Claude Skills by HolobiomicsLab

Use when when preparing to run ORA on a metabolomics study: you have a list of detected metabolites from your experiment and need to determine which metabolites from the full pathway database should serve as the statistical background, and which pathways contain how many metabolites overall.

Use when when you have completed a PALS pathway analysis on a clean metabolomics peak intensity matrix and pathway annotation set, and you need to verify that the ranked pathway discoveries are not artifacts of favorable data quality.

Use when when selecting a pathway analysis method for metabolomics peak data where noise and missing peaks are prevalent, or when validating that a chosen method performs consistently across realistic data quality variations.

Use when you have isolated reference peaks from training chromatograms (ground-truth, single compounds per sample) and need to create a diverse, labelled training set large enough to train a CNN peak detector.

Use when after EIC candidate generation and peak detection have been completed on LC/HRMS data, when you need to extract the retention time and intensity values at peak maxima for each detected peak.

Use when you have LC-HRMS profile-mode chromatograms with extracted local maxima exported as standardized 2D rt×mz areas, and you need to disambiguate true chromatographic peaks from background signals (including wall artifacts and noise) at scale.

Use when you have LC-HRMS chromatograms in retention time × m/z matrix format and need to automatically localize chromatographic peak positions and extents prior to matching against a reference peak library or generating CNN training instances.

Use when when you have initialized an MsBackend subclass (e.

Use when after running a non-targeted preprocessing (NPP) pipeline (e.g., XCMS, MZmine 2, OpenMS) on the same set of centroided mzML files that were used to generate a validated benchmark dataset.

Use when when loading and processing raw or recalibrated FT-ICR mass spectrum data (Bruker .d format) on a defined field-strength instrument (e.g., 12 T or 15 T), before executing molecular formula search.

Use when you have mass spectrometry data in .raw, .d, or mzXML format and a predefined list of target compounds (m/z and retention time ranges) you wish to quantify.

Use when after scipy.signal.find_peaks has identified candidate peaks on a composite mass track segment, evaluate each peak to decide whether to retain it in the final feature table.

Use when when applying iterative peak detection (local-maximum or Gaussian-fit methods) to 1D extracted ion chromatograms (XICs), arrival time distributions (ATDs), or MS1 spectra from MZA-format files, and you need to decide which peaks to retain based on their intensity.

Use when after peak filtering (by m/z, isotopic presence, formula assignment error, and sample prevalence) and before multivariate analysis (PCA, NMDS, PERMANOVA) when comparing peak abundance patterns across samples with potential differences in ionization efficiency, ion suppression, or total ion.

Use when when uploading a new peak list or complex sample data file with delimiter-separated columns to Punc'data, especially when column headers are ambiguous or use non-standard naming conventions.

Use when you have raw mass spectra (e.g., from NIST 2017 or MassBank) and need to prepare them for Word2vec embedding or other token-based neural models. Use it as a preprocessing step before training spectral embedding models, especially when scale and accuracy of spectrum matching are priorities.

Use when after molecular formula assignment and peak filtering are complete, when you have a filtered peak list (m/z values and molecular formulas) and want to discover biochemical transformations without prior knowledge of reaction networks.

Use when you have centroided high-resolution Orbitrap or GC-CI-MS mzML files and a formulaTable of target compounds with known m/z, retention time, and molecular formula, and you need to extract isotopologue abundances and quantification metrics (area, maxo intensity, ppm error) for each labeled.

Use when after composite-map peak detection (scipy.signal.find_peaks) has identified candidate peaks on aligned mass tracks, but before compiling the final feature table.

Use when when extracting benchmark peaks from mzML files for multiple isotopologues of target molecules, after initial m/z and retention-time matching, to validate that detected isotopologue peaks exhibit consistent peak shape and expected abundance ratios before including them in a reliability.

Use when after peak detection has been applied to untargeted or targeted mass spectrometry data and peaks have been matched against metabolite databases and reference spectra.

Use when after converting peak-picker output (from MZmine, XCMS, MS-DIAL, or Compound Discoverer) into LipidMatch-compatible format. Use this skill when you need to verify that the converted file will be successfully read by LipidMatch before proceeding to lipid identification;

Use when after running a 1D peak detection function (e.g., mzapy.peaks.find_peaks_1d_localmax or mzapy.peaks.find_peaks_1d_gauss) on synthetic mass spectra with known peak locations and heights.

Use when you have raw mass spectrometry data in MS1 format and need to assign charge states to peptide ions without manual curation or rule-based heuristics.

Use when when you have access to a set of gallery or benchmark scripts executed across multiple plotting backends and need to quantify which backend delivers the fastest median execution time for specific mass spectrometry plot types (chromatogram, mobilogram, peakmap, peakmap-marginals, spectrum.

Use when after running PERMANOVA on distance matrices derived from FT-ICR MS metabolite peak intensities or other high-dimensional compositional data, when p-values indicate statistical significance but ordination plots (NMDS, PCA) fail to discriminate among treatment groups.

Use when you have normalized peak intensities or abundance matrices from mass spectrometry (e.

Use when when you have a parent drug's raw chemical formula and need to predict its likely metabolite formulas for subsequent mass spectrometry matching, especially in high-resolution MS workflows where exact mass matching requires known candidate formulas.

Use when you have mass spectrometry data (m/z, retention time, intensity) loaded into a Pandas DataFrame and need to explore the full 3D structure of a peak map interactively, particularly when static 2D heatmaps obscure important intensity relationships or when stakeholders require browser-based.

Use when you have a preprocessed peak table (feature matrix with samples × peaks), known sample class labels or group membership, and a goal to classify or discriminate between two or more sample groups.

Use when you have a multi-polarity compound target list (e.g., a .xlsx file with a polarity or ionization mode column indicating positive or negative ESI mode) and you are about to perform targeted peak detection in a single LC–MS acquisition mode (e.g., positive-ion mode only).

Use when when working with targeted LC–MS metabolomics or lipidomics data where compound targets span both positive and negative ionization modes, or when your raw .

Use when when building a plotting library that must support multiple visualization types (1D spectra, chromatograms, mobilograms, 2D peak maps) across heterogeneous rendering backends, and you want users to specify plot type via a single kind parameter rather than importing backend-specific classes.

Use when after molecular formula assignment has been performed on FT-ICR MS peaks and you need to remove assignments with unacceptable mass error before proceeding to chemodiversity analysis, transformation network generation, or multivariate statistics.

Use when when you have assigned molecular formulas to m/z peaks or computed mass differences between peaks in FT-ICR MS data, and you need to distinguish true chemical matches from noise or random coincidences.

Use when a mass spectrum calibration procedure initialized with a narrow ppm window (e.g., ±1.0 or ±5.0 ppm) finds fewer than 5 reference m/z matches.

Use when valueMatcher.series_match has detected database-ID values (HMDB IDs, PubChem CIDs, KEGG compound identifiers) in raw metabolite metadata columns and you need to normalize them into a canonical form for storage in standardized database-ID columns (e.g., HMDB_ID, PubChem_ID, KEGG_ID).

Use when you have a pretrained PyTorch model checkpoint (e.g., JESTR weights for NPLIB1) and wish to apply it to rank candidate molecules or score spectra without modifying model parameters.

Use when when peptide identifications from a search engine (MaxQuant, MSGFPlus, Sage, etc.) contain custom or engine-specific modification notation that must be converted to a standardized format before rescoring, or when different search engines use incompatible modification label schemes that.

Use when when building a mass-spectrometry analysis pipeline that must support multiple vendor and open formats (mzML, mzXML, Thermo, Agilent, Bruker, etc.) and you need to shield downstream tools from format-specific parsing logic.

Use when after the Proteomics_Data_Processor tool has completed analysis of raw mass spectrometry files and returned structured output records.

Use when when you have a raw list of SMILES strings but lack sufficient real mass spectrometry reference data (typically <300 spectra) to train a generative model for unknown chemical identification.

Use when when you have PSM identifications from a search engine (e.

Use when when you have PSM files from a proteomics search engine (e.g., MaxQuant, MSGFPlus, Mascot) and corresponding spectrum files (mzML or MGF format) that need to be linked before rescoring.

Use when you need to supply candidate metabolite structures for mass spectrometry annotation when working within an integrative metabolomics data analysis workflow (such as MAGMa).

Use when your metadata table contains compound names but lacks structure information (SMILES, InChI, molecular formula, or PubChem CID).

Use when investigating how a specific pull request (e.g., PR #72 introducing MS2Query''s two-branch workflow split) modified the codebase architecture, control flow, or data routing.

Use when when you have mzML spectrum XML already parsed (either from a file, a database query, or an in-memory representation) and need to construct Spectrum or Chromatogram objects that expose methods like accessing MS level, retention time, m/z and intensity arrays, and other metadata.

Use when when you have mzPeak files (Parquet-based archives in uncompressed ZIP containers) or other PyArrow-compatible columnar formats containing mass spectrometry spectra, and you need to extract and decode spectral data arrays (m/z values, intensities) into Python memory for downstream.

Use when when you have a C++ library (such as OpenMS) with nanobind binding specifications in a designated bindings directory and need to create a Python module that exposes C++ classes, functions, and data types to Python code.