Claude Skills by HolobiomicsLab

Use when you have experimental fragment m/z peaklists from Q-Exactive orbitrap, Agilent Q-TOF, Bruker Q-TOF, or SCIEX Q-TOF UHPLC-HRMS/MS instruments (in CSV or mzML-derived table formats) and need to assign lipid identities using untargeted or targeted tandem MS data.

Use when when preparing raw mass spectrometry imaging (MSI) ion images for deep learning–based representation learning, especially when you need to generate augmented image pairs that preserve domain-specific artifacts (photon counting noise, missing pixels) while varying appearance.

Use when when you have preprocessed MALDI-MSI data (in msimat format) and want to determine whether abundant peaks are actually molecular adducts of simpler parent ions rather than distinct metabolites.

Use when when you have a metabolite structure (SMILES or molecular graph) and need to predict its ionization behavior in a mass spectrometry experiment using a specific derivatizing matrix or ionization mode. Use this skill when the expected ions are non-standard (i.

Use when when preprocessing a public MS/MS spectral library (e.g., GNPS) for machine learning and you discover discrepancies between expected and observed compound counts after filtering by instrument type, or when a known instrument metadata issue (e.

Use when when you have an unknown MS/MS spectrum (with ≥10 peaks, precursor m/z, and at least 5 fragment ions) and need to identify it by comparing against a curated spectral library with annotated InChIKeys or chemical structures.

Use when when you have detected peaks in a direct injection FTICR-MS mzML file (or similar high-resolution MS format) and need to assess whether m/z measurements are accurate and consistent across the m/z range.

Use when you have a GC-MS dataset with a Match.Factor column (output from Agilent Unknowns Analysis or equivalent) and need to retain only high-confidence compound identifications.

Use when when integrating LC-MS/MS data from diverse sources (e.g., public repositories like MSV000080102, instrument outputs, or precomputed workflows) into NPDtools pipelines.

Use when after importing raw LC-MS/MS data files into the SIRIUS Java framework, before constructing indexed spectrum objects or submitting data to CSI:FingerID, CANOPUS, or MSNovelist web services.

Use when when you have a Thermo Fisher Scientific Orbitrap .raw file and need to confirm that a targeted acquisition method (e.g., PRM targeting a specific precursor m/z) is achieving uniform cycle timing.

Use when you have raw or processed FT-ICR MS spectra with detected peaks (m/z values) and need to: (1) assign elemental compositions to each peak, (2) filter assignments by mass error tolerance and isotopic presence, or (3) prepare a peak table with molecular formula annotations for chemodiversity.

Use when you have R Spectra objects and need to apply Python-only MS algorithms (e.

Use when you have raw mzML files and feature tables (CSV format from mzMine or XCMS) from untargeted LCMS experiments and need to distinguish true metabolite peaks from false positives introduced by the peak-picking algorithm.

Use when you have preprocessed MSI data (as a CSV intensity matrix or Cardinal MSProcessedImagingExperiment object) and suspect that observed peaks include both parent ions and their adducts formed with matrix or salt species.

Use when you have a Pandas DataFrame containing mass spectrometry data (e.

Use when when processing centroided .mzML LC–MS runs with a multi-polarity target list (i.e., some targets ionize in positive mode, others in negative mode, or both) and you need to detect peaks and extract ion chromatograms without manually subsetting the raw data by polarity beforehand.

Use when when you need to locate and extract quantitative retention time and intensity data for known peptide standards (e.g., iRT peptides) from a Thermo .raw file to validate LC-MS retention time linearity, assess method reproducibility, or establish retention time calibration curves.

Use when you have a high-resolution LC-MS/MS experiment with a measured [M+H]+ or [M-H]− ion mass and optionally a parent ion fragmentation spectrum (peak list with m/z and intensity pairs), and you seek to generate candidate molecular structures for an unknown metabolite that may not be in.

Use when when you need to enable users to express complex mass spectrometry search patterns (e.

Use when you have acquired tunemix data (positive or negative ion mode, in .h5 format) with known CCS reference values and need to construct a calibration function that will later predict CCS values for unknown analytes.

Use when you have pre-processed MS/MS spectra and need to prepare them for word-embedding-based similarity methods (e.g., Spec2Vec).

Use when after identifying statistically significant features (e.

Use when after generating a feature table from mzML data (via Asari) and before performing MS1 or MS2 annotation.

Use when you have a mass spectrometry visualization library that claims to support multiple plotting backends and need to verify that: (1) all backends produce functionally equivalent outputs, (2) execution times are consistent with reported benchmarks, and (3) the library scales appropriately.

Use when when you have a collection of mzML.gz files from a multidimensional MS instrument (e.

Use when after applying frequency domain calibration (Ledford, linear, or quadratic equation) to a raw FT-ICR mass spectrum, validate the calibration quality by measuring residual mass errors across the m/z range.

Use when you have centroided LC-MS/MS spectral data (in MGF, mzXML, mzML, or mzData format) and want to identify known or predicted natural product structures present in your sample.

Use when you have raw GC-MS data in netCDF or vendor-specific binary format and need to separate co-eluting compounds and extract clean mass spectra for each individual chemical component prior to molecular networking, spectral matching, or metabolite identification workflows.

Use when after computing all pairwise mass differences from a mass spectrometry imaging dataset, when you need to identify which mass differences correspond to real molecular adducts (e.g., metabolite–matrix or metabolite–salt ions) rather than noise.

Use when when processing raw or centroid mass spectra (e.g., ESI-MS or FT-ICR data from Bruker .d or Thermo .raw formats) and you need to remove instrument noise and low-abundance peaks before peak picking or molecular formula assignment.

Use when you have an untargeted metabolomics feature table (with m/z and retention time columns) and a metabolic network database with compound chemical formulas, and you want to connect observed features to known metabolic reactions and pathways using mass matching rather than metabolite.

Use when when linking statistically significant LC-MS features into structural clusters based on adduct signatures and cross-assay references (e.g., [M+H]+/[M-H]−), and you need to specify the maximum allowed deviation (in ppm) between observed m/z values and calculated neutral masses.

Use when after mass tracks have been aligned across all samples (either via pairwise alignment for ≤10 samples or nearest-neighbor clustering for larger cohorts), and you need to generate a single representative m/z per aligned bin for downstream feature extraction and annotation.

Use when when you have centroided mzML files from LC-MS metabolomics and need to construct high-mass-resolution mass tracks for each sample before alignment. Apply this skill at the start of an untargeted metabolomics workflow, before building a cross-sample MassGrid.

Use when after LDA-based Mass2Motif discovery has generated a set of recurring fragmentation patterns (motifset_optimized.

Use when after MS2LDA has inferred a motifset and you need to visualize and export the relationships between discovered Mass2Motifs for post-processing exploration, comparative annotation, or integration with external tools. Use this skill when you have motifset.json or motifset_optimized.

Use when when you have a preprocessed bag-of-fragments corpus from tandem mass spectrometry spectra and need to train an MS2LDA model to discover Mass2Motifs.

Use when you have two separate LC-MS untargeted metabolomic feature datasets (each with retention time and m/z values) and need to establish feature-to-feature correspondence between them.

Use when when you have mass spectrometry data (chromatograms, spectra, mobilograms, or peak maps) in a Pandas DataFrame and need to generate the same visualization in multiple formats—e.

Use when you have IC-FTMS measurement records in JSON format with multiple samples per metabolite assignment and you need to verify that a matrix directive with collate='assignment' correctly groups records by assignment identifier and merges sample intensity values into a single dictionary per.

Use when after executing memo_from_unaligned or memo_from_aligned functions to generate a MemoMatrix object from MS2 spectra or aligned feature tables.

Use when after fitting a Multi-Block PLS (MB-PLS) discriminant or regression model on multi-assay LC-MS intensity data (e.g., HPOS, LPOS, LNEG blocks), and you need to identify which features drive model performance and warrant further statistical validation or biological interpretation.

Use when you have a feature table from LC- or GC-HRMS data (either detected via pyOpenMS or imported as a custom feature list) containing m/z, retention time, and intensity values, and you want to rapidly filter to candidate PFAS features that exhibit the elevated mass defects typical of.

Use when you have aligned feature tables (CSV format) with corresponding MS2 spectra data (MGF or mzML files), and need to construct a sample-level vectorization matrix where each row represents a sample and columns encode the occurrence counts of MS2 peaks and neutral losses observed in that.

Use when you have generated one or more MemoMatrix objects (MS2 fingerprint matrices from separate sample sets) and need to combine them for cross-cohort alignment, validate structural consistency after merging, or prepare merged matrices for downstream filtering and visualization.

Use when when designing or optimizing backends that handle large MS datasets (mzML, mzXML, CDF files via MsBackendMzR), to verify that claimed memory advantages of on-disk or chunked approaches actually materialize in practice.

Use when when you have molecular input data (SMILES strings or graph representations) and need to predict molecular properties or spectra using graph neural networks. Apply this skill specifically when the base chemprop MPNN must be extended with new feature modules (e.

Use when you have a baseline GNN model for predicting a continuous molecular property (e.

Use when you have consensus metabolic reconstructions for all members of a microbial or plant community (e.