv: python3 --version and uv --version.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Copy the skill into your Claude Code skills directory: mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/pyopenms ~/.claude/skills/
  4. Install the library: uv pip install pyopenms, then verify with python -c "import pyopenms as ms; print(ms.__version__)" (the one-line memory notice on import is harmless).
  5. Restart Claude Code, open the folder containing your mzML files, and ask something like "use the pyopenms skill to inspect this mzML file."
  6. To see per-script options, run python ~/.claude/skills/pyopenms/scripts/<script>.py --help.
View source on GitHub ↗License: MIT