Claude Skills by HolobiomicsLab

Use when you have acquired tunemix or reference standard data in ion mobility spectrometry with known m/z, drift time, and CCS values, and you need to establish a drift-time-to-CCS mapping for a specific instrument, ionization mode (positive or negative), and buffer gas.

Use when you have positive-mode tune mix reference data (e.g., example_tune_pos.h5) with known CCS values spanning a wide m/z range (e.g., 118.086–1522 m/z) and need to establish a CCS calibration model to convert experimental drift times or collision cross sections for downstream analysis.

Use when when you have received or cloned a CCS reference library (such as the DTCCSN2 library for U13C labeled lipids) bundled with lipidomics software and need to verify its integrity, understand its lipid class composition, or extract metadata before using it for CCS bias calculation or.

Use when you have structural input data (SMILES or molecular geometry files) for N-Me derived unsaturated sterol lipids and need to generate a predicted CCS dataset indexed by lipid identifier and structural isomer class.

Use when you have a dataset of molecules with known or reference CCS values, and you need to construct a trainable model that learns the mapping from molecular structure (encoded as SMILES or feature vectors) to scalar CCS predictions.

Use when you have a dataset of SMILES strings with corresponding experimental CCS measurements and want to build a predictive model that can rapidly generate CCS values for new molecules without running expensive ion-mobility spectrometry experiments.

Use when you have obtained or need to prepare a DTCCS_N2 reference library for U13C-labeled lipids (typically provided as part of a lipidomics tool distribution) and need to extract, validate, and normalize its contents into a machine-readable table format before using it for CCS bias calculation.

Use when you have TWIM-MS experimental data with arrival/drift times and m/z values, and you possess calibrant reference standards with known CCS values.

Use when you have peak-picked features with m/z, drift_time, retention_time, and intensity columns, and you need to identify monoisotopic peaks and their charge-state-specific isotopologue members (e.g., singly charged C13-substituted species).

Use when when you have CANOPUS chemical class predictions for your samples and need to identify which extracts contain chemical classes absent from the literature for their species or genus.

Use when when you have a set of molecular structures (N-Me derived unsaturated sterol lipids or structurally similar organic molecules with C=C bonds) represented as SMILES or molecular geometry files, and you need to train or apply a machine-learning model to predict an instrument-dependent.

Use when when you have loaded a TransitionGroup (extracted ion chromatogram or mobilogram from DIA-MS data) and need to identify precise peak boundaries and apex positions for feature extraction.

Use when you have mass spectrometry data loaded as a Pandas DataFrame with retention time and intensity columns, and you need to visualize the overall or mass-trace-specific signal intensity distribution across the chromatographic separation.

Use when when ingesting raw or vendor-format mass spectrometry data files of unknown or mixed acquisition modality, and you need to automatically determine whether the input originated from liquid chromatography (LC), gas chromatography (GC), ion mobility spectrometry (IMS), or MS imaging (e.

Use when you have raw LC-MS data (mzML or vendor format) and need to discover and characterize all chromatographic features present, without prior knowledge of target analytes.

Use when working with large GCIMS matrices where computational speed or memory constraints are a concern, after filtering retention time (e.g., 0–1100 s) and drift time (e.g., 5–16 ms) ranges and applying Savitzky-Golay smoothing. Use it as a preprocessing step before alignment operations.

Use when you have TWIM-MS experimental data (arrival times and ion mobility parameters) paired with pre-assigned biomolecular class labels for an ion population, and you need to obtain class-conditioned CCS values without first performing feature-level identification.

Use when when you have multi-omic TWIM-MS data (raw or processed arrival-time records) and have already assigned features or detected ion features to biomolecular classes (e.

Use when you have a feature table with assigned biomolecular class labels (e.g., from preceding class assignment step) and raw ion mobility arrival time measurements from TWIM-MS data, and you need to compute class-appropriate CCS values for downstream multi-omic analysis.

Use when when you have vendor mass spectrometry raw files (e.g., .raw format) that must be converted to an open format (Aird) using a Windows .

Use when you have a curated dataset of ≤10,000 molecular structures with known collision cross section values for training, a target set of ≤10,000 molecules requiring CCS predictions, a compatible browser, and either lack local Python installation or prefer cloud-based execution to avoid.

Use when after peak clustering has been performed on aligned GCIMS samples and a peak table matrix has been constructed, but the matrix contains NA values because some samples did not yield detected peaks at certain cluster positions.

Use when when you need to detect and classify peaks in LC-MS regions of interest (ROIs) and simultaneously localize their boundaries for area integration.

Use when when you have IM-MS lipidomics data spiked with U13C labeled lipid internal standards (e.g., fully labeled yeast extract) and want to assess whether measured CCS values deviate systematically from expected values in the DTCCS_N2 reference library.

Use when you have ion mobility-mass spectrometry lipidomics data from samples spiked with U¹³C-labeled lipid internal standards (fully labeled yeast extract) and want to assess whether measured CCS values systematically deviate from a validated DT CCS N₂ reference library, indicating bias that may.

Use when you have raw or processed arrival-time data from a traveling-wave ion mobility mass spectrometry (TWIM-MS) platform and need to convert it into standardized collision cross section (CCS) values for comparative analysis across samples or datasets.

Use when when you have acquired ion mobility–mass spectrometry data (drift time and m/z dimensions) and need to convert observed drift times into calibrated CCS values.

Use when you have LC-IMS-MS/MS data with drift_time measurements and need to convert raw drift times into calibrated CCS values for structural annotation.

Use when you have a set of molecular structures in SMILES format that require CCS prediction for metabolite annotation in untargeted mass spectrometry workflows.

Use when when you have LC-IM-MS/MS data with measured collision cross section (CCS) values and m/z assignments, and you need to disambiguate sterol isomers (particularly N-Me derived unsaturated sterols) by matching against a curated database of predicted CCS values and MS/MS fragmentation patterns.

Use when you have IM-MS lipidomics data from samples spiked with U13C-labeled internal standards (fully labeled yeast extract) and you need to quantify whether measured CCS values deviate systematically from theoretical values, or when you want to correct CCS measurements before downstream lipid.

Use when after applying deimos.calibration.tunemix() to positive-mode or negative-mode tune mix data with known CCS reference compounds (m/z range typically 118–1522), verify that the resulting calibration model achieves the expected r-squared coefficient.

Use when you have a pre-trained GNN CCS prediction model and need to assess its predictive performance and cross-dataset generalizability. Use it specifically when evaluating whether models trained on one CCS database (e.

Use when you have molecular structures (SMILES or SDF format) and need to predict their collision cross sections for ion mobility mass spectrometry workflows, particularly when generating large-scale searchable CCS databases for compound identification and characterization.

Use when when you have N-Me derivatized unsaturated sterol lipid structures (as SMILES or molecular formula) and need to predict MS/MS fragmentation patterns with collision-energy-dependent m/z values and intensities for downstream CCS prediction or LC-IM-MS/MS library matching.

Use when you have a batch of raw LC-IMS-MS/MS data in mzML or mzML.

Use when you need to bootstrap a tool workflow by generating a version- or instrument-specific default configuration file (e.g., for MS-DIAL 4 vs. 5), execute an analysis on formatted input files (e.g., MS-DIAL export .txt files), or capture tool output for downstream validation.

Use when when you have feature lists (in CSV format) from two or more different MS acquisition methods (e.g., LC-MS vs. LC-IMS-MS), different processing software (e.

Use when you have two MS/MS spectra from related compounds (e.g., a reference compound and a suspected modified version) and need to quantify where and how their structures differ.

Use when you need to understand how a data-processing software system discriminates among multiple input types (LC, GC, IMS, MALDI) and selectively instantiates processing pipelines.

Use when after RDKit has generated a large set of 3D conformers for a molecule in SDF or XYZ format, and before submitting conformers to computationally expensive quantum-chemical methods (e.g., QUICK).

Use when you have a set of conformers that have already been filtered by ASE-ANI neural network potentials and need to extract quantum-mechanical electronic properties (polarizability tensor, dipole moment) required for collision cross section calculation.

Use when after RDKit has generated a large ensemble of 3D conformers for a molecule (typically hundreds to thousands), you need to reduce computational burden before quantum-chemical single-point energy evaluation.

Use when you have SMILES strings of molecules at specific ionization states (e.g., protonated or deprotonated adducts) and need to predict collision cross section values for mass spectrometry-based metabolite annotation.

Use when when you have ionized adduct structures (SMILES or MOL format) from an ionization-state determination step and need to create an ensemble of relaxed 3D geometries for each molecule prior to expensive conformation filtering (e.g., ASE-ANI or quantum methods).

Use when deploying a containerized .NET Framework application (e.g., AirdPro) that performs computationally intensive batch operations such as vendor file conversion to Aird format.

Use when when you need to understand how a multi-instrument mass spectrometry platform (such as mzmine) selectively routes data to different processing pipelines based on declared input type (LC, GC, IMS, or MS Imaging).

Use when you have mass spectrometry data (LC–MS/MS, ion mobility, DIA) converted to MZA format and need to read or analyze it in multiple programming languages (Python and R), or share datasets with collaborators using different environments without requiring proprietary vendor libraries or format.

Use when you have detected feature tables from two or more LC-IMS-MS/MS samples and need to establish correspondence between features across samples to enable quantitative comparison, statistical analysis, or consensus feature calling.

Use when you have collected feature lists in CSV format from two or more MS acquisition methods (e.g., LC-MS vs. LC-IMS-MS), processing software packages (e.g., vendor-specific vs. open-source), or instrument platforms (e.