Claude Skills by HolobiomicsLab

Use when you have a compiled EI or MS2 library object (read from MSP format via read_lib) and access to NIST ri.dat and USER.DBU files; you need to populate RI values for capillary GC-MS workflows.

Use when you have deconvolved GC-MS spectra (from overlapping chromatographic peaks) in MGF or mzTab format and want to group chemically related compounds, visualize their similarity relationships, and identify spectral families without prior library matching.

Use when you have multiple MSP or spectral library files (e.g., one per batch of analytical standards, or organized in a directory structure) that need to be read and merged into a single library object for downstream enrichment (SMILES assignment, RI annotation, write operations).

Use when you have loaded an MS2 library (from NIST, GNPS, or other sources via read_lib()) that contains both positive and negative ionization modes mixed in a single file, and you need to produce two separate, polarity-specific MSP files for use in MS-DIAL or similar tandem MS analysis software.

Use when you have executed batch searches of MS/MS spectra against multiple domain-specific MASST indices and need to integrate the resulting match outputs into a single coherent view.

Use when you have a .msp spectral library file with sparse or incomplete metadata (e.

Use when after isotopologue and adduct grouping has been completed and you need to associate MS2 spectra with consolidated feature groups in DDA LC-MS experiments.

Use when when importing mass spectrometry spectral data from public repositories or multi-source MGF/MSP files where metadata fields are inconsistent, missing, or non-standard;

Use when you have loaded an MsmsSpectrum object from a proteomics or metabolomics dataset and need to focus the analysis window on a specific m/z range relevant to your experiment (e.g., 100–1400 m/z for typical tryptic peptides).

Use when after running MetaMiner's Dereplicator stage to identify some RiPPs via direct database matching against a constructed structure database, apply this skill to enlarge the set of identifications by propagating those matches through spectral clusters and visualizing the connected components.

Use when you have raw MS/MS spectra in multiple formats (.mgf, .msp, .mzML) that contain background noise, instrument artifacts, or low-abundance fragments that would degrade downstream LDA motif discovery.

Use when you have processed LC-MS/MS spectral data (as a .mgf file with feature identifiers) and computed pairwise ms2deepscore similarity scores, and you need to create a 2-D projection suitable for dashboard visualization or high-level pattern discovery without losing similarity structure.

Use when you have a tandem mass spectrum with observed m/z peaks and a known peptide sequence (as a ProForma string, optionally with post-translational modifications), and you want to determine which observed peaks correspond to expected fragment ions (b-type, y-type, a-type) within a specified.

Use when you have raw MS/MS spectra with variable numbers of peaks at continuous m/z values and need to feed them to a neural network (e.g., Siamese network for similarity prediction) that requires fixed-size vector input.

Use when you have raw LC-MS data in mzML or mzXML format and need to extract reproducible, high-quality metabolite features (m/z, retention time, intensity) for global metabolomics.

Use when you have imported raw mass spectrometry data in formats such as MGF, MSP, mzML, or mzXML and need to clean the spectral data prior to similarity comparisons, metadata validation, or export.

Use when when you have MS/MS spectra (LC-MS or equivalent positive ionization mode data) that you intend to embed using Word2Vec or similar distributional semantic models, or when you need to prepare spectral data for training similarity models that learn peak co-occurrence patterns rather than.

Use when when you have extracted a raw Orbitrap scan from a .raw file and need to verify that the instrument operated within expected parameters and that observed peptide fragment ions rise substantially above noise—i.

Use when after running annotateRC to generate ranked candidate annotations for LC-MS AIF features, when you need to confirm that (1) the top-ranked candidate match is chemically plausible given the observed fragment ions, (2) alternative candidate annotations exist and are correctly ranked, and (3).

Use when you have executed multiple database search pipelines (Dereplicator, VarQuest, and/or Dereplicator+) on centroided LC-MS/MS spectra (in MGF, mzXML, mzML, or mzData format) against a chemical structure database of known natural products, and you need to: (1) identify which pipeline(s).

Use when when you need to understand or modify how MS2Query routes query spectra through its dual-pathway architecture, or when integrating MS2Query into another tool and need to trace how spectral similarity scores (MS2Deepscore) feed into library-match versus analogue-search branches with.

Use when after inferring Mass2Motif definitions from LDA modeling when you need to build a network representation of motif relationships.

Use when when you have processed LC-MS/MS spectral data in .mgf format with feature identifiers and need to compute a pairwise similarity matrix to support interactive exploration.

Use when after extracting and optionally combining MS2 spectra from a chromatographic peak (e.g., at a known m/z value like 304.1131), you need to determine which compound(s) in a reference library match the experimental spectrum.

Use when after preprocessing and filtering mass spectra (peak filtering, metadata cleaning) when you need to compare all spectrum pairs within a dataset or between a query set and a reference library to identify similar or related spectra.

Use when when you have detected LC-MS/MS features (MS1 peaks with MS2 fragmentation spectra) from untargeted metabolomics experiments and seek to organize them into a chemical hierarchy for comparative metabolomic analysis.

Use when after acquiring MS/MS spectral data from untargeted metabolomics experiments and having candidate transformed structures from biotransformation rule application.

Use when when you have paired MS/MS spectra with known structural similarity labels (Tanimoto scores from molecular fingerprints) and need to predict structural similarity for new spectrum pairs faster than fingerprint-based methods, or when you want to assess model prediction confidence per.

Use when you have a query MS/MS spectrum and need to identify the -matching library spectrum from a large spectral database, particularly when the research goal requires distinguishing between exact matches and structural analogues without separate workflows.

Use when you have a query spectrum (or set of query spectra) and need to rank candidate library spectra by their likelihood of sharing the same chemical structure.

Use when when you have extracted low-resolution mass spectra from individual chromatographic peaks in GC-MS data and need to match them against a spectral library (e.g., PNNLMetV20191015.MSL) to identify the unknown compound.

Use when when preparing MS/MS spectra from .msp files for transformer-based deep learning models in IDSL_MINT. Specifically: you have raw spectral data with variable peak counts and need fixed-size tensor inputs;

Use when you have preprocessed 1D ¹H and/or ¹³C NMR spectra from an unknown organic compound with ≤19 heavy atoms, and you need to recover its molecular structure (both formula and connectivity) rapidly without access to 2D NMR experiments (HSQC, HMBC, COSY) or mass spectrometry.

Use when when converting pre-processed MS/MS spectra into fixed-length vector representations using Word2Vec embeddings for Spec2Vec similarity scoring. Specifically, apply this skill after you have represented individual peaks and neutral losses as words ('peak@xxx.xx', 'loss@xxx.

Use when after implementing or modifying a cross-language integration layer that wraps Python mass spectrometry functions (e.g., spectral matching, peak detection, normalization from matchms or spectrum_utils) as R-callable functions within Spectra workflows.

Use when after applying any sequence of spectrum preprocessing operations (set_mz_range, remove_precursor_peak, filter_intensity, scale_intensity) to an MsmsSpectrum object, to confirm that the resulting arrays fall within specified m/z windows, intensity bounds, and peak count limits before.

Use when you have raw, high-resolution MS/MS spectra in mzML, mzXML, or MGF format that need to be prepared for fast similarity searching or clustering.

Use when you have computed a sparse pairwise distance matrix from nearest neighbor indexes and need to group spectra into clusters. Use this skill when: (1) you have a sparse similarity or distance matrix as input;

Use when when preparing MS/MS spectral data for training word-embedding models (Word2Vec, Skip-gram, CBOW) that will learn relationships between fragment ions and neutral losses.

Use when when implementing or auditing a deep learning pipeline for MS/MS-based molecular formula prediction, verify that precursor m/z values in the input feature array are zeroed before they reach the spectrum encoder (e.g., TCN).

Use when you have raw mass spectrometry spectra (peak lists or intensity arrays) that must be fed into a pre-trained deep learning model for substance classification (e.g., PS²MS for NPS detection).

Use when you have raw or minimally processed tandem MS spectra (in mzML, mgf, or other standard formats) and need to prepare them for spectral matching, library searching, or quantitative analysis.

Use when when you have raw mass spectrometry data from diverse instrument vendors (Thermo, Sciex, etc.) and need to harmonize and standardize spectrum-level metadata—including scan information, precursor m/z and charge, and ion selection parameters—into a queryable, vendor-agnostic tabular schema.

Use when a user submits one or more MS/MS spectra (via .mgf file, USI list, or direct upload) and the downstream analysis requires dispatching to a specific domain-specific MASST tool (microbeMASST, plantMASST, tissueMASST, microbiomeMASST, foodMASST, or metadataMASST).

Use when when you need to quantify and compare the filtering efficacy of mutually exclusive noise-threshold methods on the same input mass spectrum, or when validating that a selected noise-filtering strategy retains an expected number of peaks for downstream molecular formula assignment.

Use when when you have a tandem mass spectrum (MS/MS) and a ProForma 2.0 peptidoform string (e.g., DLTDYLM[Oxidation]K) and need to identify which observed spectrum peaks correspond to expected b-ion and y-ion fragments, in order to validate peptide identification or annotate spectrum quality.

Use when you have mass spectrometry spectral data loaded into a Pandas DataFrame with columns representing m/z (mass-to-charge ratio) and intensity values, and you need to visualize the spectrum to inspect peak patterns, identify high-abundance ions, or compare spectral profiles across samples or.

Use when when preparing raw MS/MS spectra for input to a Siamese neural network trained to predict structural similarity scores (Tanimoto).

Use when you have raw mass spectrometry spectra (in MGF, mzML, or similar formats) that must undergo standardized preprocessing before library matching, similarity searching, or performance benchmarking.

Use when you have parsed LC-MS/MS spectral data (precursor m/z, ionization mode, collision energy, and a list of fragment m/z and intensity pairs) and need to submit it to the CSI:FingerID web service for molecular fingerprint prediction as part of a metabolite identification workflow.