Analyze Mass Spectrometry Data with PyOpenMS
Mass spectrometry workflows require careful handling of complex formats, algorithms, and parameters. This skill guides PyOpenMS analysis for proteomics, metabolomics, signal processing, identification, and quantitation.
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Review the Skillstore skill "pyopenms" from https://skillstore.io/skills/davila7-pyopenms.md and its manifest at https://skillstore.io/api/skills/davila7-pyopenms/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
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Using "pyopenms". Inspect an mzML file and summarize its structure.
Expected outcome:
Using "pyopenms". Recommend feature detection settings for centroided metabolomics data.
Expected outcome:
Using "pyopenms". Describe a peptide identification quality-control workflow.
Expected outcome:
All 55 static findings are false positives caused by Markdown syntax, scientific identifiers, parameter names, and documentation links. The skill contains instructional PyOpenMS examples without credential access, prompt injection, covert network activity, or executable scripts.
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https://skillstore.io/skills/davila7-pyopenms/audits/9?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/davila7-pyopenms?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/davila7-pyopenms?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/davila7-pyopenms/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/davila7-pyopenms.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>davila7. (2026). pyopenms security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-pyopenms/audits/9@techreport{davila7-davila7-pyopenms-2026,
author = {davila7},
title = {pyopenms security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/davila7-pyopenms/audits/9},
note = {Author version unspecified}
}cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "pyopenms security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-pyopenms/audits/9"
identifiers:
- type: other
value: "skillstore:davila7-pyopenms:audit:9"
description: "Skillstore immutable audit report identifier"
Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-pyopenms
2026-08-21
k-dense-ai-pyopenms
2026-08-21
Load identification files, filter peptide-spectrum matches by FDR, infer proteins, and prepare structured results.
Detect, align, normalize, and annotate features across sample groups while tracking blanks and quality-control samples.
Create reproducible Python workflows for file conversion, signal processing, feature linking, and pandas export.
Load and inspect the mzML file at [path] with PyOpenMS. Explain each step, then summarize spectra, chromatograms, MS levels, retention times, and peak counts.
Design a preprocessing workflow for [data type] using [smoothing method], centroiding, normalization, and filtering. Preserve the original experiment and explain parameter choices.
Build a peptide identification workflow for [mzML path] and [FASTA path]. Include search parameters, FDR filtering at [threshold], protein inference, and idXML output.
Plan a multi-sample metabolomics workflow for [input files]. Include feature detection, alignment, linking, blank filtering, normalization, missing-value handling, annotation, and table export.
Author
davila7License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
c43861a65bb95efcae259cd161c9d6f4dc7eec6f
Maintenance freshness
7/24/2026
Usage
4 downloads · 427 views
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