Claude Skills by HolobiomicsLab

Use when you have raw MS data in a supported instrument format (Agilent .d, Thermo .raw, Bruker .d, mzML) and a predefined list of molecular targets (CSV with m/z and/or retention time) that you need to quantify.

Use when when you need to verify whether a specific mass spectrometry instrument platform (vendor and model), acquisition mode (e.g., targeted, ddMS2-topN, AIF, direct infusion, imaging), or file format is compatible with a lipidomics or proteomics software tool;

Use when when you have pre-processed FT-ICR MS peak lists with assigned molecular formulas and wish to move beyond univariate chemical composition analysis to understand metabolic transformation pathways and hub metabolites.

Use when you have a dataset of augmented simulated overlapped GC-MS peaks and need to train a Transformer model to automatically deconvolve them into pure component mass spectra.

Use when you have a preprocessed unknown sample spectrum (m/z peaks and intensities) from high-throughput mass spectrometry (DI-MS, ASAP-MS, or ambient ionization methods) and need to identify the species or authenticate a sample against a curated reference database of known spectra.

Use when after database search algorithms have scored unknown MS samples against reference species, and you need to visually inspect and confirm species assignments or identify ambiguous classifications.

Use when you have raw LC-MS or GC-MS data files from a mass spectrometer (in mzML, NetCDF, or mzXML format) and need to detect chromatographic peaks, correct m/z bias via mass calibration (e.

Use when a task needs a skill from ASB Metabolomics — direct-infusion-MS — search this unit's 97 evidence-grounded skills, then apply and optionally ground the one that fits.

Use when when you have raw GCxGC-MS data imported from NetCDF into a 2D-TIC chromatogram object and need to remove chemical and instrumental noise (column bleeding, baseline drift, detector contamination) to reveal metabolite differences between sample groups for downstream multiway PCA or.

Use when after peak detection and MS1 feature extraction from FIA-MS, GC-MS, LC-MS(/MS), or CE-MS data, when you need to identify unknown metabolites by matching observed m/z values to a reference database and want to recover HMDB identifiers, molecular formulas, and structural annotations for.

Use when parsing, standardizing, or filtering MS spectra from mixed or heterogeneous databases where adduct assignment may be manually entered, auto-inferred, or missing.

Use when when you need to evaluate how a specific algorithm parameter (such as SearchMolecularFormulas first_hit mode) affects the quantity and quality of molecular formula assignments on a given spectrum or dataset.

Use when you have raw chromatography–mass spectrometry data (GC-MS or LC-MS) in 2D m/z–retention time format and need to identify and discriminate multiple analytes while avoiding false peak detections inherent in conventional peak picking.

Use when after generating a feature table via mzrtsim() containing simulated peak abundances across samples with condition and batch effects, and you need to expose the abundance data through Bioconductor's SummarizedExperiment interface for use with standard accessor functions (assay(), colData()).

Use when you have a two-dimensional GC–MS or LC–MS dataset (m/z vs retention time) and need to identify discriminative analyte features without relying on conventional peak picking algorithms. This is especially valuable when analyzing complex, low-abundance samples (e.

Use when when you have a molecular geometry (XYZ format) and need to predict electron ionization (EI) mass spectrum fragmentation patterns by exhaustively sampling conformational space and reaction intermediates.

Use when when designing injection plate layouts in InjectionDesign and needing to display sample positions with clear, domain-appropriate labels on the y-axis (e.g., row identifiers, well coordinates, or sample indices).

Use when when you have trained a candidate model (e.g., an ensemble, a new architecture) and need to demonstrate its advantage over published or reference implementations on the same test data.

Use when when you have imported a raw GCxGC-MS chromatogram as a 2D-TIC (2D Total Intensity Chromatogram) object from a NetCDF file and observe steady or increasing baseline intensity caused by instrumental contamination, column bleeding, or thermal drift.

Use when you have developed or adapted an analytical method (e.g., NPFimg for GC–MS marker identification) and need to demonstrate its reliability or improved performance over a widely-used reference method (e.g., XCMS). Apply this skill when you have access to both the same raw input data (e.

Use when when you need to establish comparable performance baselines for a novel spectrum prediction model and require fair comparison across multiple baseline architectures. Trigger this skill when: (1) you have a new spectrum prediction approach (e.g., ICEBERG, SCARF) to benchmark;

Use when your peak intensity matrix exhibits batch-to-batch variation (retention time drift, signal intensity fluctuation across injection sequences), you have QC samples injected at regular intervals throughout the analysis, and you want to preserve biological signal differences while removing.

Use when when you need to generate synthetic metabolomics feature tables with quantified batch effects for validating batch-correction methods. Use this skill when: (1) you want reproducible, ground-truth batch effects overlaid on condition-only variation;

Use when when converting simulated or real LC/GC-MS spectral data (m/z–retention-time intensity matrices) into mzML format for archival, sharing, or downstream processing.

Use when you have a trained sequence-to-sequence model (such as GCMSFormer) that predicts mass spectra from overlapped peaks, and you need to evaluate model performance on a held-out test set.

Use when you have GC–MS data from human breath samples and need to identify marker metabolites for disease diagnosis, phenotyping, or biomarker discovery without a predefined target list. Your data is noisy or conventional peak picking has produced high false-positive rates.

Use when after molecular formula assignment from FT-ICR MS peak data, when you need to classify metabolites by their redox state to predict bioavailability or lability, or when generating thermodynamic indices for chemodiversity analysis and environmental metabolomic interpretation.

Use when you have vendor-independent centroided mzML files from data-dependent acquisition (ddMS2) HRMS experiments and need to extract a reproducible feature list with mass, chromatographic, and intensity dimensions as input to PFAS prioritization, suspect screening, or other MS-based analyses.

Use when when you have raw GC-MS output (CSV with columns: Component.RT, Base.Peak.MZ, Component.Area, Compound.Name, Match.Factor, File.

Use when when you have a list of chemically known compounds and need to validate that an MS processing pipeline (e.g., mzExacto) correctly retrieves their characteristic m/z, retention time, match factor, and area values from GC-MS data.

Use when when you have a metabolomics peak list (m/z values with optional retention times) from LC-MS or GC-MS and want to filter a computationally expanded chemical library to only compounds whose calculated masses (accounting for ionization adducts) fall within a defined tolerance of observed.

Use when when you have query chemicals identified by GC-MS (with Match.Factor values) and need to verify structural similarity against a reference chemical library to confirm compound identity or detect structural analogs (e.g., isomers or homologs).

Use when after raw GC-MS CSV input has been parsed into separate matrices (Component.RT, Base.Peak.MZ, Compound.Name, Match.Factor, Component.Area) and you need to enrich sample-level identifications with authoritative chemical properties.

Use when after spreadOut() has converted raw CSV peak data into a structured list, when you have one or more Compound.Name entries from GC-MS that may be ambiguous, non-canonical, or missing standardized properties (exact mass, published retention times, reactive groups, database presence).

Use when when you have MS/MS spectra with known chemical structures (InChIKeys or SMILES) and want to validate whether a novel or existing spectral similarity scoring method actually reflects true chemical structural similarity.

Use when after identifying query chemicals from GC-MS data (via Match.

Use when you have a list of identified or suspected chemical compound names (e.g., from GC-MS Match.

Use when you have baseline-corrected and smoothed 2D-TIC chromatogram objects from individual GCxGC-MS samples that exhibit retention-time variations relative to a reference chromatogram, and you need to align peak positions across both dimensions before joining multiple samples for multiway PCA or.

Use when raw NetCDF-format GCxGC-MS chromatograms exhibit steady or increasing baseline intensity caused by instrumental contamination, column bleeding, or thermal drift;

Use when you have raw or folded 2D-TIC chromatogram data (typically imported from NetCDF files into RGCxGC chromatogram objects) that exhibits baseline drift, chemical noise, or instrumental artifacts that would obscure true metabolite peaks.

Use when you have raw GCxGC-MS chromatogram data in NetCDF (CDF) format from an instrument and need to load it into R for preprocessing (smoothing, baseline correction, peak alignment) and multivariate analysis.

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 need to generate blank or background-only .mzML files for method validation, when you want to create synthetic negative controls with realistic instrumental noise but no analyte peaks, or when you need to simulate serum matrix background (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 you have raw mzML files from an FTICR-MS or other direct-injection MS instrument and need to identify discrete chromatographic peaks across the m/z and retention-time dimensions. Use this skill when you must isolate individual ion signals before applying calibration corrections (e.

Use when you have loaded raw LC-MS or direct-injection FTICR-MS data (in mzML or netCDF format) into an XCMSnExp object and need to identify individual chromatographic peaks before feature grouping.

Use when after constructing baseline-corrected mass tracks (either composite across samples or per-sample) when you need to identify individual chromatographic peaks for feature extraction in LC-MS or GC-MS metabolomics workflows.

Use when when you have LC-MS/MS raw data (mzML or netCDF format) and need to isolate a specific compound's signal based on its known or suspected m/z value and retention time range.

Use when analyzing GC-MS data containing overlapping chromatographic peaks—a common scenario in untargeted metabolomics and environmental screening where sample complexity or chromatographic resolution limitations cause co-elution of structurally similar or temporally proximate compounds.

Use when when you have loaded raw GC-MS data in netCDF or mzML format and visual or statistical inspection reveals overlapping chromatographic peaks (i.e., multiple m/z ions co-eluting at the same retention time window).