Claude Skills by HolobiomicsLab

Use when you have received raw FT-ICR transient data from Bruker Solarix or ThermoFisher instruments and need to perform signal processing, apodization, calibration, or molecular formula assignment in CoreMS. The data must be in native vendor format (.d directory with ser/fid files, or .

Use when after peak detection on a composite mass track has identified candidate peaks in a mass chromatogram, and before compiling the final feature table.

Use when after peak detection on mass track segments using find_peaks, when you need to distinguish genuine chromatographic peaks from noise-induced false positives or irregular shapes.

Use when you have a combined EI mass spectral library (MSP format) lacking experimental RI values, access to NIST ri.dat and USER.

Use when after integratePeaks has been executed with a chosen integration method (e.g., fixed_size with RIP saturation threshold of 0.1) on a clustered, baseline-corrected GC-IMS dataset.

Use when after peak detection in GC-IMS preprocessing, when you need to assess whether detected peaks from multiple samples align to the same chemical entities (clusters) using hierarchical clustering.

Use when after autoQ has extracted isotopologue peak area measurements from mz(X)ML files and you need to prepare the integrations data frame for visualization with metBarPlot or comparative analysis.

Use when when working with raw GC-MS data in NetCDF (ANDI) format that requires peak detection, baseline removal, and retention time alignment before spectral matching against reference libraries such as PNNLMetV20191015.MSL.

Use when you have raw GC-MS data (aroma, breath, or other volatile analyte samples) in NetCDF or vendor-native format and need to identify multivariate chemo-/biomarker features without conventional peak picking.

Use when after auto-deconvolution of GC-MS data has produced a table of individual deconvolved mass spectra (one per detected peak), and your goal is to group spectra into molecular families based on mass spectral similarity rather than retention time or chemical class.

Use when you have raw GC-MS data (in netCDF or vendor format) containing overlapping chromatographic peaks from complex mixtures where individual compound spectra cannot be resolved by simple peak picking.

Use when when you have GC-MS data with detected peaks that require structural annotation, retention index calibration has been applied (typically using FAMES standards), and you need to assign compound identities with confidence scores.

Use when you have raw GC-MS data files containing overlapped peaks (unresolved components with coeluting retention times) and need to predict pure mass spectra for each individual component using a Transformer-based model.

Use when when you have deconvolved GC-MS spectra (post-deconvolution output compatible with GNPS_GC input specification) and need to group them by chemical similarity to construct a molecular network.

Use when you have raw GCIMS sample files (from a GC–IMS instrument) and an annotations table (Excel, CSV, or TSV) with sample metadata, and you need to begin the GCIMS preprocessing pipeline.

Use when input GC-MS data (netCDF or mzML format) exhibits overlapping chromatographic peaks where multiple analytes co-elute at the same retention time, resulting in composite mass spectra that conflate signals from distinct molecular species.

Use when you have raw GCxGC-MS data in NetCDF format that contains instrumental and chemical noise (baseline drift, high-frequency signal artifacts) and you need to prepare multiple preprocessed chromatogram objects for downstream multiway PCA or biomarker discovery.

Use when after preprocessing a set of aligned 2D-TIC (two-dimensional Total Intensity Chromatogram) matrices from GCxGC-MS experiments—when you have multiple samples across distinct biological groups (e.

Use when you have preprocessed individual GCxGC-MS chromatograms (each smoothed with Whittaker smoother, baseline-corrected with asymmetric least squares, and aligned against a reference using 2D correlation optimized warping) and need to consolidate them into a single analytical object for.

Use when your metabolomics dataset contains missing values below a known detection limit (left-censored MNAR data), and you need to recover these values while respecting the truncation constraint.

Use when your input is raw or semi-processed MS/MS spectra fetched from GNPS or a compatible library (e.g., EMBL-MCF 2.0, NIST23) and you need to prepare them for neural-network-based formula prediction.

Use when after constructing a spectral similarity network from pairwise cosine similarity scores between deconvolved GC-MS spectra.

Use when when generating synthetic LC/GC-MS .mzML files with companion ground-truth peak tables for method validation, you need to calculate the absolute maximum intensity that each simulated peak would exhibit in the raw mass spectrometry matrix.

Use when after peak detection in GC-IMS preprocessing, when you need to group peaks across multiple samples and must decide whether euclidean distance is appropriate for your drift time and retention time coordinate space, and when you need to validate that your chosen dt_cluster_spread_ms and.

Use when you have identified one or more proton NMR spectral regions-of-interest (ROIs)—defined by lower and upper chemical-shift bounds in ppm—from complex biological samples (serum, saliva, urine, tissue, CSF) and need to generate a ranked list of plausible metabolite identities.

Use when you have a two-dimensional MS map (m/z vs retention time) from GC–MS or LC–MS data and need to discriminate analytes and identify marker features without false positives from peak picking; particularly useful for untargeted metabolomics at ppb sensitivity (e.

Use when you have GC–MS or LC–MS data represented as a two-dimensional map (m/z vs retention time) and need to identify analyte signals and marker features while minimizing false peak detections.

Use when when you have raw GC–MS data in two-dimensional m/z × retention time format (NetCDF or proprietary binary) and need to identify marker features across aroma or breath samples at parts-per-billion concentration levels, particularly when conventional peak picking introduces false positives.

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 a metabolomics dataset with left-censored missing values (e.g., below limit of quantification in LC/MS or GC/MS) and need to evaluate multiple imputation approaches.

Use when when you have experimental UHPLC-HRMS/MS or direct infusion MS/MS data and need to identify lipid species by comparing observed fragment m/z values against a library of simulated fragments. Apply this skill when your lipid library is incomplete or specialized (e.

Use when designing multi-batch LC/GC-MS experiments where you need to control for batch effects (e.g., instrument drift, reagent lot variation) and have identified both a balance dimension (e.g., sample group, treatment condition) and a randomization dimension (e.

Use when when configuring a multi-well plate design (96-well, 384-well, or other format) in InjectionDesign for LC/GC-MS analysis and you need to specify whether analytical samples and QC controls should be injected row-by-row or column-by-column.

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 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 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 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 raw LC-MS data in mzML format (converted from .raw or acquired directly in that format) and need to invoke Asari for feature extraction, but the ionization mode is not explicitly specified in your experimental metadata or pipeline configuration.

Use when you have LC-MS peak tables from parallel unlabeled and labeled (isotope-traced) sample cohorts, sample metadata defining groups and conditions, and you seek to identify metabolic intermediates that accumulate differentially in a perturbed system (e.

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 you have compiled a multi-source EI library (NIST, RIKEN, MoNA, SWGDRUG) into a single msp object and want to enrich it with experimental retention index metadata. Apply this skill when you have access to NIST library installation files (ri.dat and USER.

Use when you have multiple competing spectral similarity scoring methods (e.

Use when you have centroid mzML files from LC-MS experiments (converted from Thermo .raw or other vendor formats) and need to identify and quantify individual chemical features across multiple samples.

Use when after chromatographic peak detection on preprocessed LC-MS data, when you have hundreds or thousands of individual m/z × retention-time peaks and need to associate them into biologically meaningful feature groups.

Use when after GCMSFormer has predicted the pure mass spectral matrix S from overlapped GC-MS peaks.

Use when you have a metabolomics dataset (LC/MS or GC/MS) with missing values and need to determine which are below the limit of detection (LOD) or limit of quantification (LOQ). Left-censored classification is necessary when the missingness is informative—i.

Use when when you have a complete metabolomics abundance table (e.g., targeted LC/MS or untargeted GC/MS counts) and need to generate synthetic left-censored missingness for evaluating imputation algorithm performance.

Use when after khipu has grouped LC-MS features into empirical compounds with inferred molecular formulas and adduct assignments.

Use when after applying mspcompiler pipeline transformation steps (e.g., reorganize_mona, assign_smiles, assign_ri, read_multilibs, separate_polarity, complete_mgf) to confirm the operation succeeded without data loss or structural corruption.

Use when you have an unknown electron ionization (EI) mass spectrum and need to identify the compound by comparing it against a reference library (msp file format).