pyopenms
by K-Dense-AIpyopenms is a Python-based mass spectrometry skill for proteomics and metabolomics workflows. Use it to install pyopenms, load and inspect mzML and related files, process spectra, detect features, identify peptides and proteins, and build reproducible LC-MS/MS data analysis pipelines.
This skill scores 78/100, which means it is a solid listing candidate for directory users: it has enough real workflow content to justify installation, but users should still expect some gaps in adoption guidance and supporting documentation. The repository gives a clear proteomics/mass spectrometry use case, concrete installation and verification steps, and example code for file I/O and core analysis workflows, so it should be easier for an agent to trigger and use than a generic prompt.
- Clear scope for proteomics and LC-MS/MS workflows, with explicit use cases like feature detection, peptide identification, and quantification.
- Operationally useful installation and verification instructions are included (`uv pip install pyopenms`, then import/version check).
- Substantive workflow examples and code fences show how to load mzML data and work with OpenMS objects, reducing guesswork for agents.
- No install command or supporting reference files are bundled in the repo, so agents may need to rely on the SKILL.md content alone.
- Documentation appears self-contained but not deeply scaffolded for edge cases; users may need extra prompt guidance for complex pipelines or uncommon formats.
Overview of pyopenms skill
What pyopenms does
The pyopenms skill is for Python-based mass spectrometry work with OpenMS bindings. It helps you load, inspect, process, and analyze proteomics and metabolomics data in code, especially when you need more than a one-off prompt and want a reproducible pipeline.
Who should use it
Use pyopenms if you work on LC-MS/MS data, peptide identification, feature detection, quantification, or format conversion across mzML, mzXML, mzIdentML, featureXML, and related files. It is a better fit for data analysis workflows than for casual spectrum comparison or simple metabolite lookup.
Why this skill is different
The pyopenms skill is most useful when you need OpenMS algorithms exposed in Python and want to move from raw files to structured analysis. The main value is workflow control: you can script ingestion, transformation, and downstream analysis instead of relying on generic AI advice that ignores file formats, object types, and pipeline order.
How to Use pyopenms skill
Install pyopenms
For a local skills setup, install with the directory’s standard command, then confirm the package is available in your Python environment. The repo’s guidance is lightweight, so check your runtime first: uv pip install pyopenms is the expected install path, and import pyopenms; print(pyopenms.__version__) is the fastest verification step.
Start from the right files
Read SKILL.md first, then follow the sections that match your task: installation, core capabilities, and the file I/O examples. If your use case depends on a specific data type or pipeline step, inspect the example code before writing your own prompt so you can mirror the library’s object model and naming.
Turn a rough goal into a usable prompt
For strong pyopenms usage, give the model your input format, desired output, and pipeline stage. Better: “Load an mzML file, detect features, and export a table with feature m/z, RT, and intensity.” Worse: “Analyze this mass spec data.” Include file paths, whether you need peptide or protein-level results, and any conversion or filtering constraints.
Workflow tips that improve output
Ask for one stage at a time when the pipeline is unfamiliar: import and inspect first, then preprocessing, then identification or quantification. Mention if the task is proteomics or metabolomics, because that changes the algorithm choices and expected objects. If you already know the target format, say so up front; it reduces avoidable detours and helps the pyopenms guide stay aligned with your analysis goal.
pyopenms skill FAQ
Is pyopenms good for beginner users?
Yes, if you already have basic Python comfort and know your input files. It is not a beginner-friendly biology tutorial, but it is usable for first-time OpenMS scripting when you start with a narrow task and verify each step.
When should I not use pyopenms?
Do not use pyopenms for simple spectral matching, lightweight metabolite annotation, or tasks that do not need the OpenMS ecosystem. If your goal is only quick comparison or lookup, a smaller tool or a different skill will be easier.
How is this different from a normal prompt?
A normal prompt may describe the analysis conceptually but miss the binding-specific details that matter in practice. The pyopenms skill is better when you need library-aware code, file handling, and analysis order that reflect how OpenMS actually works.
What should I expect from pyopenms for Data Analysis?
Expect a strong fit for reproducible MS data analysis, especially when the work involves standard file formats and established proteomics workflows. It is strongest when you can describe the dataset, the analysis stage, and the desired export or reporting format.
How to Improve pyopenms skill
Give the library the right starting point
The most useful inputs for pyopenms include file type, sample count, analysis goal, and output shape. If you want code, say whether you need a notebook example, a script, or a reusable function. If you want a pipeline, name the steps in order.
Reduce ambiguity in the analysis request
Common failures come from mixing proteomics and metabolomics language, omitting the input format, or asking for a full workflow without defining the target result. The pyopenms skill works better when you specify whether you need feature detection, identification, quantification, or conversion, not all four at once.
Iterate after the first draft
After the first answer, improve the result by adding a real file example, a small expected output sample, and any constraints such as memory limits or preferred formats. For pyopenms for Data Analysis, the fastest path to better output is to ask for code that reads your exact file type and prints or exports the exact fields you plan to inspect.