Claude Skills by HolobiomicsLab

Use when you have loaded m/z and intensity arrays from an MZA file (via mzapy) and need to inspect a mass spectrum, extracted ion chromatogram (XIC), or arrival time distribution (ATD) visually, either for QC purposes, method development, or publication-ready output.

Use when when a new file format specification exists as a living document (e.

Use when you have raw mass-spectrometry data files (mzML, mzXML, or vendor formats) from untargeted metabolomics experiments and need to identify and quantify differential metabolic ion peaks.

Use when when you have loaded a raw mass spectrum (e.g. ESI_NEG_SRFA.

Use when when building a mass spectrometry analysis pipeline that requires exact mass lookups, molecular formula validation, or isotope abundance predictions. Specifically: (1) you are constructing a MolecularFormula class that needs to validate element symbols against known isotopes;

Use when you have MS1-format mass spectrometry files and need to determine the isotope count for peptide features without manual inspection or expert parameter tuning.

Use when you have high-resolution centroided mzML files from Orbitrap or similar instruments, a target compound list with known formulas and monoisotopic m/z values, and need to quantify isotopologue abundances (M+0, M+1, M+2, etc.) for stable isotope labeling experiments.

Use when when you have one or more peptide sequences (as strings) and need to predict their theoretical isotopic distribution for comparison against experimental MS peaks, validation of mass calibration, or simulation of expected peptide signals in a mass spectrometry assay.

Use when when you have one or more peptide sequences (as strings) and need to predict their isotopic distribution pattern for MS instrument simulation, peak deconvolution, or validation of observed isotopic envelopes.

Use when after loading raw FT-ICR MS peak lists with assigned molecular formulas when you have detected peaks across multiple m/z values that correspond to isotopic variants of the same parent compound.

Use when you have a peak-picked feature table (HDF5 format with m/z, drift_time, retention_time, intensity columns) and need to identify and label isotopic signatures within singly charged species.

Use when evaluating the reliability of non-targeted data pre-processing (NPP) tools (XCMS, MZmine 2, MS-DIAL, etc.) on known metabolite peaks.

Use when when extracting isotopologue patterns from centroided mzML files in non-targeted metabolomics workflows, apply this filter after detecting candidate isotopologue peaks but before finalizing the benchmark dataset.

Use when you have 1D mass spectrometry signal data (e.

Use when you have a mass spectrum from an untargeted metabolomics experiment and a set of candidate molecules (e.g., downloaded from PubChem) that may explain that spectrum. You want to rank these candidates by likelihood of correctness to prioritize manual annotation or further validation.

Use when when you have extracted file metadata or scan summaries as R list objects from .raw files using readFileHeader(), readIndex(), or readSpectrum(), and need to persist them to disk, share them across systems, or feed them into downstream tools that consume JSON (e.

Use when after running the msfeast_pipeline notebook to generate dashboard_data.json from quantification tables, metadata, and spectral data. Use this skill to verify that the exported JSON contains all required fields and structure before attempting to load the file into msFeaST_Dashboard_bundle.

Use when you have parsed a MassQL query string into an AST representation and need to store, validate, transmit, or integrate the query structure with other tools or systems.

Use when you have raw or semi-curated mass spectrometry spectral data in JSON, CSV, MSP, or MGF format from multiple open mass spectra libraries (OMSLs) and need to standardize field names, validate chemical identifiers (SMILES, InChI, InChIKey), remove duplicates, filter by quality criteria.

Use when when you have raw mass spectrometry spectral data in JSON format from open mass spectra libraries (OMSLs) or other sources and need to validate structural completeness, check for required metadata fields (SMILES, InChI, InChIKey, precursor m/z, adduct), and separate spectra by acquisition.

Use when when you have three coordinated mass spectrometry data tables (quantification, metadata, spectral) ready for integrated preprocessing and statistical analysis, and you need to generate a standardized JSON artifact for downstream interactive exploration rather than static tabular outputs.

Use when you have uploaded m/z values from a high-resolution mass spectrometry analysis of a complex sample (e.g., crude oil, natural organic matter) and need to visualize homolog series trends.

Use when you have imported a raw LA-ICP-MS raster image (line-by-line, spot-wise, or ablation-time-aligned format) and need to isolate tissue regions from instrumental background or air before quantifying regional elemental abundance.

Use when after detecting local-maxima in LC-HRMS profile mode datasets and before training or inference with a CNN model for peak classification.

Use when you have a set of target molecules with known molecular formula, main adduct, and experimentally determined retention time boundaries, and you want to extract their chromatographic peaks and isotopologues from LC-HRMS mzML files to generate a ground-truth benchmark for validating.

Use when when you have a 1D signal array (e.g., extracted ion chromatogram, arrival time distribution, or MS1 spectrum intensity profile) and need to identify and quantify overlapping or adjacent peaks with precise position, height, and width estimates.

Use when when you need to parse a domain-specific query language (like MassQL) into an abstract syntax tree (AST), and the query strings must be decomposed into discrete lexical units before syntactic analysis.

Use when you have a mass-spectrometry query string written in MassQL (or similar domain-specific SQL-inspired syntax) that must be converted into structured form for execution. The input is raw, unparsed text containing SQL keywords, MS-specific operators (e.

Use when you need to enable users to express complex domain-specific queries in a natural, succinct syntax—particularly when SQL patterns are familiar but must be augmented with domain assumptions (e.

Use when you have a preprocessed and normalized lipid abundance matrix with batch identifiers and experimental group assignments, and you need to compare lipid levels across treatment conditions (e.

Use when your lipidomics experiment includes spiked internal lipid standards with known absolute concentrations, and you have a data matrix of signal intensities (samples × lipids) from LipidSearch or LIQUID output.

Use when after statistical analysis of lipid abundance data has produced a table of lipid identities, expression measurements, p-values, fold-changes, and condition labels.

Use when when you have a list of lipids identified by different database identifiers (e.

Use when when exporting in-memory generated spectra as MSP-format spectral libraries, you must first map each spectrum record to required MSP fields (NAME, PRECURSORMZ, SPECTRUM) and optional metadata annotations.

Use when you have received raw lipid identification output from LipidSearch or LIQUID in CSV or TSV format containing lipid species names or identifiers, and you need to extract their structural components and map them to standardized LIPID MAPS categories before building a quantitative data matrix.

Use when you have access to a lipidomics library repository (e.g., LipidMatch .csv files) and need to audit or report the total number of distinct lipid species and lipid-type categories present.

Use when when you have downloaded or cloned a lipidomics library repository (such as LipidMatch) and need to audit the breadth of lipid-type coverage to ensure the library meets minimum requirements for your analysis scope (e.g., ≥60 distinct lipid categories).

Use when when you have quantitative lipidomics data (either from Skyline CSV export or numerical matrix format) with sample annotations and a biological grouping variable (e.

Use when when you have lipidomics quantitation data (lipid abundances across samples) that you need to load into a unified, annotated R object for analysis—either from public Metabolomics Workbench studies via API, Skyline mass spectrometry software exports, or a custom numerical matrix with.

Use when you have raw LC-HRMS profile-mode data and need to identify candidate chromatographic peaks before classification or feature extraction.

Use when you have a large collection of mass spectrometry spectra (m/z and intensity pairs) and need to perform rapid similarity-based retrieval or clustering without computing all pairwise distances.

Use when apply log-transformation immediately after loading a raw metabolomics featuredata matrix (metabolite peak intensities as rows=samples, columns=metabolites) and before normalization.

Use when you have low-resolution MS data (e.g., from quadrupole instruments) in vendor format, a list of target compounds with known monoisotopic m/z and retention times, and need to extract per-isotopologue area and intensity values.

Use when before committing to full-scale targeted peak integration across all LC–MS runs in a metabolomics or lipidomics study. Apply this skill when you have a curated list of 5–50+ target compounds with theoretical m/z values and expected retention times, centroided .

Use when you have imported mass spectrometry data in .raw, .d, or mzXML format and need to inspect peak structure, verify instrument performance, or generate baseline visualizations before downstream peak annotation or statistical analysis.

Use when after extracting a feature table from XCMS, MS-Dial, or similar tools, when you possess a reference compound database with known m/z, retention time, and metadata, and you need to assign high-confidence (AnnoLevel 1) compound identities.

Use when you have uploaded peak list data containing m/z values and wish to construct a Kendrick mass plot where alkane homolog series (or other homologous families) are expected to appear as horizontal lines.

Use when when implementing or modifying data replacement methods (e.g., `mz<-`, `peaksData<-`) in a writable MsBackend subclass, or when accepting user-supplied m/z vectors destined for storage in a Spectra backend.

Use when you have a labeled dataset (e.g., mass spectra with molecular structures, SIRIUS 6 fingerprint annotations) that you wish to train a supervised deep learning model on, and you need to estimate generalization performance and reduce variance from a single train–test split.

Use when after generating mzPeak files from prototype implementations (Rust, Python, R, or .NET) or after format conversion, and before integrating files into a mass spectrometry data repository or sharing them with collaborators. Use it when specification compliance is a hard requirement (e.