Claude Skills by HolobiomicsLab

Use when after initial lipid candidate annotation via spectral library matching (e.g., from XCMS/CAMERA peak alignment and LipidIN EQ module querying), when you need to improve coverage and annotation confidence on unannotated or low-confidence lipid signals.

Use when when you have pre-processed MS/MS spectra and a pre-trained Word2Vec model, and need to compute fast, scalable similarity scores for library matching or molecular networking that correlate better with structural similarity than cosine-based methods.

Use when you have a large collection of preprocessed MS/MS spectra (typically >10,000 spectra) with diverse chemical structures and you need to learn embeddings that capture fragmentation patterns and neutral loss relationships.

Use when when applying a pre-trained Word2Vec model to mass spectra at inference time (e.g., library matching or molecular networking), especially when the query spectra may contain fragment peaks or neutral losses not represented in the model's training vocabulary.

Use when converting MS/MS spectra into Spec2Vec embeddings using a pre-trained Word2Vec model that was trained on reference data (e.g., a subset of GNPS or MassBank).

Use when you need to understand how a complex MS/MS spectral search system routes query spectra through multiple parallel processing pipelines with different objectives (e.g., reliable exact matching vs. fast approximate matching).

Use when when a spectral matching tool produces mixed output containing both exact library matches and analog search results, and your analysis or publication requires separate handling, interpretation, or reporting of these two match classes.

Use when when preparing to execute the Nextflow4MS-DIAL workflow on raw LC-HRMS metabolomics data (.mzML or .

Use when after executing a Nextflow-based MS-DIAL workflow on .mzML LC-HRMS metabolomics data using Docker or Singularity container backends.

Use when you have prepared metabolomics input files (feature quantification table, MS/MS spectra in MGF format, sample metadata) and are about to execute the TIMA taxonomically informed annotation pipeline, or after pipeline execution to verify all outputs were generated correctly.

Use when after implementing or deploying a containerized Nextflow workflow that processes LC-HRMS metabolomics .

Use when when you have raw mass spectrometry data files from multiple acquisition modalities (LC-MS, GC-MS, ion mobility, or imaging) and need to automatically route each to the correct downstream analysis module without manual intervention.

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 you have raw mzXML LC/MS files from replicated metabolomics experiments (e.g., 12 samples across labeled/unlabeled conditions) and need to extract, align, and group peaks before downstream feature filtering (e.g., fold-change or isotope enrichment analysis).

Use when you have preprocessed LC-MS data with detected chromatographic peaks that need to be consolidated into feature groups representing putative compounds.

Use when xCMS grouping has been performed on LC-MS data from studies with hundreds of samples or data acquisition periods longer than a week, where retention time drift structures are complex and the single-warping-function assumption is likely violated.

Use when you have raw gas or liquid chromatography–mass spectrometry data (in NetCDF or mzML format) and need to detect features, align them across samples by retention time and mass-to-charge ratio, correct for retention time drift, and fill missing values before downstream metabolite clustering.

Use when xCMS has produced aligned LC-MS features but alignment quality is suspected to be poor—especially when analyzing hundreds of samples, data acquired over extended periods (>1 week), or when individual m/z bins show inconsistent RT shifts.

Use when when you have direct-injection or low-complexity mass spectrometry data (mzML files) and need to detect chromatographic peaks using wavelet-based methods instead of centWave, especially when standard retention-time-dependent peak detection is not suitable or when you need to tune.

Use when you have xcms-processed LC-MS data with detected feature groups (from xcms grouping), suspect retention time misalignment across samples due to long acquisition periods or large sample cohorts, and need to feed raw profiles into ncGTW's realignment algorithm.

Use when when you have raw LC-MS all-ion fragmentation (AIF) chromatograms in centroid mode and need to prepare them for metabolite annotation using fragment ion matching.

Use when you have centroid-mode LC–MS all-ion fragmentation (AIF) data already processed through xcms for feature detection and retention-time correction, and a corresponding RamClustR object that groups co-eluting fragment ions into putative spectral clusters.

Use when you have raw LC-MS data files (mzML, netCDF, or raw vendor formats) from multiple samples and need to extract, align, and quantify chromatographic features across the cohort.

Use when when you have a resolved spectrum file (mzML, mzXML) and need to visualize where MS2 precursor scans occur on an XIC display.

Use when when you have synthesized or assembled mass spectrometry spectral data (m/z values, intensities, retention times) and need to encode it as a portable, standard mzML file format rather than a proprietary binary or text representation.

Use when you have extracted a centroided MS/MS spectrum from a Thermo Orbitrap raw file (via rawrr::readSpectrum or equivalent) and need to verify that the observed y-ion fragments for a known peptide precursor exhibit signal-to-noise ratios consistent with high-quality fragmentation.

Use when when initializing a MolNotator project with user-defined parameters for ionization modes, adduct tables, database selections, output directories, and tool-specific thresholds.

Use when after feature detection has produced a feature table with zero and missing values (sparse abundance matrix) but before multivariate statistical analysis or annotation.

Use when a task needs a skill from ASB Metabolomics — LC-MS — search this unit's 2,621 evidence-grounded skills, then apply and optionally ground the one that fits.

Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.

Use when you have a tokenized sequence of domain-specific language tokens and need to construct a hierarchical, unambiguous representation that can be validated against language design principles (expressiveness, precision, scalability, readability) and passed to downstream execution engines.

Use when when you have a domain-specific query language (such as MassQL for mass spectrometry) and need to convert query strings into structured representations that preserve domain constraints (e.g., mass tolerance, scan type, intensity thresholds).

Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.

Use when after computing PLAGE-derived activity scores for pathways or metabolite sets (Molecular Families, Mass2Motifs) from log2-standardized metabolomics intensity data.

Use when you have a set of in silico-predicted compounds (with SMILES structures) and an experimental metabolomics peak list (m/z values), and you need to filter predictions to only those that could plausibly be detected.

Use when when you have a list of target molecules with known molecular formulas and need to extract and validate their peaks from centroided mzML files, or when assessing whether detected peaks in a mass spectrometry run correspond to expected adducts of known metabolites.

Use when you have computed a histogram of mass differences from all pairwise mass comparisons in your MALDI-MS imaging dataset and need to prioritize which mass differences are most frequent and likely represent genuine molecular adducts (e.g., metabolite + matrix ions) rather than noise.

Use when when you have extracted feature tables from multiple breath samples (mzML/mzXML files) using feature extraction, and you need to identify which features are the same across samples to enable downstream statistical or comparative analysis.

Use when you are evaluating a new or existing data analysis pipeline (e.g., MetaboDirect) and need to produce a transparent, evidence-based feature matrix showing which analyses it supports—particularly when the pipeline is positioned as an alternative to or improvement over established tools.

Use when after generating TP candidates from annotation algorithms (ann_comp for structure-based, ann_form for formula-based), when you need to filter and rank candidates to focus on the most credible identifications.

Use when you have run MSMetaEnhancer's annotate_spectra() method on a .

Use when when you have access to multiple public metabolomics databases and need to build a unified reference table for metabolite annotation in untargeted mass spectrometry analysis.

Use when you have mzPeak files stored as Parquet tables within a ZIP archive and need to load spectrum metadata, chromatogram metadata, signal data (profile or centroid), or peaks into memory for analysis in R, Python, or another Arrow-supported language.

Use when you have fingerprint or spectrum data that requires compound-class annotation but prefer not to run SIRIUS locally, or need to integrate predictions into an automated analysis pipeline.

Use when when you have a large spectral library (hundreds of thousands to millions of spectra) and need to perform open modification searching on query spectra where exhaustive comparison against all library entries is computationally prohibitive.

Use when when you have a large spectral library (hundreds of thousands or millions of spectra) and need to search unknown query spectra against it for both unmodified and post-translationally modified peptides.

Use when when reading mzPeak files or other Parquet-backed mass spectrometry archives where spectral m/z and intensity arrays are stored in columnar layouts (point or chunked format) and you need to reconstruct them into Python NumPy arrays, pandas DataFrames, or equivalent in-memory structures for.

Use when you have loaded mzPeak spectrum or chromatogram metadata and signal data into PyArrow Table structures (via the Python mzPeak reader or equivalent) and need to persist them to disk in Parquet format for downstream analysis, interoperability with other languages (R, .

Use when after AutoTuner has completed EICparams extraction and parameter estimation on raw untargeted metabolomics data (mzML, mzXML, or CDF format), and you need to pass those estimates into XCMS or MZmine2 for full dataset processing.

Use when you have mass spectrometry data loaded into a pandas DataFrame with m/z, retention time, and intensity columns, and need to confirm that pyOpenMS-Viz can produce visualizations (spectra, chromatograms, or peak maps) using a specific plotting backend (matplotlib, Bokeh, or Plotly) on real.