Claude Skills by HolobiomicsLab

Use when you have raw LC-MS/MS data in MRM acquisition mode and need to systematically identify and catalog all precursor m/z and corresponding product m/z values for each transition monitored during data collection.

Use when after parsing raw MRM data into a transition table containing m/z values, retention times, and transition parameters.

Use when when processing LC-MS peak tables from isotope tracing experiments where multiple ionization adducts ([M+H]+, [M+Na]+, [M+NH4]+, etc.) and in-source fragments have generated redundant features at different m/z values that represent the same underlying metabolite.

Use when after running annotateRC on LC–MS AIF features with fragment libraries (e.

Use when when receiving raw MS data files of unknown or mixed acquisition modalities and needing to route each to its corresponding analysis pipeline. Specifically, apply this skill when: (1) input files arrive without documented instrument type or chromatographic/mobility dimensionality;

Use when you have received raw CE-MS or LC-MS output files in vendor-specific formats from a mass spectrometry instrument and need to process them through an untargeted metabolomics workflow (e.g., AriumMS) that requires standardized, interoperable file formats.

Use when you have MS-DIAL 4 or MS-DIAL 5 alignment results and need to configure LipoCLEAN for quality filtering of lipid identifications. Use this skill at the start of a LipoCLEAN analysis when you need to specify which MS-DIAL export files to analyze and how to locate them.

Use when you have performed lipid identification in MS-DIAL and need to pass the results to LipoCLEAN or another downstream quality-filtering tool. The skill is required whenever you are preparing MS-DIAL output for consumption by external analysis pipelines that expect standardized export formats.

Use when when you have raw LC-HRMS data in .mzML or .abf format and need to detect metabolite features (peaks) across multiple samples, align them temporally and by mass-to-charge ratio, and generate a reproducible feature matrix.

Use when you have completed peak picking in MS-DIAL (generating files like Urine_RP_NEG_norm.txt or Urine_RP_POS_norm.txt) and need to load the resulting feature table into R for quality control, feature filtering, normalization, or metabolite annotation.

Use when you have a filtered MS-DIAL peak list (post-generic filtering, containing m/z, retention time, and peak intensity metrics for each feature) and need to group features into clusters that represent true metabolite signals rather than instrumental or chemical artifacts.

Use when you have untargeted LC-MS/MS metabolomic data (peak-detected .mzXML/.mzML/.mzDATA files processed through MZmine2) and need to relate MS1 features by chemical similarity rather than sequence homology.

Use when after completing MS-CleanR filtering (blank subtraction, background removal, RSD/RMD thresholding) and feature clustering steps, when you have a consolidated set of representative features and need structural identification via MS/MS spectral matching.

Use when when evaluating or designing a mass spectrometry data analysis platform, and you need to verify that every supported separation/ionisation technique (LC, GC, IMS, MS Imaging) is covered by at least one processing module.

Use when after ModiFinder has generated modification site probability scores for an unknown compound by comparing its MS/MS spectrum to a known analog, and you have access to the true structure of the unknown compound (oracle mode) or a reference modification site annotation.

Use when you have completed LC-MS/MS data processing and feature alignment in MZmine2 or Optimus, generated a feature quantification matrix and MGF file, and now need to format these outputs for submission to GNPS to compute spectral similarity networks and retrieve node/edge tables.

Use when when you have predicted MS/MS fragments from quantum chemistry calculations on N-Me derived unsaturated sterol structures and need to map each fragment to its precursor lipid, calculate exact m/z values, estimate relative intensities, and produce a machine-readable reference table for.

Use when when you have two MS/MS spectra (each with a precursor m/z and a list of fragment ion m/z values) and need to identify which fragment ions correspond between them, especially when structural differences make simple monotonic alignment unreliable.

Use when when you have raw LC-MS/MS instrument output files (e.g., .mzML, .

Use when when you have MS/MS spectra from both query compounds and a reference library and need to decide which similarity metric will maximize identification accuracy (true positive rank, precision@k) or when benchmarking a new compound identification workflow against a known-good reference.

Use when when you have MS/MS spectral data (raw or intermediate format) that must be fed into the Mass2SMILES Docker container or similar deep learning models for MS/MS-to-structure inference.

Use when you have acquired MS/MS spectral data (in mzML, mzXML, or equivalent format) for unknown compounds and need to identify the most probable metabolite structure.

Use when you have experimental MS/MS spectra from nontargeted metabolomics data and need to assign molecular identities or identify structurally related analogs.

Use when you have a labelled dataset of raw MS/MS spectra annotated as 'relevant' (compounds of interest) or 'other' (reference standards or non-target compounds) and need to prepare them for supervised classifier training.

Use when when you have raw MS/MS spectra (from NIST, MassBank, or local acquisition) and plan to compute spectral similarity for compound identification.

Use when you have paired MS/MS spectra from unknown and known metabolites with raw intensity values and need to prepare them as input for a deep-learning model that will predict structural similarity.

Use when you have raw LC-MS/MS data acquired in Data-Dependent Acquisition (DDA) mode and need to create a labeled training dataset for customized purification model development.

Use when you have a cleaned and clustered set of LC-MS features (m/z, retention time, MS/MS spectra) from MS-CleanR output and need to assign putative compound identities by matching observed MS/MS fragmentation patterns against reference spectral libraries using HRR-based scoring.

Use when you have millions of MS/MS spectra in mzML, mzXML, or MGF format that have been converted to low-dimensional vectors via feature hashing, and you need to identify which spectra are similar enough to cluster together.

Use when you have raw or preprocessed MS/MS spectra in one of the supported formats (MGF, mzML, mzXML, JSON, MSP, mzXML, pickled matchms objects, or USI) and need to extract peak lists (m/z and intensity pairs) along with metadata (precursor m/z, charge, ionization mode) to feed into MS2Query or.

Use when you have an experimental MS/MS spectrum (e.g., from MassBank or local data) and need to identify significant fragment ions above noise, assign occurrence scores to peaks, and generate a CSV-formatted library entry for use in metabolite feature annotation pipelines.

Use when you have MS/MS spectra (in MGF format) with required metadata fields (TITLE, PRECURSOR_MZ, PRECURSOR_TYPE, COLLISION_ENERGY) and need to predict candidate molecular formulas ranked by confidence.

Use when you have raw or semi-processed MS/MS spectral data from bottom-up tandem mass spectrometry experiments (data-dependent acquisition) that you intend to input to de novo peptide sequencing tools like Casanovo.

Use when processing LC-MS/MS data acquired in DDA mode that contains chimeric (co-fragmented) MS/MS spectra—i.e., when a single MS/MS scan contains fragments from multiple precursor ions due to co-isolation.

Use when you have aligned fragment ion pairs from two MS/MS spectra (via maximum weight matching or other methods) and need to assign p-values or Z-scores to each matched pair to distinguish true biological/chemical relationships from random noise.

Use when when you have raw MS/MS spectra in MSP format (or similar) with m/z–intensity peak pairs and need to prepare them for neural embedding models that require fixed-size discrete token inputs. Applies before generating dense spectral embeddings for retrieval or similarity scoring tasks.

Use when you have an MS-DIAL feature table from DDA or DIA LC-MS analysis that includes blank injection samples (at least 3 recommended), and you need to eliminate features that are artifactual contamination rather than genuine metabolites.

Use when you have acquired a Thermo mass spectrometry RAW file (or other proprietary instrument format) and need to extract MS1 and/or MS2 scans in an open, interoperable format (mzML, MGF, or Raxport-processed FT1/FT2 files) for TIC visualization, PSM scoring, or stable isotope labeling analysis.

Use when when you have a raw or partially processed MS/MS spectra collection (e.g., GNPS-sourced Orbitrap or Q-TOF spectra in MGF format) and need to (1) restrict to a specific instrument type (e.

Use when you have raw LC-MS/MS data in mzML or mzXML format and need to: (1) identify the top-abundance MS1 signals in an LC run, (2) compute a single scalar metric (separation efficiency) that summarizes how well compounds are resolved across the chromatogram, and (3) feed that metric into a.

Use when you have MGF or native MS/MS arrays (mz_array, intensity_array, precursor_mz, adduct) and want to predict the most likely molecular formula. The input spectra must include required MGF fields (TITLE, PRECURSOR_MZ, PRECURSOR_TYPE, COLLISION_ENERGY) or equivalent Python API parameters.

Use when when comparing two or more MS/MS spectra for compound identification, library matching, or spectral validation. Triggered when raw spectral data requires quantitative similarity assessment before database lookup, or when validating that two spectra originate from the same chemical compound.

Use when you have raw or parsed tandem MS spectra (MGF, mzML, or in-memory Spectrum objects) and need to remove artifacts and normalize intensities prior to spectral matching, library searching, or downstream analysis.

Use when after computing a sparse pairwise distance matrix from nearest neighbor indexes of MS/MS spectra (in mzML, mzXML, or MGF format), and you need to assign each spectrum to a cluster group for downstream analysis such as peptide identification or spectral library construction.

Use when you have FIA-MS, LC-MS, or GC-MS full-scan data in mzML format and need to identify unknown molecular features by accurate mass.

Use when when you have an LC-HRMS feature table (with m/z values, retention times, and isotopic signatures) and a suspect compound database (with reference m/z, expected retention time windows, isotope ratios, and neutral loss fragments), and you need to rapidly prioritize which features are most.

Use when you have merged MS1 spectra (output from spectral binning/merging steps) from a full-scan FIA-MS or LC-MS acquisition and need to identify distinct molecular features before accurate mass annotation or background filtering.

Use when when you have raw LC-MS data files and need to identify which compounds were actually detected at high abundance during a gradient run, prior to evaluating whether the gradient provided good separation across the chemical space.

Use when when you need to prototype, test, or benchmark MS1-only acquisition strategies on a defined set of metabolites (e.

Use when you have a Thermo Fisher Scientific .raw file from an Orbitrap instrument and need to programmatically retrieve MS1 spectral attributes (base-peak m/z, intensity, retention time) for downstream statistical analysis or quality control in R, rather than relying on external preprocessing.