A Python interface to OpenMS for analyzing mass-spectrometry data used in proteomics and metabolomics. It supports spectra and feature files, peptide and protein identification, quantification, and LC-MS/MS workflows.

Use it to inspect and convert MS data, detect features, identify peptides and proteins, quantify samples, annotate compounds, and export common scientific result formats.

It brings established mass-spectrometry processing steps into Python, reducing the need to connect separate tools for file handling, detection, identification, and measurement.

pyopenms — Skill for Claude Code by K-Dense-AI

About the project

Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.

K-Dense-AI/scientific-agent-skills · 44,220 stars · on GitHub · arxiv.org

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add K-Dense-AI/scientific-agent-skills --skill pyopenms

Clone the repo

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pyopenms/github.svg)](https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pyopenms)

Your own site

<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pyopenms"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pyopenms/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pyopenms"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pyopenms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,308 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

Socket pass 4 May 2026
Snyk pass 4 May 2026
NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00074	$0.02308
Opus 5	$0.00037	$0.01154
Sonnet 5	$0.00015	$0.00462
Haiku 4.5	$0.00007	$0.00231

Measured 7d ago against content hash 5a95655547c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

pyopenms scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

The scan reads SKILL.md. This mod also ships 16 executable files (scripts/accurate_mass_search.py, scripts/align_link_quantify.py, scripts/consensus_to_matrix.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

pyopenms — 88% identical, 21 lines differ

skills/pyopenms/SKILL.md · 197 lines

How it starts

The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PyOpenMS

Overview

PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use it to read/write MS file formats, process raw spectra, detect and quantify features, identify peptides and proteins, and run end-to-end LC-MS/MS pipelines.

This skill ships ready-to-run scripts in scripts/ covering the most common high-level workflows. Prefer running a script over writing new code—each is a parameterized CLI tool that handles loading, processing, and export. Drop into the Python API (and the references/) only when no script fits.

Installation

uv pip install pyopenms

Verify (note: __version__ works, but the bundled binary prints a one-line memory-status notice on import that is harmless):

import pyopenms as ms
print(ms.__version__)  # 3.5.0

Scripts (start here)

Run with python scripts/<name>.py --help for full options. All accept standard MS file formats and write featureXML/consensusXML/CSV/mzTab/PNG as appropriate.

Inspect & convert

Script	What it does
`inspect_ms_data.py`	Summarize any mzML/mzXML/featureXML/consensusXML/idXML (counts, RT/m/z ranges, TIC, metadata); optional per-spectrum CSV.
`convert_format.py`	Convert between mzML/mzXML/MGF with optional MS-level, RT, and intensity filtering.
`process_spectra.py`	Configurable signal-processing chain: smoothing (Gauss/SGolay), centroiding (PeakPickerHiRes), normalization, S/N and intensity thresholds.

Feature detection & quantification

Script	What it does
`detect_features_metabo.py`	Untargeted metabolomics feature finding: MassTraceDetection → ElutionPeakDetection → FeatureFindingMetabo.
`detect_features_centroided.py`	Peptide/centroided feature detection via FeatureFinderAlgorithmPicked.
`align_link_quantify.py`	Multi-sample pipeline: detect (or load) features → RT alignment → consensus linking → quant matrix CSV.
`consensus_to_matrix.py`	consensusXML → wide intensity matrix + metadata, with optional median/quantile normalization and long format.

Read the full file on GitHub · 197 lines

Files

What ships with it

22 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago First seen · 197 lines · 74 tokens per session scan A 5a95655547c4

Subscribe to this mod's changes

pyopenms is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 3d ago), licensed MIT. It adds 74 tokens to every session and 2,308 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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