Claude Skills by HolobiomicsLab

Use when a software package claims to bundle or install a specific number of tools (documented or approximate) but the tool names, versions, and sources are not enumerated in the primary README or documentation.

Use when when you need to reverse-engineer or formally document the computational steps within a closed or under-documented scientific software module—particularly when the software performs in silico generation, enumeration, or filtering of candidate molecular structures and the published paper or.

Use when when you need to extract m/z and intensity peak values from a Spectra object backed by MsBackendMzR or similar on-disk backends; when analyzing subsets of spectra without loading all peaks into memory;

Use when you have Spectra objects in an R environment and need to apply Python MS algorithms (e.g., matchms similarity scoring, spectrum normalization, or filtering) that operate on Python spectral objects, then return results back to R for further analysis or visualization.

Use when when designing or configuring an MsBackend subclass (e.

Use when when designing a new MsBackend subclass or extending an existing backend to support mass spectrometry data import or representation.

Use when your input is an mzPeak archive (ZIP of Parquet files) and you need to recover spectrum signal data (m/z values and intensities) for downstream analysis, visualization, or format conversion. Use this skill when working with profile or centroid mode spectra stored in `spectra_data.

Use when you have a collection of mass spectrometry spectral data (m/z and intensity pairs) that must be indexed for fast neighbor retrieval or similarity search, and you need to trade some precision in similarity matching for substantial gains in query speed and memory efficiency across large.

Use when after converting existing mass spectrometry formats (mzML, vendor formats) into mzPeak using command-line tools or when receiving mzPeak files from external sources.

Use when after converting mass-spectrometry data from an existing format (mzML, mzXML, or vendor-specific formats) into mzPeak using command-line tools or API calls.

Use when you have raw lipidomic and metabolomic spectral data files from a Multi-ABLE barocycler-based concurrent multiomics experiment and need to normalize ion intensities, align retention times and m/z values across samples, and remove noise or low-signal features before statistical comparison.

Use when importing raw or public mass spectrometry spectral data in formats such as MGF, MSP, or mzML that may contain incomplete metadata (e.g., missing instrument type, precursor m/z, retention time), low-intensity noise peaks, or spectra with invalid or inconsistent metadata fields.

Use when when you have mzPeak format spectrum files and need to work with spectrum metadata, intensity/m/z arrays, or precursor information in a tabular, columnar, or vectorized computing environment.

Use when you have generated predicted fragment spectra for a set of compounds using CFM-ID or similar in-silico prediction tools and need to organize these results into a persistent, queryable database that mirrors the schema of an existing online spectral reference (such as the predicted-fragments.

Use when when you have compiled raw mass spectrometry transition data (precursor m/z, product m/z, intensity, retention time, compound IDs) from experiments or external sources, and need to prepare it for ingestion into the EISA-EXPOSOME R Shiny platform for suspect screening.

Use when when you have downloaded fragment records from separate experimental and predicted online databases and need to verify that each fragment can be traced back to a valid compound entry in a reference compound database (e.g., SDF-format DNA adduct compound collection).

Use when you have a Thermo Fisher Orbitrap .raw file and need to recover the intensity profile of a specific m/z value or peptide across the LC separation dimension (chromatogram), particularly when calibrating retention times against internal standards (iRT peptides), performing quality control.

Use when you have a GNPS DBResult file containing spectral library matches with InChIKey identifiers but lacking ClassyFire ontology columns (superclass, class, subclass), and you need to restore this taxonomic context for consensus classification or chemical ontology annotation workflows.

Use when you have large spectral libraries (thousands to millions of spectra) and need to search query spectra against them for peptide identification with tolerance for post-translational modifications.

Use when you have a set of candidate molecular structures from in silico fragmentation (e.

Use when when you have discovered Mass2Motifs from MS2LDA topic modeling and need to automatically annotate them by finding the most structurally similar known spectra in a reference library.

Use when you have a query spectrum and a reduced candidate set of library spectra (via approximate nearest neighbor indexing), and need to search for exact unmodified peptide matches with high confidence before proceeding to variable-mass-shift modification searches.

Use when you have multiple MSP or spectral library files (e.g., one per batch of analytical standards, or organized in a directory structure) that need to be read and merged into a single library object for downstream enrichment (SMILES assignment, RI annotation, write operations).

Use when you have a .msp spectral library file with sparse or incomplete metadata (e.

Use when processing a mass spectrometry dataset (in FragHub JSON format or similar) where duplicate spectral records are suspected or known to exist. The input dataset should already be in a standardized format with computed or retrievable SPLASH keys.

Use when processing heterogeneous mass spectral datasets from multiple open libraries (e.g., MassBank, UNPD, GMD) where structural identifiers, precursor m/z, and adduct information are frequently incomplete or inconsistent across records.

Use when use this skill after normalizing, smoothing, and baseline-reducing MSImagingArrays objects when you need to detect peaks across multiple spectra with consistent SNR-based thresholding.

Use when after peak picking across individual spectra in an MSImagingExperiment, when you need to harmonize peak m/z positions across pixels to account for small shifts in peak location due to instrumental drift, calibration differences, or natural variation.

Use when when you need to retrieve m/z–intensity pairs from a MsBackend-backed Spectra object for visualization, comparison, or processing; particularly when the backend stores peak data separately (e.

Use when when you have a large collection of query high-resolution mass spectra that must be rapidly matched against a spectral library containing modified and unmodified peptides, and you need to reduce computational overhead before approximate nearest neighbor indexing or GPU-accelerated.

Use when after molecular formula assignment has been performed on FT-ICR MS peaks (e.g., by Formularity or CoreMS), and before calculating thermodynamic indices, compound class assignments, or statistical analysis.

Use when you have imported raw mass spectrometry data in formats such as MGF, MSP, mzML, or mzXML and need to clean the spectral data prior to similarity comparisons, metadata validation, or export.

Use when you have 1D MS signal data (extracted ion chromatograms, arrival time distributions, or intensity profiles) and need to resolve overlapping or closely-spaced peaks with accurate position, height, and width estimates.

Use when after filtering and noise removal when you have a cleaned spectrum with m/z and intensity pairs and need to normalize the intensity distribution prior to peptide fragment annotation, spectral library matching, or machine learning-based spectrum analysis.

Use when when preparing query spectra and library spectra for similarity-based matching via dot product scoring, particularly in open modification spectral library searches where you need to match modified query spectra to unmodified library counterparts without the results being biased by.

Use when when you have implemented or obtained a spectral library search tool (e.

Use when you have mzML mass spectrometry data and a list of predicted candidate metabolites (with known mass-to-charge ratios and retention times) for a drug of interest, and you need to determine which candidates actually appear in the observed spectra rather than treating all predictions as.

Use when you have an unknown query spectrum suspected to carry a post-translational modification and need to match it against a library of unmodified reference spectra.

Use when when you have raw mass spectra in an MSImagingArrays object and need to decide between Savitzky-Golay and Gaussian smoothing methods based on their effects on peak shape fidelity and baseline noise. Apply this skill when peak preservation is a priority (e.

Use when when you need to prepare mass spectra and molecular structures for joint modeling in a BART or transformer-based sequence model, and you lack a unified representation scheme that allows both modalities to be encoded and decoded without collision or information loss.

Use when when you have a collection of normalized mass spectrometry spectra (peak intensities or binned m/z representations) and seek to enrich neural network training signals for metabolite identification by discovering latent spectral patterns.

Use when when you have a set of mass spectra (query or library) that need to be searched against a large spectral reference database, and you want to use fast approximate nearest neighbor methods rather than exhaustive pairwise comparisons.

Use when after applying any sequence of spectrum preprocessing operations (set_mz_range, remove_precursor_peak, filter_intensity, scale_intensity) to an MsmsSpectrum object, to confirm that the resulting arrays fall within specified m/z windows, intensity bounds, and peak count limits before.

Use when you have mass spectrometry data in a Pandas DataFrame with columns for m/z and intensity (spectrum), retention time and intensity (chromatogram), or drift time and intensity (mobilogram), and need to render 1D traces as static or interactive plots for exploratory analysis, quality control.

Use when you have pre-computed Word2vec embeddings of mass spectra and need to retrieve the k most similar spectra from a library of hundreds of thousands to millions of candidates.

Use when you have raw mass spectrometry spectra (peak lists or intensity arrays) that must be fed into a pre-trained deep learning model for substance classification (e.g., PS²MS for NPS detection).

Use when processing heterogeneous mass spectrometry libraries (e.g., from OMSLs) where chemical identifiers are unevenly populated across records.

Use when when you have paired tandem MS spectra and corresponding molecular structures (SMILES or fingerprints), and you want to build a retrieval system for metabolite structure annotation that can rank candidate structures for novel spectra by embedding similarity rather than spectral matching.

Use when you have loaded raw PSM search results from a proteomics search engine (e.g., MaxQuant, MSGFPlus, Sage) and need to prepare them for multi-rank rescoring in MS²Rescore.

Use when when ingesting heterogeneous MS spectral data from multiple open-access libraries (OMS libraries) where metadata completeness and correctness are uncertain.