Claude Skills by HolobiomicsLab

Use when you have loaded raw MS intensity tables into QuantyFey and observe or suspect intensity drift artifacts across your measurement sequence. Drift is especially likely in long-running targeted MS experiments where calibration curves or internal standards show systematic variation over time.

Use when you have MGF or native MS/MS arrays (mz_array, intensity_array, precursor_mz, adduct) and want to predict the most likely molecular formula. The input spectra must include required MGF fields (TITLE, PRECURSOR_MZ, PRECURSOR_TYPE, COLLISION_ENERGY) or equivalent Python API parameters.

Use when after computing a sparse pairwise distance matrix from nearest neighbor indexes of MS/MS spectra (in mzML, mzXML, or MGF format), and you need to assign each spectrum to a cluster group for downstream analysis such as peptide identification or spectral library construction.

Use when you need to determine the complete set of validated instrument/vendor and acquisition mode combinations for a mass spectrometry analysis tool, when assessing whether your specific instrument platform (vendor, model, acquisition method) is supported before committing to a workflow, or when.

Use when when you need to prototype, test, or benchmark MS1-only acquisition strategies on a defined set of metabolites (e.

Use when you have raw MS2 spectra files (mzML, mgf, msp, mzxml) that may contain multiple MS2 spectra per feature and require reduction or standardization before library matching.

Use when you have centroided MS2 spectra from data-dependent LC- or GC-HRMS measurements and need to rapidly prioritize potential PFAS features within a larger feature set.

Use when after MS2 fingerprints have been generated by counting MS2 peaks and neutral losses in each sample, and you have aligned them into a MemoMatrix (sample-by-fingerprint matrix).

Use when you have two MemoMatrix objects generated from separate sample cohorts (e.

Use when you have raw MS2 spectral data (MGF, mzML, or msp format) and need to generate a sample-level fingerprint for comparison across metabolomics samples, especially when samples were acquired using different LC methods, mass spectrometer technologies, or exhibit poor feature overlap or large.

Use when when beginning a MEMO analysis workflow with raw or unaligned MS2 spectra files and needing to extract fragmentation data and precursor information before counting MS2 peaks and neutral losses to generate sample fingerprints.

Use when after peak picking, sample alignment, and isotopologue/adduct grouping are complete, and you have DDA-MS2 scans associated with grouped feature ions.

Use when when you have DDA LC-MS/MS data (mzML format) with identified chromatographic peaks at a specific m/z (e.g., 304.1131) and multiple MS2 spectra fragmented from that precursor, and you need to produce a single high-confidence MS2 spectrum for comparison against reference databases (e.

Use when after creating a GNPS mass spectral molecular network and running an MS2LDA experiment, use this skill when you want to identify and visualize which substructural motifs (Mass2Motifs) are shared across clustered spectra, particularly to highlight fragmentation pattern similarities between.

Use when when you have a GNPS molecular network (classical or feature-based) and corresponding MS2LDA experiment results, and you want to annotate network nodes with discovered substructural motifs to support structural elucidation and chemical family interpretation.

Use when your MSI data is stored in a Cardinal imaging experiment object (version 2.2+) that has already been peak-binned with peakBin(), and you want to run mass2adduct's massdiff() and adductMatch() pipeline without manually exporting to CSV;

Use when you have MSI intensity data exported from MSiReader, SCiLS, or Cardinal as plain-text CSV files or as Cardinal MSProcessedImagingExperiment/MSContinuousImagingExperiment objects, and need to load it into R as a validated msimat object for mass difference and adduct analysis.

Use when you have acquired EI or MS2 library files in MSP format (e.g., from NIST via Lib2NIST export, RIKEN, MoNA, SWGDRUG, or GNPS) and need to read them into R to assign SMILES, retention indices, or combine multiple libraries into a single consolidated MSP file for MS-DIAL.

Use when you have one or more .msp spectral library files (NIST format) that need to be ingested for metadata curation, enrichment via web services, or export after transformation. Use this skill as the entry and exit point for any .msp-based annotation or analysis pipeline.

Use when you have a .msp format MS/MS spectrum library (e.g., from MassBank or similar public databases) and need to convert it into individual CSV entries indexed by positive or negative ionisation mode for use as a custom fragment library in MetaboAnnotatoR annotation pipelines.

Use when you have statistically significant features from multiple LC-MS assays with different ionization modes (e.g., positive and negative ESI) and need to collapse redundant feature representations into single structural entities.

Use when you have independent LC-MS assays (e.g., positive and negative ionization modes, different lipid profiling assays, or different chromatographic methods) analyzed on the same sample cohort and want to integrate them into a single discriminant or regression model without losing assay-level.

Use when your metabolomics experiment includes samples acquired across multiple instrument runs, different preparation dates, or distinct sample cohorts.

Use when you have generated hypothetical links (e.g., GCF–MF pairs) and computed multiple independent scoring functions on them (e.g., strain co-occurrence, IOKR structural fingerprint matching), but individual scores show incomplete discrimination power.

Use when you have raw or processed mass spectrometry data in HDF5 (.h5) or mzML format and need to ingest it into DEIMoS for multi-dimensional analysis.

Use when you have two or more feature tables in HDF5 format with detected peaks characterized across multiple dimensions (mz, drift_time, retention_time, intensity) and need to harmonize feature coordinates across samples to account for instrument variation, enabling downstream cross-sample.

Use when when building a transformer-based model to process mass spectrometry data (MS/MS spectra or fingerprints) where you need the model to learn multiple independent attention patterns across spectrum features simultaneously.

Use when when you need to support multiple plotting library backends (static or interactive) for the same data visualization domain (e.

Use when when converting tabular data to JSON via the matrix directive, you need to exclude records that fail domain-specific validation (e.g., only retain records where a 'test' condition evaluates to true) and/or reorder the output list by one or more fields in ascending or descending order.

Use when you need to evaluate whether a newly released or candidate library (e.g., spectrum_utils v0.4.

Use when when you have matched transcriptomics (RNA-seq read counts), intracellular metabolomics (LC-MS abundance data), and extracellular flux measurements (YSI bioanalyzer or similar) from multiple biological samples or cell lines, and you need to determine whether differences in metabolic enzyme.

Use when you have paired microbiome-metabolome (or similar multivariate) datasets and want to quantify whether training on a superset of features (e.g., both annotated and unannotated metabolites) improves prediction accuracy for a well-defined target subset (e.

Use when after initial retention-time-based feature grouping (e.

Use when you have two or more independently processed MemoMatrix objects (each generated from a separate sample set) and your analysis goal requires direct comparison of MS2 fingerprint profiles across those samples.

Use when you have two or more independent scoring functions ranking the same set of candidate links (GCF-MF pairs, BGC-spectrum associations, etc.), and you want to determine whether they capture complementary information that justifies combining them.

Use when your research software comprises multiple independent subprojects or microservices (calculation engines, web services, data processors, websites) that must be deployed and initialized in a coordinated sequence, with explicit dependency declarations and network communication paths between.

Use when when building or modifying an asynchronous annotation pipeline that dispatches metadata enrichment requests to multiple heterogeneous web services and must verify that each service's HTTP calls succeed, response parsing is correct, and enriched fields are correctly merged into the output.

Use when you need to validate that Docker image builds for multiple deployment variants (e.g., cli, dev, linux, windows) meet documented compressed size ranges, or when you must audit storage footprint across a multi-target build pipeline without pre-computed metrics.

Use when you observe sawtooth or discontinuous peak profiles in EICs after running tardisPeaks() on LC-MS data acquired with multiple overlapping or sequential m/z scan windows (e.g., positive and negative polarity scans, or data-independent acquisition with staggered windows).

Use when you have split multi-assay LC-MS intensity data into training (90%) and test (10%) subsets with assay-specific column prefixes, and you need to fit a discriminant or regression model that respects the block structure (separate assays) while jointly predicting a phenotypic outcome (e.

Use when you have a peak-picked feature table (HDF5 format) from high-dimensional MS data (m/z, drift_time, retention_time, intensity) and need to identify and label isotopic signatures to distinguish monoisotopic peaks from isotopologues, reduce feature redundancy, and support multi-dimensional.

Use when after computing a pairwise sample distance matrix from aligned MS2 fingerprint vectors and you need to visualize sample relationships, clustering, or separation by group identity in 2D space.

Use when when you have computed raw p-values for multiple independent statistical tests (e.g., Pearson correlation tests across all pairwise ion combinations in MSI data) and need to report which results remain significant after accounting for multiple comparisons.

Use when you have Sciex Multiquant text export files from one or more metabolomics or lipidomics analytical sequences and need to identify and locate QCpool (pooled quality control) samples that should have been injected at regular intervals, or when you must validate that the study design's.

Use when you have a trained multitask model checkpoint and preprocessed spectral inputs (1D NMR spectra, 1H-only, 13C-only, or combined 1H+13C), and you need to generate simultaneous predictions of molecular formula and connectivity structure to quantify modality contributions, compare single vs..

Use when after peak filtering and normalization, when you have a peak-abundance matrix (samples × assigned molecular formulas) and need to visualize and test for differences in overall molecular composition across experimental conditions or sample groups.

Use when you have paired microbiome and metabolomic (or similar compositional) data with a new regression model and want to rigorously demonstrate its predictive advantage over alternatives (Elastic Net, Random Forest, CCA).

Use when when building or extending a mass spectrometry data parser that must support multiple mzML storage formats (plain .mzML, indexed .mzML.gz, standard-compressed .mzML.

Use when you have mzML-format mass spectrometry data files and need to load them into memory as structured data (pandas DataFrame) to prepare for visualization with pyOpenMS-Viz or other analysis pipelines.

Use when you have raw LCMS data in mzML format and a feature table (CSV) from a peak detection pipeline (e.g., MZmine) and need to prepare these inputs for NeatMS preprocessing, batch creation, or peak classification. This skill is the mandatory entry point for any NeatMS workflow.