Claude Skills by HolobiomicsLab

Use when you have raw LC-MS/MS data files (mzML/mzXML format from Thermo, Waters, or Bruker instruments) and a list of target compounds defined by precursor m/z values (and optionally retention time windows).

Use when when designing a targeted lipidomics experiment and you have lipid species definitions (including chain composition and adducts) but need to configure precursor–fragment transitions for MRM or PRM acquisition on a Thermo QExactive HF, Agilent QTOF, or compatible high-resolution or.

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 processing SWATH-MS (Sequential Windowed Acquisition of all Theoretical Mass-spectra) raw data files (mzML or vendor format) for untargeted metabolomics, specifically before attempting to deconvolute overlapping MS/MS spectra.

Use when you have generated a lipid spectral library (lipid identities, adducts, m/z values, fragmentation patterns) and your downstream analysis requires DDA acquisition on an Orbitrap instrument using Excalibur software.

Use when you have an unknown MS/MS query spectrum with a known or measured precursor m/z value and need to search a spectral library (local or public: GNPS, MASSBANK, DrugBANK) to annotate the compound.

Use when when you have a compound's SMILES string or molecular formula and need to determine the expected precursor ion m/z for comparison against observed spectra, particularly before applying formula-based denoising, entropy similarity scoring, or denoising search against reference libraries.

Use when when comparing two MS/MS spectra using modified cosine similarity and the precursor m/z values differ, indicating potential neutral losses, adduct variations, or analogs with different substituents.

Use when after retrieving top-scoring library candidates from a full MS2Deepscore comparison, but before or during final re-ranking.

Use when when analyzing tandem mass spectra (MS/MS data) and you want to incorporate neutral loss patterns—characteristic mass losses from molecular precursors—into your spectral similarity or feature representation.

Use when after cosine similarity or dot-product scoring of experimental MS/MS spectra against a reference library, when you need to eliminate matches whose precursor m/z values deviate too far from the experimental precursor m/z, thereby improving specificity of library matching before final.

Use when when preparing augmented training data for Siamese or contrastive learning architectures in mass spectrometry, specifically when you need to generate hard negative examples that are spectrally distinct but mass-similar to positive examples.

Use when after loading an MsmsSpectrum object but before intensity filtering or spectral annotation.

Use when you have centroided MS2 spectra from data-dependent acquisition (ddMS2) in mzML format and seek to prioritize potential PFAS features by detecting diagnostic fragment masses.

Use when you have raw MRM sample files from a LC-MS/MS instrument and need to systematically recover all precursor m/z and product m/z pairs for each transition. Use this as an initial parsing step before quantitation, method optimization, or transition verification workflows.

Use when when computing entropy_similarity() between experimental (noisy or denoised) MS/MS spectra and reference library spectra, if the precursor m/z value is known and available.

Use when when you have normalized MS1 ion intensity features from multiple samples and need to assess overall data quality, detect systematic batch effects or instrumental drift, or identify which samples deviate significantly from the cohort norm in an omics-agnostic manner (metabolomics.

Use when when you have computed pairwise distances between MS2 fingerprint vectors from multiple metabolomics samples and need to visualize sample similarity relationships in low dimensions.

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 during the LDA training phase when you need to decide whether the model has learned a stable representation of Mass2Motifs.

Use when you have preprocessed tandem mass spectrometry spectra converted into a bag-of-fragments representation (with fragments and neutral losses extracted and noise filtered) and your goal is to discover recurring fragmentation patterns or substructures across a large spectral dataset without.

Use when after running ModiFinder's probability generation on a known compound–modified compound pair, you have a vector of per-atom modification probabilities and need to validate whether the predicted probability peaks align with the true modification sites.

Use when you have raw MS2 spectra (m/z and intensity pairs) that you want to match against a large training dataset of annotated library spectra (e.g., GNPS), and you need to reduce noise and computational burden before applying kernel-based scoring methods such as IOKR.

Use when after training or loading a NeatMS neural network model, apply this skill when you have a labelled validation dataset and need to determine the optimal probability threshold that maximizes classification performance (true positives minus false positives).

Use when you have a trained NeatMS neural network model (.h5 format) and need to select an operating threshold for peak classification on your LCMS dataset.

Use when you have a ProForma 2.0 peptidoform string (e.

Use when when you have a ProForma 2.0–formatted peptide string with PSI-MOD or UniMod modification labels and need to extract the underlying amino acid sequence and modification positions before performing theoretical fragment ion calculation or spectrum peak matching.

Use when a paired omics project record contains a genome identifier (e.g., GenBank accession) but lacks the corresponding organism name field, and you need to populate that metadata field to enable full text search, sample tracking, or project validation before archival to Zenodo.

Use when when a user uploads a JSON project document to the Pairing Omics Data Platform, before accepting it into the repository or indexing it for search. Use this skill to catch missing required fields, incorrect field types, and constraint violations early in the submission workflow.

Use when after initial peak detection on composite mass tracks via local maxima and smoothing, when you have unfiltered peak lists (JSON or structured format) containing prominence values and need to reduce the number of detected features while maintaining signal quality.

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 a collection of MS/MS spectra in MGF format and need to prepare them for GPU-based clustering. Dataset size and available GPU memory are critical: use GTX 1080Ti for smaller proteome datasets; use GTX 3090 for datasets like PXD000561 that exceed GTX 1080Ti capacity.

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 after executing annotateRC to match six or more lipidomics/metabolomics features against ion fragment databases (e.

Use when when you have an MS2 scan with observed peak data (m/z, intensity, charge state), a known peptide sequence, and need to determine which observed ions correspond to B and Y fragment ions. Apply this skill particularly when working with natural abundance (1.

Use when you have an experimental tandem mass spectrum and chemical formula for an unknown compound, and need to identify the true structure among all PubChem entries with that formula.

Use when you have an experimental MS/MS spectrum (m/z and intensity pairs with known precursor m/z) and need to identify the compound by searching against public repositories or a local reference library.

Use when after annotating a mass spectrometry spectrum with fragment ions (e.g., via ProForma 2.

Use when you have a mass spectrometry spectrum from a supported repository (GNPS, MassBank, MetaboLights, Metabolomics Workbench, ProteoXchange, MS2LDA, or MassIVE) and need to include it in a publication or supplementary material.

Use when you have resolved a USI string pointing to a spectrum in a supported repository (GNPS, MassBank, MassIVE, MetaboLights, Metabolomics Workbench, ProteoXchange, or MS2LDA) and need to embed a publication-ready visualization or machine-readable reference that preserves the spectrum's.

Use when you have Nightingale Health 1H-NMR metabolomics measurements for a new cohort and wish to compute one or more established metabolic risk scores (mortality, MetaboAge, cardiovascular event, type-2 diabetes, COVID-19 severity) without recalibration.

Use when analyzing GC-MS data containing overlapped peaks where two or more components co-elute within the same retention time window, making direct spectral assignment impossible.

Use when when you have polypeptide sequences and need to compute their monoisotopic or average mass, isotopic distribution patterns, or other physico-chemical properties; or when you need to parse and manipulate MS/LC-MS data, FASTA databases, or search engine output in a Python workflow.

Use when when building a machine learning library for scientific workflows where users need to: (1) prepare domain-specific data (e.

Use when when you have classification predictions and ground-truth labels and need to generate a confusion matrix visualization with flexible normalization (by row, column, or all elements) and styling options for publication or diagnostic review.

Use when when you have Thermo Fisher RAW mass spectrometry files and need to extract mass-to-charge ratios, intensities, scan metadata, and peak lists within a Python script or notebook for downstream computational analysis, and you require programmatic control over extraction parameters rather.

Use when you have developed new scoring logic (e.g., average inchikey score, neighbourhood score) for an MS/MS spectral analogue search tool and need to embed these components into an existing production pipeline that uses a random forest to combine multiple features for candidate re-ranking.

Use when when you have completed spectral data preprocessing in Jupyter notebooks and generated a specXplore session data object saved to disk, use this skill to restore that object into a live specXplore dashboard session instance for interactive LC-MS/MS spectral data exploration, without.

Use when when setting up a new computational environment for tandem MS/MS spectrum clustering or other proteomics analysis, and you need to install a tool (like falcon) that depends on specific versions of auxiliary packages (like spectrum-utils==0.3.5).

Use when when you have LC-MS/MS data in MZmine-generated MGF and CSV files (for positive and/or negative ionization modes) and need to apply a sequence of deduplication, annotation, and dereplication steps defined in a MolNotator YAML configuration file to predict actual molecules and build.