Claude Skills by HolobiomicsLab

Use when when correcting LC-MS fractional abundances of measured isotopologues (FAM) from isotope labeling experiments where instrument resolution (e.

Use when when you have a feature table from LC-MS preprocessed data (e.g. from asari v1.9.2) and need to annotate ions and infer neutral mass.

Use when when preprocessing a public MS/MS spectral library (e.g., GNPS) for machine learning and you discover discrepancies between expected and observed compound counts after filtering by instrument type, or when a known instrument metadata issue (e.

Use when you have a large MS/MS experiment (mzML format) requiring lipid annotation and need to match experimental spectra against >10 million theoretical lipid fragments.

Use when when you have an unknown MS/MS spectrum (with ≥10 peaks, precursor m/z, and at least 5 fragment ions) and need to identify it by comparing against a curated spectral library with annotated InChIKeys or chemical structures.

Use when you have a set of unidentified tandem mass spectra (queries) and need to identify them by matching against a curated reference library (e.g., GNPS Orbitrap dataset).

Use when when you have detected peaks in a direct injection FTICR-MS mzML file (or similar high-resolution MS format) and need to assess whether m/z measurements are accurate and consistent across the m/z range.

Use when you plan to perform repeated queries or filtering on MS metadata attributes (e.g., extract all MS2 spectra with collision energy > 30 eV, or collect all scans in a retention time window) across a large MZA HDF5 file.

Use when you have peak/feature tables from one or more of MZmine, XCMS, MS-DIAL, or Compound Discoverer and need to integrate them into a unified lipidomics workflow (e.g., LipidMatch).

Use when when integrating LC-MS/MS data from diverse sources (e.g., public repositories like MSV000080102, instrument outputs, or precomputed workflows) into NPDtools pipelines.

Use when after importing raw LC-MS/MS data files into the SIRIUS Java framework, before constructing indexed spectrum objects or submitting data to CSI:FingerID, CANOPUS, or MSNovelist web services.

Use when when you have a Thermo Fisher Scientific Orbitrap .raw file and need to confirm that a targeted acquisition method (e.g., PRM targeting a specific precursor m/z) is achieving uniform cycle timing.

Use when you have raw DIA mass spectrometry files (.raw, .d, .wiff formats) from timsTOF, TripleTOF, or Orbitrap instruments and need to quantify file quality for automated quality control, longitudinal instrument monitoring, or training a quality prediction classifier.

Use when you have multi-sample MS1 data (LC-MS, LC-IMS-MS, or direct infusion across any omics domain) and need to detect samples with abnormal global ion intensity patterns or unusual per-ion metric behavior (intensity distribution, signal-to-noise, retention time stability) that may indicate.

Use when you have R Spectra objects and need to apply Python-only MS algorithms (e.

Use when when you have aligned peak data from molecular networking (with m/z, intensity, retention time, and alignment quality metrics across multiple spectra) and need to interactively explore peak alignments under multiple filtering criteria (intensity thresholds, alignment score cutoffs, peak.

Use when you have raw mzML files and feature tables (CSV format from mzMine or XCMS) from untargeted LCMS experiments and need to distinguish true metabolite peaks from false positives introduced by the peak-picking algorithm.

Use when you have raw MS/MS spectra in MGF, mzML, or msp format and need to prepare them for mass2motif discovery or topic modeling.

Use when when you need to locate and extract quantitative retention time and intensity data for known peptide standards (e.g., iRT peptides) from a Thermo .raw file to validate LC-MS retention time linearity, assess method reproducibility, or establish retention time calibration curves.

Use when when you have a collection of compound structures in SDF format (e.

Use when immediately after importing raw peak tables and metadata from MS preprocessing software (e.g., Progenesis, MS-DIAL, Bruker Metaboscape).

Use when when setting up a new LC-MS QC workflow or modifying existing QC rules: you have access to internal standards and target analytes, know their expected retention times and m/z values, and need to establish pass/fail boundaries for sample acceptance before or concurrent with instrument runs.

Use when you have centroided .mzML LC–MS data, a validated target compound list with adjusted expected retention times (e.g., after a screening mode run), and need to quantify peak quality and integration reliability across multiple sample runs to support metabolomics or lipidomics workflows.

Use when you have a high-resolution LC-MS/MS experiment with a measured [M+H]+ or [M-H]− ion mass and optionally a parent ion fragmentation spectrum (peak list with m/z and intensity pairs), and you seek to generate candidate molecular structures for an unknown metabolite that may not be in.

Use when you have generated or obtained a two-dimensional mass-spectrometry intensity matrix (m/z × retention time scan points) with simulated or experimental peak shapes, noise, and background, and need to encode it as a binary .

Use when when you have raw DIA mass spectrometry data files (.raw, .d, .

Use when you have individual MS/MS spectra or batch .mgf files from untargeted metabolomics experiments and need to search them against domain-specific reference libraries (e.

Use when you have positive- or negative-mode tunemix reference data (with known CCS values, m/z, and measured drift times) and need to establish a calibration model for converting observed drift times into CCS values for downstream feature annotation.

Use when you have acquired tunemix data (positive or negative ion mode, in .h5 format) with known CCS reference values and need to construct a calibration function that will later predict CCS values for unknown analytes.

Use when after executing a MassQL query against mzML mass spectrometry data files and obtaining tabulated results (DataFrame or equivalent in-memory table), apply this skill to persist those results in both human-readable CSV format and visual image form for archival, sharing, and downstream.

Use when you have raw LC-MS/MS chromatogram files in mzML/mzXML format (converted from Thermo, Waters, or Bruker instruments) acquired in DDA or targeted MS/MS mode, and you need to isolate specific MS1 precursors and their corresponding MS2 fragments based on known m/z values and optional.

Use when you have a Thermo Fisher Scientific .raw file and need to (1) enumerate all scans and their metadata, (2) identify which scans are MS1 vs. MSn to enable level-specific filtering, (3) retrieve scan ranges or specific scan numbers for targeted spectral extraction, or (4) plan.

Use when you have high-resolution LC-MS data processed through both XCMS feature detection and RAMClustR clustering, and you need to verify the reliability of molecular weight assignments before downstream annotation or statistical analysis.

Use when when you have high-resolution LC-MS or GC-MS data from environmental samples and need to simultaneously screen for both known suspect chemicals and their transformation products, rather than targeting single compounds.

Use when you have a pair of MS/MS spectra—one from a known compound and one from a structurally modified variant of that compound—and you need to identify which atoms in the structure likely bear the modification.

Use when when you have preprocessed MS/MS spectral pairs (peak intensities and m/z values) and need to predict molecular structural similarity scores, or when you want to project spectra into a learned chemical embedding space for visualization (e.g., via UMAP) or downstream similarity searches.

Use when compiling or harmonizing MS/MS spectral libraries from multiple source repositories and you need to identify and remove spectra that fail quality thresholds (low resolution, precursor-fragment mass inconsistency, duplicate fragment patterns, noise-dominated, or missing critical metadata.

Use when you have pre-processed MS/MS spectra and need to prepare them for word-embedding-based similarity methods (e.g., Spec2Vec).

Use when you have high-resolution MS/MS spectra in mzML, mzXML, or MGF format and need to cluster or search across millions of spectra. The vectorization step is necessary before constructing nearest-neighbor indexes or computing pairwise distance matrices for spectrum clustering.

Use when after identifying statistically significant features (e.

Use when after Mass2Motif discovery via LDA on preprocessed MS/MS spectra, when you have a set of inferred motifs (fragments and neutral losses with LDA probabilities) and need to assign putative substructure identities rather than retain anonymous motif labels.

Use when after generating a feature table from mzML data (via Asari) and before performing MS1 or MS2 annotation.

Use when after feature extraction from raw LC-MS or GC-MS data (using XCMS, MS-Dial, or similar), when you have a feature intensity table with m/z and RT metadata and a reference compound database (known molecules list with m/z, RT, and annotation metadata), and you need to assign confidence-ranked.

Use when when you have raw mass spectrometry transition data from a triple-quadrupole or other tandem MS instrument and need to prepare it for suspect chemical screening using EISA-EXPOSOME, or when merging custom compound libraries into the T3DB format.

Use when when you have a collection of mzML.gz files from a multidimensional MS instrument (e.

Use when you have a collection of mzML or mzML.gz files from LC-IMS-MS/MS experiments and need to apply a consistent, reproducible sequence of feature detection, alignment, CCS calibration, isotope detection, and MS/MS deconvolution operations across multiple samples.

Use when when analyzing tandem mass spectra with unknown precursor adduct identity, especially for positive-mode data containing non-protonated adducts ([M+Na]+, [M+K]+, [M+NH4]+).

Use when you have loaded a Bruker Solarix transient file (.d format with .ser or .fid content) and need to generate a processed mass spectrum for peak picking and molecular formula annotation.

Use when when you have Thermo Fisher Scientific .raw files from Orbitrap instruments and need to build a quantitative summary of MS1 acquisition intensity dynamics across a run—specifically, the m/z and intensity of the most intense peak in each MS1 scan.

Use when when preparing MS/MS spectra for neural network training or inference, particularly when you need to feed variable-length spectra into a Siamese network or embedding model that requires fixed-dimensional input.