Claude Skills by HolobiomicsLab

Use when after LC-MS feature detection, alignment, and quantification are complete and you have a feature table with m/z and retention time attributes. Use this skill when you have access to a reference list of molecules of interest (e.

Use when after initial retention-time-based feature grouping (e.

Use when you have aligned feature tables (CSV format) paired with MS2 spectral data (MGF or mzML files) and need to compare chemodiverse samples with poor feature overlap or strong retention-time shifts across different LC methods or mass spectrometer technologies (e.g., Orbitrap vs. Q-ToF).

Use when after Blueshift or Gravity processing has produced a feature abundance table with annotations, but before final reporting or integration with sample/injection metadata.

Use when after feature extraction from XCMS or MS-Dial when you have a feature intensity table (samples × features), a feature info table with m/z and retention time measurements, and access to a reference compound database with known m/z, retention time, and compound metadata.

Use when your input is a feature intensity table (CSV or R data frame) with features as columns and samples as rows, and you have accompanying sample metadata (batch identifiers, QC/study sample labels, run order, sample phenotypes, collection dates).

Use when after loading an MZmine3-exported feature quantification table from non-targeted LC-MS/MS data when your experiment includes blank (negative control) samples and you need to remove features attributable to contamination or instrument background before downstream statistical analysis.

Use when you have detected feature tables from multiple LC-IMS-MS/MS samples and need to establish a unified feature catalog in which each row represents a distinct molecular entity observed across one or more samples, with harmonized m/z, drift time, and retention time coordinates.

Use when after completing feature detection, alignment, and optional filtering (blank subtraction, QC reproducibility, feature occurrence thresholds) in MZmine2 or Optimus, and you need to prepare the feature table and MS/MS spectra for GNPS-based molecular networking, bioassay integration, or.

Use when when you have a quantitative feature table (peak intensities across samples) and need to isolate molecular features that show differential abundance between defined sample groups within a specified fold-change range.

Use when transitioning feature intensity data between pipeline stages (e.

Use when you have an aligned feature table from untargeted LC-MS with missing intensity values (NA or zero entries) for features that are present in some samples but fell below detection threshold in others.

Use when after retention-time and m/z-based peak alignment has been completed across a cohort of LC-MS samples, and you need to create a unified quantitative matrix for statistical testing, multivariate analysis, or annotation workflows.

Use when after retention-time correction and alignment of centroided mzML or mzXML LC-MS files across a sample cohort.

Use when when you have MS1 feature tables from heterogeneous sources—e.g., XCMS peak detection output mixed with vendor software (MS-DIAL, MZmine2) results—and need to merge them into a single, format-normalized table for ISFrag analysis.

Use when when processing a metabolomics feature table through multiple sequential transformations (e.g., imputation, normalization, batch correction, annotation) and you need to track which version of the table is being used at each step.

Use when after Asari completes feature detection and produces multiple feature table variants (full and quality-filtered preferred tables) from centroid mzML files within a PCPFM experiment.

Use when you have peak/feature tables from one or more peak picking tools (MZmine, XCMS, MS-DIAL, Compound Discoverer) and need to ingest them into LipidMatch or combine results from multiple tools in a single lipidomics workflow.

Use when you have extracted volatile organic compound (VOC) features from individual breath samples (mzML or mzXML files) and wish to consolidate multiple per-sample feature tables into a single aligned feature table, or you need to programmatically access feature metadata (m/z, intensity, scan.

Use when when you have a feature intensity matrix (samples × compounds) from untargeted LC–MS/MS or GC–MS analysis and accompanying sample-type metadata (blank, curve, QC, unknown classifications), and you need to remove features with high measurement variability, low QC detection rates, high blank.

Use when after peak quality filtering has been applied to a composite map peak detection output using SNR (>2), goodness-of-fit (peakshape > 0.5), minimum peak height (default 1e5), and prominence (≥20% of peak_height) thresholds.

Use when you have feature tables from external metabolomics software (MS-DIAL, XCMS, vendor tools) in CSV format and need to integrate them into JPA for cross-sample alignment and metabolite annotation.

Use when you have an untargeted metabolomics feature table (m/z, retention time, p-value from statistical test) but lack comprehensive metabolite identifications or MS/MS annotations.

Use when you have millions of MS/MS spectra in mzML, mzXML, or MGF format and need to identify similar spectra for clustering, but exhaustive pairwise cosine-similarity computation would be prohibitively slow.

Use when when you have extracted concatenated MS/MS spectra for multiple features from replicate mzML files and need to verify that a TIC-based filtering step (e.g., top x% TIC extraction) reduces per-feature spectrum counts to expected target levels.

Use when you have preprocessed molecular structures as fixed-length feature vectors and need to establish a fair-comparison baseline model for tandem mass spectrum prediction.

Use when you have experimental retention times measured on a source chromatographic method and want to predict RTs on a target chromatographic method, but possess only a small set (10–100) of molecules with ground-truth measurements on both methods.

Use when you have generated a peak table or feature list from MZmine, XCMS, MS-DIAL, or Compound Discoverer and need to ingest it into LipidMatch for lipid identification.

Use when when integrating MSConvert into an automated LC-MS QC workflow and you need to confirm that vendor acquisition files are properly converted to mzML format with intact spectral metadata. Apply this skill after each MSConvert invocation or when QC results appear incomplete or anomalous (e.

Use when after executing MassQL queries on mass spectrometry data that produce tabulated results (e.g., MS1 or MS2 scan metadata, peak intensities, retention times), and you need to persist those results for archival, sharing, or downstream statistical analysis.

Use when you have raw MS data files from one or more instrument vendors (Agilent, Bruker, Thermo Fisher, or mzML-formatted) and need to convert them to a vendor-agnostic HDF5-based storage format for downstream software development, machine learning, or cross-platform data access.

Use when you have peak/feature tables from one or more of MZmine, XCMS, MS-DIAL, or Compound Discoverer and need to ingest them into LipidMatch for lipid identification. The input files are in tabular format (CSV, TSV, or Excel) and their upstream tool origin may be unknown or mixed.

Use when when processing MS spectral data from multiple open mass spectra libraries (OMSLs) in mixed formats (MSP, MGF, JSON, CSV), especially when source data exhibits missing fields, malformed entries, inconsistent adduct representations, or non-standard format variants that may cause silent.

Use when after completing an Environment simulation or replay with scan-level MS2 acquisition control, and evaluation data has been collected in memory.

Use when you have a validated ReDU sample-information metadata table (gnps_metadata.tsv) loaded from a MassIVE accession, and you need to partition public MS/MS files into multiple analysis cohorts by one or more sample attributes.

Use when you have MZmine-exported LC-MS/MS data (MGF spectra and CSV metadata files) in both positive and negative ionization modes and need to execute the full MolNotator pipeline from duplicate filtering through dereplication and network generation.

Use when when preparing to read Thermo Fisher Scientific .raw files using rawrr functions (readFileHeader, readSpectrum, readChromatogram, readIndex), or when retrieving cached assembly paths for the wrapped RawFileReader dependency.

Use when after invoking the saveAnnotations function on a MetaboAnnotatoR annotations object to confirm that all four expected output file types (global results file, ranked results file, per-feature ranked spectra PDFs, and pseudo-MS/MS MGF file) have been written to the output directory without.

Use when you are preparing to reuse public tandem MS data from MassIVE via ReDU and need to partition files by sample metadata (e.g., organism, tissue type, extraction method, ionization source, pre-MS separation) into groups for co-analysis.

Use when after applying one or more mpactr filters (mispicked, group, cv, insource) to a feature table, you need to quantify the distribution of ions by their pass/fail status across filters to understand filtering impact, identify potential over-filtering, or communicate QC results via treemap or.

Use when after applying a filter function (filter_mispicked_ions(), filter_group(), filter_cv(), filter_insource_ions()) to an mpactr object, use this skill to inspect and document which features were retained versus removed.

Use when after applying a sequence of mpactr filters (filter_mispicked_ions, filter_group, filter_cv, filter_insource_ions) to an LC-MS/MS peak table in Progenesis or MS-DIAL format, call qc_summary() to obtain a structured report of per-ion filtering outcomes and use this skill to understand.

Use when after loading a specXplore session data object file from the hard drive and instantiating a dashboard session layer with it, validate that the architecture layer has initialized without errors and that the interactive dashboard is responsive to user input.

Use when when implementing or validating a lossy numeric codec for mass-spectrometry data (e.g., MSNumpressCoder in OpenMS). Specifically: after implementing both encoder and decoder, before shipping to production, or when comparing alternative compression schemes.

Use when when you have (1) LC-MS normalized intracellular metabolite abundance data across multiple cell lines or samples, (2) a constraint-based metabolic model with stoichiometric coefficients, and (3) a need to quantify metabolic control through substrate availability independently of enzymatic.

Use when when you have a quantified peak table (LC-MS feature intensities) with sample metadata assigning samples to discrete groups (e.

Use when you have generated a lipid spectral library (with lipid identities, adducts, m/z values, and fragmentation patterns) and need to export it for downstream mass spectrometry analysis on either an Orbitrap (via Excalibur DDA) or via Skyline's transition-based workflow.

Use when when training or validating a deep learning model for molecular formula prediction from tandem MS/MS spectra, use this metric to track whether the model's predicted formula (including hydrogen atoms) exactly matches the annotated ground-truth formula.

Use when when preparing multi-formula MS/MS training data for a rescore model, if the raw positive examples show extreme imbalance (some formulas represented by hundreds of spectra while others have only a few).

Use when you have m/z values from mass spectrometry imaging (or similar MSI experiments) and need to assign molecular formulae to them. This is especially valuable when working with spatially-resolved metabolomics data where annotation precision lags behind traditional LC-MS approaches.