Claude Skills by HolobiomicsLab

Use when you have raw GC-MS output from Agilent Unknowns Analysis (a .CSV with columns Component.RT, Base.Peak.MZ, Component.Area, Compound.Name, Match.Factor, File.

Use when after chromatographic peak detection on preprocessed LC-MS data when you have an xcms result object (XcmsExperiment or xcmsSet) with detected peaks and need to collapse redundant m/z signals into feature groups.

Use when after chromatographic peak detection (findChromPeaks) when you have a processed XcmsExperiment object with detected peaks and need to perform initial feature grouping. Use it when features of the same compound are expected to co-elute within a narrow retention-time window (e.

Use when working with GCIMS datasets where retention time spans a wide range (e.g., 0–1500 s) but your analytes of interest are confined to a narrower window (e.g., 0–1100 s).

Use when you have a set of small-molecule compounds (e.g., from MS/MS library matching or database annotation) that require retention time validation or ranking to resolve ambiguous identifications.

Use when when you have a retention-time dataset (e.g., SMRT or Eawag_XBridgeC18_364) in .xlsx format and need to train or adapt a graph neural network model to predict chromatographic retention times for new compounds.

Use when designing multi-batch LC/GC-MS experiments where samples belong to multiple groups or conditions and you need to ensure that each injection plate receives a balanced representation of all groups.

Use when when designing injection sequences for LC/GC-MS multi-omics experiments where you need to distribute samples across multiple plates and must account for mandatory QC sample positions (Blank QC, Solvent QC, Pooled QC, Long-Term Reference QC, and custom QC).

Use when when you have an Excel file uploaded by a user following the InjectionDesign template schema and need to convert it into a modifiable, structured sample list that preserves up to three classification dimensions and QC type assignments for LC/GC-MS multi-omics experiments.

Use when when beginning mass alignment in a multi-sample LC-MS metabolomics study, before constructing the MassGrid.

Use when after filtering retention time and drift time ranges on raw GCIMS samples but before decimation and alignment.

Use when you have a new or modified LC-MS data processing tool and need to determine whether it can handle production-scale sample cohorts (50–100+ samples) on modest hardware (single-core CPU, ≤16 GB RAM).

Use when when evaluating whether an MS data processing platform (such as mzmine) supports the full range of separation/ionization techniques your laboratory uses, or when assessing whether gaps exist in the software architecture that would require external pre- or post-processing for specific.

Use when you have processed the same GC–MS dataset (m/z vs retention time) through two independent signal acquisition pipelines and need to compute and compare their detection accuracy. Use this skill when one method (e.

Use when when you have a query electron ionization mass spectrum (m/z and intensity pairs) and need to identify the most similar spectra from an MSP-formatted spectral library.

Use when you have observed compounds (from LC-MS, GC-MS, or spectroscopy) and a set of predicted metabolite structures from BioTransformer, and need to assign identities to the observed compounds by matching their experimental features (mass-to-charge ratio, retention time, spectral signature) to.

Use when when processing downloaded mass spectral libraries (particularly MoNA EI or MS2 libraries) where SMILES information exists but is embedded in unstructured Comment fields rather than a dedicated SMILES field, or when assigning SMILES from external structure databases (SDF files) to library.

Use when you need to verify the scope and completeness of a software platform's analytical capabilities—particularly when the project claims to support multiple input modalities (e.

Use when you need to assess whether a newly developed FT-ICR MS pipeline (e.

Use when when processing raw or aggregated mass spectra datasets (from .mgf, .msp, .json, or .

Use when you have deconvolved GC-MS spectra in GNPS_GC input-compatible format and want to construct a molecular network to identify relationships between unknown compounds and perform structured chemical similarity analysis.

Use when you need to generate synthetic LC/GC-MS feature tables or raw mzML files with realistic peak complexity, ion multiplicities, and natural spectral variation—not just theoretical m/z values.

Use when after auto-deconvolution has resolved co-eluting GC-MS peaks into individual component spectra.

Use when you have raw mass-spectrometry data (precursor m/z, ionization mode, and fragment m/z–intensity pairs) and need to feed it into a CNN-based metabolite annotation pipeline.

Use when you have multiple mass spectral library files in different formats (MSP, MGF, MOL folders) from sources like NIST, MoNA, RIKEN, or GNPS, and need to produce a single consolidated MSP file with complete SMILES, InChIKey, and experimental retention index (RI) annotations for metabolomics or.

Use when you have raw GC-MS output in CSV format (with Component.RT, Base.Peak.MZ, Component.Area, Compound.Name, Match.Factor, and File.Name columns) and need to systematically rank putative identifications by match quality and exact mass agreement.

Use when you have GC-MS data preprocessed into a structured spread format and need to confirm that a set of known or suspected compounds are correctly identified in your samples.

Use when when building a comprehensive reference spectral library for metabolomics or chemical identification, you have multiple source libraries in different formats (msp, mgf, NIST binary) and ionization modes (positive/negative MS/MS or EI) that need to be combined into a single.

Use when you have a compiled EI or MS2 library object (read from MSP format via read_lib) and access to NIST ri.dat and USER.DBU files; you need to populate RI values for capillary GC-MS workflows.

Use when when ingesting mass spectral libraries (EI or MS2) where SMILES information is embedded in the Comment field rather than in a dedicated SMILES metadata field—particularly common in MoNA GC-MS and LC-MS/MS exports.

Use when you have deconvolved GC-MS spectra (from overlapping chromatographic peaks) in MGF or mzTab format and want to group chemically related compounds, visualize their similarity relationships, and identify spectral families without prior library matching.

Use when you have loaded an MS2 library (from NIST, GNPS, or other sources via read_lib()) that contains both positive and negative ionization modes mixed in a single file, and you need to produce two separate, polarity-specific MSP files for use in MS-DIAL or similar tandem MS analysis software.

Use when when you have GC-MS output with Match.Factor values or structural similarity scores from categorate() and need to decide which identified compounds are reliable enough to carry forward. Specifically: (1) after running GC-MS and receiving a Match.Factor column;

Use when after loading an MSP spectral library file into memory using mssearchr's MSP parser, when you need to verify that each spectrum record contains complete and valid metadata (precursor m/z values, peak lists, header annotations) before writing the parsed spectra to a new MSP file or.

Use when immediately after extracting ion chromatograms (EICs) by binning mass spectral data across the full m/z range from raw LC/HRMS files (mzML, mzXML, or netCDF format).

Use when after peak detection on individual GC-IMS samples, when you need to assign consistent cluster IDs to peaks detected across multiple samples to enable cross-sample comparison and quantification.

Use when when generating synthetic LC/GC-MS .mzML files from MoNA or HMDB spectral records where you need to compute absolute ground-truth maximum intensity (sim_ins) for each peak while accounting for chromatographic band broadening, peak tailing, and retention-time dispersion.

Use when when you have MS/MS spectra (LC-MS or equivalent positive ionization mode data) that you intend to embed using Word2Vec or similar distributional semantic models, or when you need to prepare spectral data for training similarity models that learn peak co-occurrence patterns rather than.

Use when you have pre-trained MLP and GNN spectral prediction models evaluated on the same ESI/LC-MS test dataset, and you seek to improve average rank performance beyond either baseline model alone.

Use when after extracting and optionally combining MS2 spectra from a chromatographic peak (e.g., at a known m/z value like 304.1131), you need to determine which compound(s) in a reference library match the experimental spectrum.

Use when you have a query spectrum (or set of query spectra) and need to rank candidate library spectra by their likelihood of sharing the same chemical structure.

Use when you have a query electron ionization (EI) mass spectrum and need to search it against a library of known EI mass spectra to identify unknown compounds. Use it when exact identity matching (e.

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 when converting pre-processed MS/MS spectra into fixed-length vector representations using Word2Vec embeddings for Spec2Vec similarity scoring. Specifically, apply this skill after you have represented individual peaks and neutral losses as words ('[redacted-email]', 'loss@xxx.

Use when when preparing MS/MS spectral data for training word-embedding models (Word2Vec, Skip-gram, CBOW) that will learn relationships between fragment ions and neutral losses.

Use when importing mass spectra from multiple open mass spectra libraries (OMSLs) or databases with heterogeneous metadata quality. Use when you observe spectra annotated with negative adducts (e.

Use when when you need to quantify and compare the filtering efficacy of mutually exclusive noise-threshold methods on the same input mass spectrum, or when validating that a selected noise-filtering strategy retains an expected number of peaks for downstream molecular formula assignment.

Use when you have a spectral library with structural ground truth (InChIKey or SMILES annotations for ≥50% of spectra) and want to benchmark whether a new or existing spectral similarity scorer ranks structurally related compounds higher than unrelated ones.

Use when you have an experimental tandem mass spectrum (collision-induced dissociation or ESI-MS/MS) and a chemical formula, and you need to identify the correct structure among multiple isomeric candidates in a database like PubChem.

Use when importing MS/MS spectral libraries (particularly from MoNA or GNPS) where SMILES or chemical structure identifiers are embedded in free-text or non-standard Comment fields rather than in dedicated SMILES/InChIKey fields, or when positive and negative ionization mode spectra are commingled.