Claude Skills by HolobiomicsLab

Use when you have raw mass spectrometry data files (mzML, NetCDF, or vendor formats) with unknown or mixed acquisition modalities, and you need to automatically determine whether the input is LC-MS, GC-MS, IMS (ion mobility spectrometry), or MS imaging (e.

Use when you have imaging mass spectrometry data from spatial metabolomics experiments and need to reduce the high-dimensional peak space to a ranked set of marker ions for downstream spatial analysis (e.g., tissue region annotation or biomarker discovery).

Use when when you have raw Agilent MassHunter (.d) or UIMF IM-MS data files from drift tube (DT) or structure for lossless ion manipulations (SLIM) instruments and need to ingest them into a preprocessing pipeline that requires standardized in-memory or intermediate representations for.

Use when you have acquired imaging mass spectrometry (IMS) data stored in imzML format (accompanied by an .ibd ion binary data file) and need to load it into a Python-based spatial metabolomics workflow.

Use when you are repeatedly querying or iterating over multidimensional MS data stored in MZA HDF5 format (retention time, drift time, m/z dimensions) and profiling shows that repeated disk I/O for the same metadata or scan ranges dominates runtime.

Use when you have candidate metabolite structures (from database lookup or enumeration) and experimental MS/MS spectra (mzML, mzXML format), and need to rank candidates by how well their predicted fragments match observed peaks.

Use when you have trained two or more graph neural network models on the same CCS dataset split (using identical hyperparameters, loss functions, and optimization settings) and need to rigorously compare their held-out test performance to determine which architecture balances prediction accuracy.

Use when you have DIA raw mass spectrometry files from multiple instrument types (timsTOF, TripleTOF, Orbitrap) and need to build a single machine learning model to predict data quality across all platforms, or when you need to compare quality characteristics of files produced by different.

Use when when you have raw or semi-processed mass spectrometry data files in mixed formats (e.g., vendor-native .raw, .d, .

Use when when visualizing 2D peak maps (x=m/z, y=retention time or ion mobility, z=intensity) using pyOpenMS-viz with any plotting backend (matplotlib, bokeh, plotly).

Use when your mass spectrometry DataFrame contains m/z, retention time (or mobility), and intensity columns, and you need to generate an interactive HTML figure for exploration, web-based presentation, or interactive supplementary material.

Use when when you have extracted ion chromatograms (XICs), ion mobilograms (IMs), or mass spectra from diaPASEF or other DIA workflows and need to visualize them interactively to inspect peak boundaries, compare MS1 vs MS2 traces, validate feature identifications, or communicate results.

Use when when you have mass spectrometry data in a Pandas DataFrame with columns for m/z, retention time (or ion mobility), and intensity, and you want to generate interactive (rather than static) visualizations for exploratory analysis, interactive drill-down, or deployment in web applications or.

Use when after running saturation repair or multidimensional smoothing on IM-MS data when you need to validate whether corrected peaks are reliable or whether overlapping coeluting/comobiling ions may have caused incorrect signal reconstruction.

Use when you have added a known internal standard compound to your nano-DESI MSI sample and want to correct for pixel-to-pixel variation in ionization efficiency or sample deposition.

Use when you have IM-MS lipidomics data from samples spiked with U13C-labeled internal standards (fully labeled yeast extract) and measured CCS values need bias assessment or correction.

Use when you have (LC-)IM-MS lipidomics data from samples spiked with U13C-labeled yeast extract, measured CCS values for both labeled and unlabeled lipids, and need to quantify systematic CCS bias between your instrument and a reference library before applying bias correction.

Use when your IM-MS lipidomics samples have been spiked with fully labeled isotopic internal standards (e.

Use when after loading raw diaPASEF or DIA mass spectrometry data (mzML format) and search results (DIA-NN, OpenSwath, or equivalent) to visually inspect extracted ion chromatograms and mobilograms for selected peptide precursors.

Use when your peak table contains suspected mispicked ions—ions with similar m/z and retention time that likely represent the same metabolite split across multiple features due to preprocessing errors.

Use when you have a feature table containing raw ion mobility arrival time measurements paired with experimentally assigned biomolecular class labels (e.g., lipid, protein, carbohydrate), and you need to compute CCS values for downstream structural or comparative analysis.

Use when you have TWIM-MS experimental data with arrival times and m/z values, and access to calibrant reference standards with known CCS values (typically loaded from a calibration template).

Use when when you have positive- or negative-mode ion mobility spectrometry data with tunemix reference standards (known m/z, drift times, and CCS values) and need to verify that the calibration model accurately captures the relationship between drift time, reference m/z, and collision cross.

Use when you have TWIM-MS experimental data with assigned biomolecular class labels (e.g., peptides, lipids, carbohydrates) and arrival time measurements, and you need to compute CCS values conditioned on class membership.

Use when you have raw IM-MS data in UIMF or Agilent MassHunter .d format acquired using multiplexed (compressed) ion mobility pulse sequences, and you need to recover conventional (non-multiplexed) IM-MS spectra with resolved mobility and mass dimensions.

Use when when processing raw mass spectrometry data files of unknown or mixed provenance, and you need to automatically route IMS inputs to their corresponding analysis pipeline.

Use when when processing raw IM-MS data (Agilent MassHunter .

Use when you have high-dimensional TWIM-MS data (arrival time and m/z dimensions) from a multi-omic sample and need to associate experimental features with biomolecular classes *before* running peak detection or feature identification pipelines.

Use when you have a set of metabolite structures (or their molecular descriptors) and need to construct training or target feature matrices for CCS prediction. Specifically, use it when you are preparing data to fit or apply a machine learning model (e.g., Sklearn v1.0.

Use when your raw MSI dataset is acquired on an ion-mobility-enabled instrument (e.g., Bruker .baf, .tsf, or .tdf formats) and your analyte of interest has a known or experimentally determined ion mobility value (1/K0 or drift time μs).

Use when you have raw LC-IMS-MS data (Agilent, Thermo, Bruker, or mzML format) and need to visualize and export the spatial distribution of a specific ion species (or ion family) across both ion mobility and retention time dimensions.

Use when you have a curated dataset of molecular structures (or molecular descriptors) paired with experimentally measured or reference collision cross section values, and you need to predict CCS values for a set of ≤10,000 novel molecules to filter or prioritize metabolomics identifications.

Use when when you have raw IM-MS data from drift tube (DT) or SLIM instruments in Agilent MassHunter (.

Use when you have IM-MS lipidomics samples spiked with U13C-labeled internal standards (e.

Use when when you have mass spectrometry data with ion mobility (drift time or 1/K₀) measurements as a continuous dimension and want to visualize intensity distributions across the ion mobility axis.

Use when when you have extracted ion mobilograms from DIA-MS experiments and need to automatically identify peak boundaries and apex positions in the ion mobility dimension.

Use when you have raw arrival-time data from TWIM-MS and need to convert it to collision cross section (CCS) values for multi-omic analysis.

Use when preprocessing raw Agilent MassHunter (.d) or UIMF IM-MS data files that exhibit signal saturation—ion intensity clipping caused by detector or amplifier limits—which distorts peak shape and abundance estimates across the m/z and drift-time axes.

Use when you have raw ion mobility-mass spectrometry data (drift time and m/z measurements) from DTIMS-MS, TWIMS-MS, or SLIM-based IMS-MS instruments and need to derive collision cross section values for molecular ion characterization. Use it specifically when calibrant standards (e.

Use when after peaks have been detected in aligned GCIMS samples using findPeaks with CWT parameters and peaks have been clustered across samples, and you need to integrate peak signals into a matrix format where each entry represents the intensity of a peak cluster in a specific sample for.

Use when you have tunemix reference data acquired in both positive and negative ion modes and need to establish independent CCS calibration curves for each mode. The skill is required when downstream CCS assignments must achieve R² ≥ 0.

Use when when evaluating whether a mass spectrometry analysis platform (such as mzmine) has comprehensive module support across multiple ionisation and separation techniques (LC, GC, IMS, MALDI MS imaging), or when planning a multi-technique MS study and needing to confirm that all intended.

Use when when you have SMILES strings representing neutral organic molecules and need to enumerate the likely protonated (e.g., [M+H]+) and deprotonated (e.

Use when you have a detected feature table (m/z, drift_time, retention_time, intensity) from LC-IMS-MS or similar multidimensional MS data and want to identify and annotate isotopic families (e.g., singly-charged C13 patterns).

Use when you have a feature table (m/z, drift_time, retention_time, intensity) from LC-IMS-MS or similar multi-dimensional MS acquisition and need to (1) link isotopic variants to their monoisotopic parent features, (2) disambiguate true chemical features from noise or instrumental artifacts, or.

Use when after DEIMoS isotope detection has assigned potential isotopic signatures to detected features in aligned MS1 data.

Use when after isotope detection has enumerated C13 isotopologue patterns across m/z, drift time, and retention time dimensions, and you need to reduce false positives by retaining only well-populated isotopic signature clusters before annotation or export.

Use when you have latent low-dimension peak features extracted by a Graph-attention autoencoder from imaging mass spectrometry (IMS) datasets, and you need to automatically identify a ranked subset of marker ions without manual inspection.

Use when when you need to understand how a Java application routes input data to processing modules based on declared data types, conditionally branches on instrument or format types (e.

Use when when building reproducible Python-based computational workflows that must serve both beginner and expert users; when the analysis requires interactive parameter tuning, file upload capability, or real-time result visualization;