Claude Skills by HolobiomicsLab

Use when when you have raw mzML or mzXML files containing uncompressed m/z and intensity arrays and need to load spectra into a uniform data contract before compression, cross-format comparison, or algorithmic processing.

Use when you have a trained conformer-based peak-picking model (in ONNX format) and need to apply it to chromatographic or ion-mobility mass spectrometry data (TransitionGroup objects) to detect peak boundaries and apex positions, generating TransitionGroupFeature records for downstream analysis or.

Use when when you have an aligned GCIMS dataset and need to configure the findPeaks function with CWT algorithm to detect peaks across retention time and drift time dimensions.

Use when working with raw IM-MS data (Agilent MassHunter .d or UIMF format) that contains low-abundance background noise, isolated high-intensity artifacts, or jagged peaks characteristic of low-abundance ions.

Use when when working with Bruker .d/.baf mass spectrometry imaging data and needing to feed it into MSIGen or other open-source MSI processing pipelines.

Use when when you have mass spectrometry data in a Pandas DataFrame with column names that do not match pyOpenMS-viz's default expectations (e.g., 'm/z' vs 'mz' or 'retention_time' vs 'rt'), or when your data uses domain-specific column labels (e.g., 'mass_to_charge', 'scan_time', 'peak_intensity').

Use when you have raw MS data in instrument-native or mzML format (Agilent .d, Thermo .raw, Bruker .d) and need to isolate specific analyte regions defined by precise m/z windows, RT windows (in seconds or minutes), and/or ion mobility arrival-time windows.

Use when after extracting ion chromatograms and ion mobilograms from raw diaPASEF or DIA data within specified m/z, retention time, and ion mobility windows.

Use when you have raw IM-MS data in Agilent MassHunter (.d) or UIMF format from drift tube (DT) or SLIM instruments, and you intend to perform HRdm demultiplexing or peak deconvolution to resolve co-eluting or structurally similar ions.

Use when when you have a pre-aligned GCIMSDataset and need to systematically identify and annotate chromatographic peaks across multiple samples.

Use when after peak detection and clustering have been completed on aligned and baseline-corrected GC-IMS data, and before imputation or statistical analysis.

Use when when you have loaded mass spectrometry data (from mzML or Bruker .

Use when you have mass spectrometry data loaded into a Pandas DataFrame with columns for m/z, retention time (or ion mobility), and intensity, and you need to visualize the complete 2D peak map landscape to identify co-eluting features, assess data quality, or explore retention time and.

Use when when you have a peptide sequence and need to predict its fragmentation pattern under specific isotopic labeling conditions (e.g., natural 13C abundance at 1%, or enriched 13C at arbitrary levels).

Use when you have IM-MS lipidomic data from samples spiked with U13C-labeled internal standards (e.g., fully labeled yeast extract), measured CCS values stratified by lipid class and adduct type, and access to the DTCCS_N2 reference library for U13C lipids.

Use when you have one or more peptide or protein sequences in string format and need to calculate their mass, isotopic envelope (m/z and intensity pairs), charge state behavior, isoelectric point, or predicted chromatographic retention.

Use when you have raw line-scan mass spectrometry imaging data from nano-DESI or other line-scan acquisition modes and need to produce a georeferenced 3D pixel array.

Use when after ASE-ANI has filtered conformers to remove high-energy geometries, and you need to compute electronic properties required for CCS prediction.

Use when after fitting a polynomial calibration model to tunemix reference data in DEIMoS, assess whether the model explains sufficient variance in the m/z–drift-time–CCS relationship.

Use when after training a GNN model on molecular structures with continuous targets (e.g., CCS values), when you need to understand which node-level (atom) or edge-level (bond) features contribute most to individual or aggregate predictions.

Use when you have raw GC–MS or LC–MS data in two-dimensional m/z vs retention time format and need to identify marker features at parts-per-billion sensitivity without relying on conventional peak picking.

Use when when processing imzML/ibd Imaging Mass Spectrometry datasets and you need to extract ion density maps for specific analytes or isotopes. Apply this skill after importing the .imzML metadata and .

Use when you have raw DIA mass spectrometry data files (.raw, .d, or .wiff formats) from timsTOF, TripleTOF, or Orbitrap instruments and need to extract precursor ion chromatograms for quality assessment or downstream analysis.

Use when you have raw TOF-MS or IM-MS data in Agilent MassHunter (.d) or UIMF format with jagged peaks and low-abundance ions that require signal enhancement, but you need to decide whether to apply smoothing, and at what strength, to avoid over-smoothing real signals or under-removing artifacts.

Use when you have a Thermo Fisher Scientific .raw file containing PRM data and need to verify that acquisition of a specific precursor ion (e.g., LGGNEQVTR++ at m/z 487.2567) is happening at regular intervals consistent with your instrument method design.

Use when you have 3D optimized molecular conformers (RDKit mol objects or SDF files) and need to extract shape-based features for collision cross section prediction, graph neural network input, or conformer comparison.

Use when you have received raw mass spectrometry data in one of four proprietary vendor formats (Agilent '.d', Bruker ion mobility 'd', Thermo '.raw', or mzML) and need to convert it to a cross-platform, cross-language accessible format.

Use when you have vendor raw files (e.g., .raw from Thermo, .d from Agilent, .wiff2 from Sciex) that need to be converted to a standard format for analysis pipelines, data sharing, or when you require the high compression rates and fast decoding provided by Aird format.

Use when you have SMILES-encoded molecular structures and need to model their behavior under electrospray ionization (ESI) or other ionization methods in mass spectrometry.

Use when you need to enable optional modules in Pyteomics that depend on external libraries not bundled with the core package—such as h5py and hdf5plugin for mzMLb format access, sqlalchemy for Unimod database queries, or psims for ProForma parsing.

Use when when you have molecular structure inputs (SMILES strings, molecular graphs, or feature vectors) and need to predict a continuous molecular property (e.g., CCS values, retention time, ionization efficiency).

Use when you have completed parallel QUICK quantum calculations on multiple conformers filtered by ASE-ANI and need to extract electronic properties from the output logs and consolidate them into a single structured table for use in CCS calculations or metabolite annotation workflows.

Use when you have measured IM-MS lipidomics data spiked with U13C labeled internal standards and need to assess whether CCS bias remains within acceptable limits for each lipid class-adduct combination.

Use when you have raw DIA mass spectrometry files (.raw, .d, or .wiff format) from timsTOF, TripleTOF, or Orbitrap instruments and need to assess their quality before downstream proteomics analysis.

Use when you have raw MS data in a supported instrument format (Agilent .d, Thermo .raw, Bruker .d, mzML) and a defined list of m/z, retention time, or other identifiers for which you need to extract and quantify peak abundances across one or more samples.

Use when when you have SMILES strings or molecular formulae for N-Me derivatized unsaturated sterol lipids and need to generate theoretical MS/MS spectra (predicted fragment m/z values and intensities) to compare against experimental LC-IM-MS/MS data before performing CCS prediction or downstream.

Use when when you have a collection of N-Me derived unsaturated sterol lipid identifiers or structures and need to generate predicted collision cross section (CCS) values for LC-IM-MS/MS analysis.

Use when you have a set of RDKit-generated conformers ranked by ASE-ANI single-point energies, and you need to submit the lowest-energy subset to quantum software (e.g. QUICK) for CCS-relevant electronic structure calculations.

Use when you have a peak table matrix with NA values that need to be imputed using cluster statistics, or when a GCIMSDataset object requires filtering by retention time (0–1100 s) and drift time (5–16 ms) ranges, or when you need to apply smoothing, decimation, or baseline correction to raw GCIMS.

Use when when performing peak detection on Gas Chromatography–Ion Mobility Spectrometry samples where the Reactant Ion Peak (a high-intensity background signal from the ion source) would otherwise be misidentified as an analyte peak, causing false positives in the peak list.

Use when you have detected feature tables from multiple LC-IMS-MS/MS samples (each with mz, drift_time, retention_time, and intensity columns) and need to match corresponding features across datasets to enable cross-sample quantitation or cohort analysis.

Use when when you have IM-MS lipidomics data with measured CCS values from samples spiked with U13C labeled internal standards, and you need to assess systematic CCS bias or enable CCS correction by comparing measured lipids against known library entries with validated CCS values.

Use when you have trained two or more regression models (e.g., original vs. alternative GNN architectures) on the same training set and need to evaluate which generalizes better on held-out test data.

Use when you have model predictions and ground-truth labels for a test set and need to assess how well the trained model generalizes to unseen data. Typical triggers: after executing inference on a held-out test partition, after cross-dataset evaluation (e.

Use when after training a deep-learning regression model (e.g., for CCS prediction from voxel projected area features), evaluate it on held-out test data to report per-molecule predictions and aggregate performance metrics.

Use when after generating collision cross section predictions on a validation or test set using a trained graph neural network model, and you need to quantify prediction accuracy and compare against reported performance metrics in the literature or prior experimental runs.

Use when you have multidimensional MS data converted to MZA HDF5 format and need to retrieve specific spectra or chromatographic slices defined by one or more of: retention time (in minutes), ion mobility arrival time (in milliseconds for DT/SLIM or Vs/cm² for TimsTOF), or m/z value (as a float or.

Use when working with GCIMS datasets where retention time spans a wide range (e.g., 0–1500 s) but your analytes of interest are confined to a narrower window (e.g., 0–1100 s).

Use when processing raw LC/MS data (mzML or mzXML format) from multi-sample cohorts where retention time or intensity drift is suspected due to batch effects, instrument calibration drift, or variable run order.

Use when when you have mass-spectrometry data in tabular form (Pandas DataFrame) with columns for m/z, retention time or ion mobility, and intensity, and you need to visualize the 2D distribution of peaks to assess peak separation, detect co-elution, or examine chromatographic and mass resolution.