OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.
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 agentmods add skills/pharmolix/openbiomed/single-cell-proteomics-data-processingnpx skills add PharMolix/OpenBioMed --skill single-cell-proteomics-data-processinggit clone --depth 1 https://github.com/PharMolix/OpenBioMedWrote 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/pharmolix/openbiomed/single-cell-proteomics-data-processing)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-proteomics-data-processing"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-proteomics-data-processing.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.03034 |
| Opus 5 | $0.00000 | $0.01517 |
| Sonnet 5 | $0.00000 | $0.00607 |
| Haiku 4.5 | $0.00000 | $0.00303 |
Grade A, and why
single-cell-proteomics-data-processing 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 6d 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Raw Mass Spectrometry Data Processing (pyOpenMS)
Load, inspect, centroid, and extract features from raw LC-MS/MS data files. This is Step 1 of the proteomics pipeline — all downstream peptide identification and quantification steps require centroided, quality-checked spectra as input.
What it does
- Loads raw or profile-mode spectra from mzML, mzXML, or vendor-converted files using pyOpenMS
- Inspects run-level QC metrics: total ion current (TIC), scan counts per MS level, m/z and RT ranges
- Converts profile-mode spectra to centroid mode using PeakPickerHiRes
- Extracts MS1 and MS2 spectra separately for downstream use
- Detects LC-MS features (isotope envelopes) using FeatureFinder for label-free quantification
- Extracts extracted ion chromatograms (EIC) for targeted m/z values
- Converts between mzML, mzXML, and featureXML formats
- Generates per-run QC plots (TIC, scan distribution, peak width)
Why this exists
If you ask a general AI to "process my mzML files for proteomics," it will:
- Not distinguish between profile-mode and centroid-mode spectra (critical difference for downstream tools)
- Use incorrect pyOpenMS API calls (the API changed significantly between versions 2.x and 3.x)
- Skip quality control checks that reveal injection failures, column issues, or contamination
- Not explain the difference between peak picking and feature detection, or when each is needed
- Produce centroided output without verifying peak width or mass accuracy
This skill encodes the correct methodological decisions:
- Checks MS level distribution before processing to confirm DDA vs. DIA acquisition mode
- Applies PeakPickerHiRes (correct algorithm) not PeakPickerIterative (for Orbitrap data)
- Separates MS1 feature detection (for LFQ) from MS2 centroiding (for database search)
- Generates TIC plots to visually confirm run quality before investing compute time in search
- Uses pyOpenMS 3.x API (
MSExperiment,MzMLFile) which differs from 2.x
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.
- 6d ago First seen · 282 lines · 0 tokens per session scan A d17b2c963477
single-cell-proteomics-data-processing is a skill published in the GitHub repository PharMolix/OpenBioMed (1,107 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,034 tokens. 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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