Claude Skills by HolobiomicsLab

Use when when you have extracted low-resolution mass spectra from individual chromatographic peaks in GC-MS data and need to match them against a spectral library (e.g., PNNLMetV20191015.MSL) to identify the unknown compound.

Use when you have an unknown MS/MS spectrum (query spectrum with m/z and intensity pairs) and a reference spectral library (local or public: GNPS, MASSBANK, DrugBANK), and you need to identify the -matching compounds by ranking library entries by spectral similarity.

Use when when you have chemical annotations (GNPS matches) distributed across multiple sample groups (e.g., by sample type, extraction method, ionization source) with unequal numbers of files per group, and you need to compare enrichment fairly without group-size bias.

Use when you have CE-MS raw data in OnDiskMSnExp format with both positive and negative polarity acquisitions, migration times that vary due to electroosmotic flow drift, and access to two well-characterized mobility markers (e.g., Paracetamol and Procaine with known charges and migration times).

Use when when implementing or modifying a numerical compression/decompression component (e.g., Numpress for mass-spectrometry m/z and intensity arrays) and you need to verify that round-trip encoding and decoding preserves numerical fidelity.

Use when you have raw MS data files directly from a vendor instrument (Thermo .raw, Agilent .d, Waters .ms, etc.) and need to process them through AriumMS or any other metabolomics pipeline that accepts only .mzXML or .mzML formats.

Use when you have a raw MSI data file from an unknown or mixed set of vendors and need to apply format-specific data extraction, spectral parsing, or image reconstruction. The file extension alone must determine which parsing module (MSIGen.raw, MSIGen.D, MSIGen.baf, MSIGen.tdf, MSIGen.

Use when you have aligned MS/MS feature tables (e.g., from MSDial ver. 4.80) representing unknown metabolites suspected to be Phase I/II transformation products of xenobiotics, and you need to assign both chemical identity and biotransformation pathway context to each feature.

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 a task needs a skill from ASB Metabolomics — CE-MS — search this unit's 114 evidence-grounded skills, then apply and optionally ground the one that fits.

Use when after peak detection and MS1 feature extraction from FIA-MS, GC-MS, LC-MS(/MS), or CE-MS data, when you need to identify unknown metabolites by matching observed m/z values to a reference database and want to recover HMDB identifiers, molecular formulas, and structural annotations for.

Use when after MS1 feature detection and spectra merging in an untargeted or semi-targeted metabolomics workflow, when you have a list of observed accurate m/z values from high-resolution mass spectrometry (e.

Use when you have acquired the same sample(s) using multiple LC-MS, LC-IMS-MS, or direct infusion methods (e.

Use when adopting a mass spectrometry-based analysis tool (e.

Use when after accurate mass searching has assigned multiple detected m/z features to the same metabolite via positive and negative adduct libraries, and before sample-level feature merging.

Use when after molecular formula assignment from FT-ICR MS peaks and elemental composition tabulation (C, H, O, N, S, P counts), when you need to quantify the degree of aromaticity and carbon-skeleton unsaturation for each detected compound to support Van Krevelen classification, chemodiversity.

Use when when you have raw or processed direct-infusion MS (DI-MS) or ASAP-MS spectra as mz/intensity pairs and need to rapidly identify salient peaks for species authentication, sample scoring, or comparative profiling without manual inspection.

Use when you are introducing a novel spectrum prediction model and need to demonstrate that performance improvements come from architectural innovation rather than experimental advantage.

Use when after you have detected and assigned molecular formulas to peaks in a single FT-ICR MS sample, and you want to infer which biochemical or abiotic reactions are occurring by examining pairwise mass differences.

Use when after MS1 feature detection and accurate mass annotation, when you have identified a set of blank injections (negative controls) run in the same analytical sequence segment as your biological or study samples.

Use when after peaks have been filtered (by m/z, isotopic presence, and formula assignment error) and you have a list of peaks with assigned molecular formulas.

Use when you have mass spectrometry spectral data from multiple instrument types (e.g., direct infusion MS, ambient ionization MS, laser desorption/ionization MS) and need to perform unified species discrimination or database scoring across all samples regardless of their source instrument.

Use when when exporting quantified MSI data as HDF5 containers following the Cardinal::HDF5 layout convention, and you need to establish bidirectional indexing between intensity data (feature-by-pixel matrix) and metadata groups (featureData, pixelData).

Use when you have normalized peak-abundance matrices with sample metadata containing categorical treatment variables (e.

Use when you have pairs or triplets of MS/MS spectra with associated metadata (compound structural information, Tanimoto similarity scores) and want to learn embeddings that simultaneously preserve spectral similarity relationships and reconstruct peak intensities.

Use when when deploying Galaxy-M or similar multi-component metabolomics platforms that depend on heterogeneous runtime environments (Python, R, MATLAB, WINE) across multiple operating systems (Ubuntu 14.

Use when when you have LC-MS raw data and need to process it through a feature detection and quantification pipeline in KNIME, but lack a structured mapping between sample identifiers, experimental conditions, and the raw LC-MS runs.

Use when you have raw MS data (in Agilent .d, Thermo .raw, Bruker .

Use when you have feature lists in CSV format originating from different acquisition methods (e.

Use when after nontargeted peak detection and segmentation has generated a feature table from raw LC-MS data (mzML or vendor format), apply quality assessment when you need to rank or filter features by confidence before annotation, adduct grouping, or MS/MS matching.

Use when after completing feature detection, alignment, and optional filtering (blank subtraction, QC reproducibility, feature occurrence thresholds) in MZmine2 or Optimus, and you need to prepare the feature table and MS/MS spectra for GNPS-based molecular networking, bioassay integration, or.

Use when you have raw FIA-MS full-scan data in mzML format and need to prepare it for untargeted metabolite discovery. Apply this skill when your goal is to detect and annotate unknown metabolites across a wide m/z range (e.

Use when you have experimental MS/MS spectra matched against a reference library (via cosine similarity or dot-product scoring) and need to map individual fragment peaks in the experimental spectrum to their corresponding m/z and intensity values in the matched library entry.

Use when when you have extracted retention times from the top detected MS1 features in a LC-MS run and need to evaluate whether the gradient spreads those compounds efficiently across the available chromatographic time window—particularly during iterative gradient optimization where you need a.

Use when after XCMS feature detection, grouping, retention time correction, and missing value filling have produced an aligned feature matrix, when you need to group features (m/z, retention time pairs) that likely originate from the same metabolite.

Use when when you have experimental UHPLC-HRMS/MS or direct infusion MS/MS data and need to identify lipid species by comparing observed fragment m/z values against a library of simulated fragments. Apply this skill when your lipid library is incomplete or specialized (e.

Use when you have experimental peak lists (m/z, retention time, intensity) from UHPLC-HRMS/MS or direct infusion MS/MS data and need to assign lipid identities with confidence scores. Use it when your instrument produces high-resolution tandem mass spectra (e.

Use when when adopting a mass spectrometry data processing tool (e.g., LipidMatch) and needing to verify whether your specific instrument platform (vendor + model) and acquisition mode combination (targeted, ddMS2-topN, AIF, direct infusion, imaging) have been formally validated.

Use when when you have imzML mass spectrometry imaging data files and need to convert raw ion image intensities into quantitative lipid abundance (pmol/mm²) using known internal standards.

Use when you have raw LC-MS data (mzML, NetCDF) from multiple runs that require sequential feature detection, alignment, quantification, and optional filtering (e.g., blank exclusion, QC reproducibility, retention-time outlier removal) before spatial mapping or annotation.

Use when when beginning an untargeted LC-MS metabolomics study and need to assemble a cohort of mzML files for processing; particularly when establishing performance baselines across sample counts (10, 50, 100+ samples), validating reproducibility, or preparing data for publication.

Use when you have peak-picked MS/MS data (e.g., from MZmine, XCMS, MS-DIAL, or Compound Discoverer) and need to identify lipid species present in your sample.

Use when after quantifying ion images in LipidQMap and before exporting to HDF5 format, when you need to organize per-feature metadata (lipid ID, class, adduct, m/z, internal standard flag) into aligned datasets that can be linked to intensity data via dimension scales and sorted for reproducible.

Use when you have MS/MS spectra with initial lipid annotations from spectral library matching (e.g., from XCMS + CAMERA or LipidIN's Expeditious Querying module) and seek to improve recall, precision, and annotation coverage.

Use when you have peak-picked LC-HRMS/MS or direct infusion MS/MS data (m/z, retention time, intensity) from Q-Exactive, Agilent, Bruker, or SCIEX instruments and need to annotate experimental fragment patterns to known lipid structures.

Use when you have raw or processed MS spectrum data (m/z and intensity pairs) from DI-MS, ASAP-MS, or other high-throughput mass spectrometry instruments that requires automated peak detection.

Use when when processing GC–MS or LC–MS data as m/z vs retention time chromatograms and you need to identify biomarker or chemical marker features without conventional peak picking, particularly when false positive detection rates from peak detection algorithms are problematic.

Use when you have a preprocessed peak list (m/z values and assigned molecular formulas) from direct injection FT-ICR MS of a complex organic mixture (e.

Use when you receive raw MS data files from LC-MS, LC-IMS-MS, direct infusion, or DDA/DIA experiments and need to extract ion chromatograms, mobility heatmaps, quality metrics, or perform spectral matching.

Use when you have raw MS data files from supported instruments (Agilent, Thermo, Bruker, or mzML format) and need to ingest them into IonToolPack for visualization, quality control, targeted extraction, or spectral library matching.