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 agentlas-ai/agentlas-desktop --skill pyopenmsgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/pyopenms)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/pyopenms"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/pyopenms"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/pyopenms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.5 | $0.00030 | $0.00923 |
| Sonnet 5.5 | $0.00015 | $0.00462 |
| Haiku 4.5 | $0.00007 | $0.00231 |
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 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.
This is a copy
100% identical to pyopenms — 88 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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. |
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.
- references/data_structures.md 12 KB
- references/feature_detection.md 14 KB
- references/file_io.md 8.6 KB
- references/identification.md 12 KB
- references/metabolomics.md 16 KB
- references/signal_processing.md 10 KB
- scripts/accurate_mass_search.py 4.4 KB runs code
- scripts/align_link_quantify.py 5.2 KB runs code
- scripts/consensus_to_matrix.py 2.7 KB runs code
- scripts/convert_format.py 3.2 KB runs code
- scripts/detect_adducts.py 3.6 KB runs code
- scripts/detect_features_centroided.py 3.0 KB runs code
- scripts/detect_features_metabo.py 4.1 KB runs code
- scripts/digest_protein.py 3.5 KB runs code
- scripts/export_gnps_sirius.py 3.3 KB runs code
- scripts/extract_chromatograms.py 3.7 KB runs code
- scripts/inspect_ms_data.py 5.8 KB runs code
- scripts/mass_calculator.py 3.4 KB runs code
- scripts/plot_ms_data.py 3.9 KB runs code
- scripts/process_identifications.py 4.4 KB runs code
- scripts/process_spectra.py 4.7 KB runs code
- scripts/theoretical_spectrum.py 2.9 KB runs code
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 First seen · 197 lines · 74 tokens per session scan A 5a95655547c4
pyopenms is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 74 tokens to every session and 2,308 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to pyopenms, differing in 88 lines, and is treated as a copy.
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