Claude Skills by HolobiomicsLab

Use when after NPFimg's automated detection algorithm has identified marker features from a two-dimensional MS map (m/z vs retention time), especially when you need to validate feature positions, inspect co-localization patterns, or communicate results to stakeholders.

Use when when you have generated in silico annotations (from GNPS ISDB, SIRIUS, or timaR) and need to distinguish true matches from false positives by enforcing a mass accuracy constraint. Apply this skill before computing novelty metrics (e.

Use when after implementing or modifying an mzML parser module that converts mzML files into MS-DIAL's internal data model, and before integrating the parser into the production analysis pipeline.

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 correlation-based feature pairing has identified feature groups with matching temporal intensity profiles through direct-injection or plasma ionization mass spectrometry experiments. You have a set of putative feature-pair candidates and need to assign specific chemical identities (e.

Use when you have a formula-assigned DOM dataset from FT-ICR MS (or other mass spectrometry with compound annotations) and need to characterize how molecular transformations occur across the sample—e.

Use when after peak picking and before or after molecular formula assignment on FT-ICR data, especially when calibrating on a specific field strength (e.g., 12 T or 15 T).

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 after applying polynomial m/z recalibration using a reference peak list (e.g., SRFA.ref) to an FT-ICR mass spectrum.

Use when you have isolated TIC peak regions and need to extract ion chromatograms (EICs) for XCMS parameter estimation via the EICparams function. Use it specifically when your mass analyzer's measurement accuracy is known (e.

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

Use when when you have constructed a MassGrid (m/z-aligned mass tracks across multiple samples) and need to retrieve all sample-specific mass tracks for a given m/z value in order to sum their intensities, apply retention time calibration, or construct composite mass track objects for peak.

Use when you have a query mass spectrum of unknown modification status and need to search a spectral library to identify the peptide and its post-translational modifications.

Use when when searching for peptide spectra with unknown or open modifications (i.e., any mass shift within a broad tolerance range rather than a fixed set of known modifications).

Use when when you have paired mass spectra and molecular structure data and need to train a unified model for structure elucidation. Use this skill at the data preparation stage before pretraining, when you want both modalities to share representational capacity rather than operate in isolation.

Use when you have access to the Flash Entropy Search implementation and want to validate that real-time spectral library querying achieves the reported timing and throughput metrics under the same library sizes, query parameters, and measurement methodology documented in the paper.

Use when when processing in-silico or experimental MS spectra records from databases with incomplete metadata, specifically when the adduct field is null or absent but the ionmode field (positive/negative polarity) is present.

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 after peak detection when you have a detected peaks table with m/z values and need to correct systematic mass drift or inaccuracy before peak alignment across multiple batches.

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 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 when you have mass spectrometry data stored in non-standard formats (SQLite, HDF5, custom binary) that pymzML does not natively support, and you want to enable random-access and sequential parsing of spectra without converting to mzML first.

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 when raw mass spectrometry spectral data has been imported into matchms from common file formats (mzML, mzXML, msp, MGF, JSON) and you need to assess whether metadata fields are correctly normalized, validated against schema requirements, and peaks are appropriately filtered before.

Use when after RAMClustR clustering and molecular weight inference via do.findmain, when you need to export deconvoluted cluster spectra for import into external annotation tools (MSFinder or Sirius).

Use when when you have received metabolomics mass-spectrometry data in vendor-native or open formats (.raw, .d, mzXML) and need to ingest it into SMART for preprocessing, peak detection, or statistical analysis.

Use when when you have a mzPeak file (uncompressed ZIP archive containing Parquet tables) and need to access decoded spectral data arrays (m/z, intensity), spectrum metadata (scan descriptors, precursors), or chromatogram data in a form suitable for Python/R analysis pipelines.

Use when you have a formula-assigned FT-ICR MS dataset (CSV or tab-delimited table with molecular formulas and mass values) and need to quantify molecular chemodiversity, characterize elemental stoichiometry patterns, or explore transformation pathways across samples.

Use when after XCMS CentWave feature extraction when your feature table contains redundant entries — multiple features with highly similar m/z and retention time values that represent the same metabolite.

Use when when you have processed LC-HRMS mzML files through a non-targeted preprocessing tool (e.g., XCMS, MZmine 2, MS-DIAL) and need to assess whether peak detection rates, isotopologue ratio fidelity, and alignment accuracy meet acceptable thresholds before downstream analysis.

Use when you have raw mzML mass spectrometry data and need to detect which predicted candidate metabolites are present in the sample.

Use when after aligning raw mass spectra into a 2D feature intensity matrix (features × time points) but before computing pointwise correlation to identify feature pairs with matching temporal profiles.

Use when when you have mass spectrometry spectra in one of the six supported formats (mzML, mzXML, msp, metabolomics-USI, MGF, JSON) and need to convert them to a different format for compatibility with downstream tools, or when importing raw spectra into matchms for the first time and need to.

Use when mS quantification data exhibits intensity drift—a systematic decline or variation in detector response across the run sequence. Intensity drift is particularly common in long measurement sessions and compromises the accuracy of feature-by-sample intensity matrices.

Use when when extracting and validating chromatographic peaks for target molecules from centroided mzML files, particularly when working with multiple isotopologues and adducts where mass tolerance directly affects whether predicted isotopologues are correctly grouped and detected as part of the.

Use when you have a Thermo Fisher Orbitrap .raw file and need to programmatically inspect or validate its acquisition parameters (instrument type, total scan count, acquisition duration, file name) before extracting spectral data, or when you need to serialize metadata to JSON for data provenance.

Use when you have raw or processed FT-ICR MS spectra with detected peaks (m/z values) and need to: (1) assign elemental compositions to each peak, (2) filter assignments by mass error tolerance and isotopic presence, or (3) prepare a peak table with molecular formula annotations for chemodiversity.

Use when when you have raw or parsed MS spectra data (m/z values, intensity values, and spectral metadata) and need to integrate it into a Spectra object for downstream processing.

Use when you have raw high-resolution mass-spectrometry data in mzML or mzXML format from breath analysis and need to identify individual volatile organic compounds before sample-to-sample alignment or comparative analysis.

Use when you have a Pandas DataFrame containing mass spectrometry data (e.

Use when when processing centroided .mzML LC–MS runs with a multi-polarity target list (i.e., some targets ionize in positive mode, others in negative mode, or both) and you need to detect peaks and extract ion chromatograms without manually subsetting the raw data by polarity beforehand.

Use when when you have raw mass-spectrometry data (precursor m/z, ionization mode, and fragment m/z–intensity pairs) that must be fed into a CNN model for metabolite annotation via compound fingerprint prediction.

Use when you have raw LipidSearch or LIQUID output files (CSV or TSV format) containing lipid identifiers and per-sample quantification measurements, and you need to convert them into a machine-readable data matrix for downstream statistical analysis, normalization, or differential abundance.

Use when you have measured MS intensity data from unknown samples and known-concentration standard compounds, and you need to convert sample intensities into predicted concentrations. Specifically applicable when intensity drift is observed across the measurement sequence (e.

Use when when you need to express complex mass spectrometry search patterns (e.g., isotope envelope detection, neutral loss patterns, intensity relationships across m/z ranges) in a human-readable format that can be executed against spectral data files (mzML, mzXML) or spectral repositories.

Use when you have raw MS data files from Thermo Orbitrap or other vendor instruments that must be uploaded into a centralized platform for automated processing. Use it at the start of a multi-stage omics workflow where data collection, management, processing, and visualization are integrated;

Use when you have raw mass spectrometry spectra from an unknown analyte or a synthetic compound library and need to feed them into PS2MS or similar deep learning classifiers for NPS detection.

Use when when you have raw LC–QTOF wastewater spectra (or other real experimental mass spectrometry data) and need to feed them into a pretrained MSGO model for molecular structure prediction.

Use when you have a raw .xlsx or tabular file listing candidate compounds with theoretical or measured m/z values, expected retention times, and ionization polarities, and you need to feed this into TARDIS or another targeted LC–MS analysis pipeline.