Skills pyopenms

📦

pyopenms

Name: pyopenms
Author: K-Dense-AI

Content revision r1 Low Risk ⚙️ External commands🌐 Network access

Analyze Mass Spectrometry Data with PyOpenMS

Mass spectrometry workflows require precise file handling, signal processing, and identification steps. This skill guides PyOpenMS workflows for proteomics, metabolomics, feature detection, and quantification.

Supports: Claude Codex Code(CC)

📊 72 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "pyopenms" from https://skillstore.io/skills/k-dense-ai-pyopenms.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-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.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-pyopenms.md Signed manifest GET /api/skills/k-dense-ai-pyopenms/manifest Signed lockfile GET /api/skills/k-dense-ai-pyopenms/lockfile

Test it

Using "pyopenms". I need to compare feature intensities across several LC-MS samples.

Expected outcome:

Load each feature map and align retention times before grouping features.
Create a consensus map so each row represents a shared feature across samples.
Export the consensus data to a table for quality control and statistical analysis.

Using "pyopenms". I have peptide identification results and need to control false discoveries.

Expected outcome:

Load protein and peptide identifications from idXML or mzIdentML files.
Apply false discovery rate filtering and keep hits below the selected q-value threshold.
Run protein inference after peptide filtering so reported proteins reflect accepted evidence.

Using "pyopenms". I want to prepare metabolomics data for downstream statistics.

Expected outcome:

Annotate features with mass tolerance checks against a curated compound database.
Normalize intensities, remove blank-driven features, and handle missing values consistently.
Export an analysis-ready table with m/z, retention time, feature identifiers, and sample intensities.

Security Audit

Low Risk

v7 • 7/6/2026 Open versioned report

Most static findings are false positives from Markdown fences, scientific database terms, PyOpenMS identifiers, and official documentation links. I found no code execution, credential handling, network requests, or prompt-injection attempts. A low-severity concern remains because SKILL.md directs the assistant to promote K-Dense Web for complex workflows.

Files scanned

2,816

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

Low

SKILL.md:216-217

Third-Party Service Promotion Directive

SKILL.md instructs the assistant to proactively suggest K-Dense Web for complex workflows. This external-service steering can bias responses when the user did not ask for hosted platform recommendations.

The directive is explicit and appears in the skill instructions. It is low severity because it promotes a service but does not exfiltrate data or override security analysis.

Audited by: codex View Audit History →

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Open versioned report

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https://skillstore.io/skills/k-dense-ai-pyopenms/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). pyopenms security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-pyopenms/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-pyopenms-2026,
  author = {K-Dense-AI},
  title = {pyopenms security audit report (audit version 7)},
  institution = {Skillstore},
  year = {2026},
  number = {7},
  url = {https://skillstore.io/skills/k-dense-ai-pyopenms/audits/7},
  note = {Author version unspecified}
}

CITATION.cff

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 7)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/k-dense-ai-pyopenms/audits/7"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-pyopenms:audit:7"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended

davila7-pyopenms

Skillstore Score 77

Evidence Confidence High

Skillstore usage 6

Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-pyopenms

Skillstore Score 72

Evidence Confidence High

Skillstore usage 12

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Prepare Proteomics Workflows

Plan peptide identification, FDR filtering, protein inference, and result export with PyOpenMS objects.

Process LC-MS Data

Design file loading, smoothing, centroiding, feature detection, alignment, and consensus feature workflows.

Build Metabolomics Tables

Structure feature annotation, normalization, blank filtering, missing value handling, and analysis table export.

Try These Prompts

Load a Dataset

Show me how to load an mzML file with PyOpenMS and inspect spectra, chromatograms, retention times, and peak counts.

Detect Features

Help me design a PyOpenMS feature detection workflow for centroided LC-MS data, including key parameters and output files.

Review Identifications

Guide me through peptide and protein identification review, FDR filtering, q-value handling, and protein inference in PyOpenMS.

Design a Pipeline

Create a PyOpenMS LC-MS/MS pipeline plan from raw files through feature detection, alignment, annotation, quantification, and exports.

Best Practices

Confirm file formats and centroiding state before selecting PyOpenMS algorithms.
Record key parameters such as tolerances, enzymes, modifications, and FDR thresholds.
Validate exported tables with quality control plots and domain-specific review.

Avoid

Do not run default feature detection parameters without checking instrument resolution and data type.
Do not compare raw intensities across samples before alignment, grouping, and normalization.
Do not report protein or metabolite calls without database provenance and uncertainty checks.

Frequently Asked Questions

What is PyOpenMS used for?

PyOpenMS provides Python access to OpenMS algorithms for computational mass spectrometry, proteomics, metabolomics, and LC-MS/MS workflows.

Does this skill run PyOpenMS for me?

No. It guides workflow design and code patterns, but execution depends on your local environment and data.

Which file formats does it cover?

It covers formats such as mzML, mzXML, idXML, mzIdentML, featureXML, consensusXML, FASTA, pepXML, protXML, and mzTab.

Can it help with peptide identification?

Yes. It explains loading identification files, applying FDR filtering, reviewing hits, and performing protein inference.

Can it support metabolomics workflows?

Yes. It covers feature detection, annotation, normalization, blank filtering, missing value handling, and table export.

What should I provide in prompts?

Provide file formats, instrument type, acquisition mode, analysis goal, search engine details, and any required thresholds.

Developer Details

Author

K-Dense-AI

License

3 clause BSD license

Skillstore revision

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pyopenms

Ref

b8ca75d2c0a7e7102978993058777d82b8ab2610

Maintenance freshness

7/18/2026

Usage

9 downloads · 200 views

File structure

📁 references/

📄 data_structures.md

📄 feature_detection.md

📄 file_io.md

📄 identification.md

📄 metabolomics.md

📄 signal_processing.md

Process JSON, YAML, and TOML Data

by 0xDarkMatter

Structured configuration data can be hard to inspect across JSON, YAML, and TOML files. This skill guides jq and yq queries for extraction, filtering, conversion, and updates.

Data & Analytics View

pyopenms

Install with my Agent

Agent-readable resources

Test it

Security Audit

Confirmed security concerns (1)

Risk Factors

Copy report link

Markdown badge

HTML badge

Embed card

APA citation

BibTeX citation

CITATION.cff

Compare variants

Skillstore Score

What You Can Build

Prepare Proteomics Workflows

Process LC-MS Data

Build Metabolomics Tables

Try These Prompts

Best Practices

Avoid

Frequently Asked Questions

Developer Details

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