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.
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-pyopenmsgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote 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.
[](https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pyopenms)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pyopenms"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-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.
<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pyopenms"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pyopenms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
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 | $0.00081 | $0.02048 |
| Opus 5.5 | $0.00032 | $0.00819 |
| Sonnet 5.5 | $0.00016 | $0.00410 |
| Haiku 4.5 | $0.00008 | $0.00205 |
Grade A, and why
alterlab-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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 258 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 for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides/proteins, and performing quantitative analysis.
Installation
Install using uv (pyOpenMS 3.x — examples here are verified against 3.5):
uv pip install "pyopenms>=3.4"
Verify installation:
import pyopenms
print(pyopenms.__version__)
Version note: pyOpenMS 3.x removed the old
FeatureFinderfacade. UseFeatureFinderAlgorithmPicked(the former"centroided"algorithm) or, for metabolomics, theMassTraceDetection→ElutionPeakDetection→FeatureFindingMetabochain. Seereferences/feature_detection.md.
When to Use This Skill
Use this skill when the user wants to:
- Read, convert, or inspect MS file formats (mzML, mzXML, idXML, featureXML, consensusXML, mzTab).
- Process raw spectra — smoothing, baseline removal, centroiding/peak picking, normalization.
- Run feature detection and label-free quantification on LC-MS runs.
- Do peptide/protein identification and FDR control (search-engine adapters, PeptideIndexer, FalseDiscoveryRate) or build a full LC-MS/MS pipeline.
- Do untargeted metabolomics feature finding (MassTraceDetection -> ElutionPeakDetection -> FeatureFindingMetabo) and adduct/formula work.
- Drive OpenMS algorithms programmatically with
Paramobjects instead of TOPP command lines.
Does NOT Trigger
| Scenario | Use Instead |
|---|---|
| Spectral similarity scoring / library matching of small-molecule MS/MS | alterlab-matchms |
| Cheminformatics on the identified molecules (descriptors, substructures, SMILES) | alterlab-rdkit |
| Statistics on an already-quantified abundance matrix (tests, PCA, clustering) | alterlab-statistical-analysis |
| Protein sequence/structure work rather than the mass spectra | alterlab-biopython or alterlab-alphafold |
| Transcript quantification from RNA-seq reads | alterlab-rnaseq-quant |
What ships with it
7 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.
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.
- 1mo ago Changed · +23 lines e4ce049cc0be
- 1mo ago First seen · 235 lines · 81 tokens per session scan A a52b0a314a82
alterlab-pyopenms is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (78 stars, last pushed yesterday), licensed MIT. It adds 81 tokens to every session and 2,048 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
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