Claude Skills by HolobiomicsLab

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 Spectra-based MS analysis workflow in R but need access to specialized spectral similarity, filtering, or normalization algorithms available only in Python libraries.

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 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 a processed mass spectrum object with assigned molecular formulas for individual peaks and need to summarize peaks by their heteroatom composition—e.

Use when after importing and preprocessing mass spectrometry data (in .raw, .

Use when you have an unknown mass spectrum (as m/z and intensity arrays) and need to identify candidate compounds by matching against a mass spectral library.

Use when when you have a full mass spectrum scan (e.g., FT1 or FT2 scan from an Orbitrap or IonTrap instrument) and need to isolate a narrow m/z window containing a known or predicted precursor ion before matching it to theoretical isotopic envelopes.

Use when when you have one or more peptide sequences (as strings) and need to predict their theoretical isotopic distribution patterns to compare against experimental MS data, validate peak assignments, or generate synthetic spectra for method development.

Use when after loading MS-Dial feature tables (e.g., Urine_RP_NEG_norm.txt or Urine_RP_POS_norm.txt) and before sample-level filtering or imputation, whenever the feature abundance matrix contains m/z values acquired across multiple chromatographic runs or polarities.

Use when after calculating neutral mass from observed m/z and adduct type, and before ranking candidates by chemical plausibility. Use it whenever querying a formula database (KEGG, PubChem, or custom) to retrieve all molecular formulae within a specified mass tolerance window of each neutral mass.

Use when your pandas DataFrame contains mass spectrometry data with retention time (rt) and intensity columns AND a column representing different mass-to-charge (m/z) values or ion identifiers. This is particularly relevant when generating chromatogram plots from data with multiple mass traces (e.

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 rendering spectrum data (m/z vs. intensity arrays) from MZA files and need to control visual presentation: applying m/z range windows, setting line colors and labels for legend identification, sizing the figure, or choosing between interactive display versus file export.

Use when you have mass spectrometry data in a pandas DataFrame with retention time (rt) and intensity columns, and need to produce a static figure for publication, presentation, or archival.

Use when when you need to subset a backend containing multiple MS spectra to a user-defined subset (e.

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 you have a raw or extracted peak feature table (CSV or tabular format) containing mass-to-charge ratios, retention times, and intensity values across multiple samples from different experimental groups, and you need to identify which peaks show statistically significant differential.

Use when after batch effect removal and sample integration, when you have a normalized feature-by-sample abundance matrix (finalData) with corresponding sample group labels (finalLabel), and need to identify which metabolites discriminate between biological conditions or phenotypes for focused.

Use when you have normalized peak intensities using MetaboDirect's data preprocessing step and are preparing to perform PERMANOVA or NMDS ordination on a bacterium-phage or environmental metabolomics dataset (36+ samples).

Use when after loading a raw metabolite abundance table (rows=metabolites, columns=samples) from Metabolomics Workbench format and before mapping metabolites to pathway identifiers or computing enrichment statistics.

Use when you have a peak-abundance matrix from FT-ICR MS (peaks as rows, samples as columns with raw peak intensities) and need to compute abundance-based diversity indices or functional diversity metrics that are sensitive to relative vs. absolute peak heights.

Use when after running in silico annotation tools (SIRIUS, ISDB) or spectral library matching on your feature table, when you need to retain only annotations meeting a minimum confidence threshold.

Use when after mzRAPP has exported a benchmark CSV file from processing a batch of centroided mzML files (e.

Use when you have: (1) a set of predicted candidate metabolites with known mass-to-charge ratios and chemical properties derived from a parent drug formula; (2) raw mass spectrometry data in mzML format from a sample suspected to contain those metabolites;

Use when you have a query mass spectrum matched to multiple candidate metabolites (by accurate mass, database lookup, or spectral similarity), and you possess or can train a DNN model for retention time prediction on your target chromatographic method.

Use when you have peak-abundance data (after molecular formula assignment, peak filtering by m/z, isotope, ppm error, and sample presence thresholds) and you need to quantify and compare the molecular composition diversity across samples or conditions.

Use when after running do.findmain on a RAMClustR-clustered object to infer molecular weights and assign features to compound clusters, when you need to conduct structural elucidation in MSFinder or Sirius and require spectra in their native import formats rather than the intermediate MSP or.

Use when you have normalized peak intensity tables from FT-ICR MS data (e.g., MetaboDirect .

Use when when you have mzML files from targeted or untargeted metabolomics experiments run in multiple ionization modes (e.g., KO_NEG, KO_POS, STD_NEG, STD_POS, WT_NEG, WT_POS) and a CSV list of reference compounds (e.

Use when you have raw or baseline-corrected metabolite abundance measurements from mass spectrometry and need to prepare them for batch effect correction (e.g., CordBat) or multivariate analysis (e.g., PCA).

Use when you have metabolomics metadata in mwTab or tabular format with column headers and values that may contain database identifiers (e.g., HMDB IDs, PubChem CIDs, KEGG compound IDs) in heterogeneous or non-canonical formats (mixed case, optional prefixes, variable naming conventions).

Use when you need to construct a reference metabolomics database from scratch or when existing public databases (HMDB, MassBank, METLIN) need to be merged into a single queryable resource for metabolite annotation in untargeted mass spectrometry analysis.

Use when you have normalized peak intensity tables from FT-ICR MS data (or MetaboDirect pre-processed .csv output) with samples grouped by experimental treatments (e.

Use when after importing raw metabolomics data (e.g., from Metabolon, Nightingale, SomaLogic, or Olink platforms) into a Metaboprep object, but before quality control or statistical analysis.

Use when you have (1) a benchmark dataset of known molecules with accurate m/z values, retention time boundaries, and isotopologue identifiers for all enviPat-predicted adducts;

Use when when you have a known drug's chemical formula and need to generate a comprehensive list of predicted metabolite formulas to match against experimental mzML mass spectrometry data.

Use when when you have raw mass spectrometry peak intensity data (rows = peaks with IDs, columns = individual samples) and you need to align it with metabolite annotations (peak ID → KEGG or ChEBI compound ID mappings) before performing pathway-level analysis.

Use when you have an annotated list of metabolite compounds (with associated m/z features or compound IDs) and want to determine which KEGG metabolic pathways are significantly enriched or depleted in your experimental samples.

Use when after peak detection has identified significant m/z and retention time features in untargeted or targeted mass spectrometry data (as a .raw, .d, or mzXML file).

Use when you have paired microbiome (genus-level 16S or functional profiles) and metabolome datasets (e.g., mass spectrometry or metabolomics panels) with 100+ samples, and you want to demonstrate that a new prediction method outperforms prior work.

Use when you have an unknown compound's mass spectrum (m/z peaks and intensities) in .

Use when you have a raw LC–MS compound metadata file (xlsx or csv) with heterogeneous column names and column order, and you need to prepare it for targeted peak detection in TARDIS.

Use when you have raw or minimally processed FT-ICR MS peak tables in Formularity .

Use when when a preprocessed metabolomic feature table (e.g., MS-Dial output) retains features and samples that passed filtering for missingness thresholds and m/z validity, but still contain scattered missing values (NA). This skill is appropriate after sample-level filtering (e.

Use when you have multiple metabolomic studies with aggregate summary statistics (p-values, fold-change estimates) and need to perform meta-analysis while harmonizing compound nomenclature across datasets. Use this skill when the underlying R package (e.

Use when after peak detection in untargeted LC/HRMS workflows, when you have a list of candidate peaks with signal intensity profiles and need to filter them according to data quality thresholds (signal-to-noise ratio, peak width, baseline separation, and isotopic pairing consistency) before.

Use when when you have raw or partially processed metabolomics data (feature tables with sample metadata) and need to apply quality-control metrics, normalization, statistical inference, or advanced classification/variable selection without relying on a Galaxy instance.

Use when you have raw or semi-processed m/z peak lists (positive and negative ion mode) and a sample metadata table, and you need to confirm they meet MetaboShiny's structural and semantic requirements before loading them into the normalization pipeline.