Claude Skills by HolobiomicsLab

Use when you have intracellular metabolomics concentration measurements across multiple cell lines or conditions, a stoichiometric metabolic network model with reaction-metabolite associations, and you need to predict how differences in substrate availability (independent of enzyme expression).

Use when you have extracted and intensity-normalized fragment ion masses and neutral loss values from MS/MS spectra and need to prepare them for unsupervised topic modeling to discover recurring fragmentation motifs.

Use when when processing raw FT-ICR transient data (e.g., ESI_NEG_SRFA.d) that requires assignment of molecular formulas to experimental m/z peaks. Calibration is necessary before SearchMolecularFormulas because uncalibrated mass error will cause false formula rejections or incorrect assignments.

Use when after successfully matching at least 5 reference m/z points (from a .ref file) to spectrum peaks within a PPM error window (starting at ±1.0 ppm and widened iteratively to ±1.5, ±3, ±5, ±7, or ±10 ppm if needed).

Use when after loading centroided .mzML LC-MS data and creating a target list with compound ID, name, theoretical or measured m/z, expected RT (in minutes), and polarity designation, perform this validation step to confirm target visibility and refine m/z and RT window parameters before running.

Use when after chromatographic peak detection on preprocessed LC-MS data, when you have detected features (peaks) in multiple samples and need to establish which peaks across samples represent the same molecular species.

Use when processing feature lists from LC- or GC-HRMS data (in mzML format or as custom feature tables with m/z and molecular formula columns) and you need to flag potential PFAS candidates.

Use when you have a list of detected masses (m/z peaks) from MALDI-MS imaging data and want to systematically search for adduct relationships. Apply this skill when you suspect that observed peaks include not just parent metabolites but also their adducts with matrix ions (e.

Use when you have a filtered peak list (CSV with m/z values and assigned molecular formulas) from FT-ICR MS and want to infer biochemical transformations occurring in microbial or environmental samples.

Use when after importing MSI data as an msimat object and having a list of detected peak masses, but before annotating which mass differences correspond to biologically plausible adducts.

Use when after peak picking and sample alignment when you have an aligned feature table containing m/z and retention time coordinates. Use it when your untargeted LC-MS workflow needs to reduce feature redundancy caused by naturally occurring stable isotope patterns and common adduct formation.

Use when after molecular formula assignment has been performed on calibrated m/z values. Apply this skill when you need to quantify the accuracy of formula-to-peak matching, validate mass calibration performance against reference standards (e.g., SRFA.

Use when when annotating observed mass spectrometry peaks against theoretical fragment ions (b, y, or other ion types) using ProForma 2.0 peptidoforms, compute the m/z deviation for each matched peak to verify that the annotation adheres to your specified mass tolerance (e.g., ±10 ppm or ±0.

Use when after LDA modeling has produced an inferred motifset (JSON format) containing Mass2Motifs with fragment and neutral-loss patterns.

Use when after mass track extraction from individual LC-MS samples, when you need to align mass tracks across a cohort to produce a unified feature matrix. Specifically: when study size is ≤10 samples, use pairwise anchor-prioritized alignment;

Use when when generating a virtual chemical mixture for LC-MS/MS simulation, or when sampling molecular formulas from a metabolite database (such as HMDB), you need to restrict the sample to a specific m/z window that matches your instrument's acquisition range or your analytical focus.

Use when you have raw LC-MS/MS spectral data in vendor formats or unvalidated .mgf files before feeding them into the specXplore importing pipeline.

Use when you have untargeted metabolomics MS/MS spectra from multiple features and need to identify which features belong to the same molecular family or are related by biotransformation.

Use when you have unaligned MS2 spectra from one or more samples (in formats like .mgf, .mzML, or .mzXML) and need to compare them in a retention-time-agnostic manner.

Use when you have multiple mass spectral libraries in different formats (NIST MSP + MOL folder, MoNA MSP, RIKEN MSP, SWGDRUG MSP) and need to merge them into a single, MS-DIAL-compatible MSP file with consistent SMILES assignments, Kovats retention indices (RI), and polarity annotations across all.

Use when when applying a pre-trained Spec2Vec Word2Vec model to new mass spectra (particularly those outside the model's training distribution), you need to assess whether peaks and neutral losses in query spectra have been seen during model training.

Use when you have a GNPS-generated molecular network (graphml or JSON format) and corresponding MS2LDA experiment results or chemical class assignments, and you want to annotate network nodes with substructural motifs or chemical classes to facilitate structural interpretation and identify.

Use when you have centroided MS2 spectra (in mzML format from data-dependent acquisition) and a list of known or suspect PFAS diagnostic fragment masses, and you need to systematically flag which detected features contain fragments characteristic of PFAS compounds (e.

Use when you have predicted structural similarity scores (e.g., Tanimoto or Dice scores) for a large set of spectrum pairs and need to assess prediction accuracy across the full range of possible similarities. Critical when evaluating whether uncertainty filtering (e.

Use when when you have a list of chemical compounds (with m/z values, retention times, and intensities) and need to simulate their acquisition behavior under a specific ionization polarity and mass spectrometer configuration.

Use when you have preprocessed MS/MS spectra with measured precursor m/z values but the ionization adduct type is unknown or ambiguous—particularly in metabolomics workflows where multiple adducts co-occur or in de novo formula annotation without access to curated spectral databases.

Use when you have implemented or modified a tandem mass spectrometry formula inference model and need to measure whether a specific architectural change (e.

Use when when claiming that one mass spectrometry processing library achieves higher throughput than competitors, or when evaluating whether a new or optimized implementation delivers the expected computational efficiency gains.

Use when you have a combined EI library (from multiple sources such as NIST, RIKEN, MoNA) and access to NIST RI database files (ri.dat and USER.

Use when you have a preprocessed GC-MS dataset (from spreadOut) with standardized column names (Compound.Name, Component.RT, Base.Peak.MZ, Component.Area, Match.Factor) and a specific list of chemical compounds you want to extract and aggregate across multiple sample runs.

Use when you have generated a peak table or feature list from MZmine, XCMS, MS-DIAL, or Compound Discoverer in its native export format and need to ingest it into LipidMatch for lipid identification.

Use when when you have processed LC-MS/MS spectral data in .mgf format with precomputed ms2deepscore similarity matrices and need a 2-D overview representation that preserves local spectral relationships for interactive exploration and visualization.

Use when after peak picking, sample alignment, and isotopologue/adduct grouping steps have been completed in an untargeted LC-MS workflow.

Use when you have raw mass spectrometry instrument output (mzML, vendor binary formats, or mzPeak archives) and need to load spectrum metadata, chromatogram data, or signal arrays into memory for quality control, format conversion, or statistical analysis.

Use when when beginning a new mass spectrometry analysis workflow with raw spectral data files in mzML, mzXML, msp, MGF, or JSON format.

Use when you have multiple mzML or HDF5 feature tables from the same study acquired on the same or similar instruments and need to align feature coordinates across samples to correct for systematic shifts in mass-to-charge, drift time, or retention time caused by instrumental drift, column aging.

Use when you have raw or curated mass spectrometry data (MS1, MS2, or MSMS) in mzML, mzXML, CDF, MGF, MSP formats, or from a MassBank/MetaboLights repository, and need to convert it into an in-memory or on-disk spectral object that supports filtering, comparison, and annotation workflows.

Use when you have raw mass spectrometry data in mzML or Bruker .d format and need to ingest it into a tabular format (pandas DataFrame) for visualization, statistical analysis, or integration with other Python-based mass spectrometry tools.

Use when you have raw mass spectrometry files in standard formats (mzML, mzXML, msp, MGF, JSON) and need to extract precursor m/z values, fragment peaks, neutral losses, retention times, and compound metadata into a structured, queryable spectrum object representation before performing MS/MS.

Use when you have raw mass spectra or processed feature matrices from liquid chromatography–mass spectrometry (LC-MS) or direct infusion MS that must be ingested by a deep learning model for substance identification.

Use when you have raw mass spectrometry outputs (peak areas/heights across samples and fragmentation spectra) that need to be formatted and validated before running the tima taxonomically informed annotation workflow.

Use when you need to store or retrieve mass spectrometry spectra (m/z and intensity pairs) from a novel data source or storage medium (e.

Use when you have received an mzPeak archive (a ZIP file containing Parquet tables) and need to understand its internal structure, validate that spectrum metadata aligns with signal data, reconstruct m/z and intensity arrays (especially when null marking or zero-run stripping is present), or verify.

Use when when you have raw or processed TWIM-MS data (arrival time and m/z values) from a mass spectrometry instrument and need to organize it into a feature table before biomolecular class assignment or CCS calculations.

Use when when you have mass spectrometry data (mzML, Bruker .d, or CSV) loaded into a Pandas DataFrame with columns for m/z, retention time, ion mobility, or intensity values, and you need to render spectrum plots, chromatograms, mobilograms, or 2D peak maps.

Use when after applying retention time, abundance correlation, or EIC similarity-based feature grouping (e.g., via SimilarRtimeParam, AbundanceSimilarityParam, or EicSimilarityParam). Use when you need to visually confirm that grouped features belong to the same compound—i.

Use when you have vendor-independent centroided mzML files from LC- or GC-HRMS data acquired in data-dependent acquisition (ddMS2) mode and need to extract detected features with m/z, retention time, and intensity attributes as input for non-target screening or PFAS prioritization workflows.

Use when after running qiime qemistree make-hierarchy and obtaining a tree artifact (qemistree.

Use when when you have Thermo Fisher Scientific Orbitrap .raw files (e.g., from Q Exactive HF instruments) and need to extract spectral, chromatographic, or metadata directly into R for downstream statistical analysis, benchmarking, or integration with Bioconductor workflows.

Use when you have raw MS/MS spectral data in one or more standard mass spectrometry file formats (.mgf, .msp, or .mzML) and need to convert them into a standardized bag-of-fragments representation for unsupervised topic modeling or substructure discovery workflows.