Claude Skills by HolobiomicsLab

Use when after PuInc_seeker has identified putative incorporations in a stable-isotope-labeled LC/MS dataset (e.g., CELL_Glc12 unlabeled vs. CELL_Glc13 labeled samples).

Use when when constructing a reference mass-matching framework for untargeted metabolomics or isotope-tracing LC-MS data, before pattern-matching observed features to isotopic and adduct variants.

Use when after filtering LC-MS features by statistical significance (e.g., p-value < 0.01) and you wish to group features that represent the same metabolite at different isotopologue states.

Use when you have GC-CI-MS or LC-MS data in mzML format targeting a defined set of compounds with known monoisotopic mass, retention time, and chemical formula, and you need per-isotopologue (M+0, M+1, M+2, etc.) quantification metrics (area, maxo intensity, abundance).

Use when you have preprocessed, statistically significant LC-MS features (from multiple assays or a single assay) and need to group features that represent the same metabolite in different isotopic labeling states.

Use when when you need to understand how a Java application routes input data to processing modules based on declared data types, conditionally branches on instrument or format types (e.

Use when you have grouped LC-MS features into empirical compounds with inferred molecular formulas and adduct assignments (via khipu), and you need to assign candidate metabolite identities at Level 4 annotation depth by matching against curated reference libraries.

Use when when you have raw LC-MS data in mzXML format (or vendor formats convertible via MS-Convert) and need to extract metabolic features as the first major step of untargeted metabolomics analysis. Choose MS1 peak picking for DDA/full-scan data;

Use when when your project JSON document contains public identifiers (genome IDs, biosample accessions, etc.) that lack human-readable or linked metadata, and you need to populate those fields programmatically before storage or publication to enable full-text search, validation, or cross-linking.

Use when after generating a structured JSON result file from a prior computational step (e.g., gensim LDA model output in myexp.ldaresult.json format) and needing to store it in PostgreSQL for web application access, visualization, or further analysis.

Use when you have completed the MS2LDA LDA modeling phase and possess motifset.json or motifset_optimized.json files containing inferred Mass2Motifs.

Use when when a project JSON record contains a resolvable public identifier (genome accession, biosynthetic gene cluster ID, etc.) but lacks the corresponding human-readable or standardized metadata field (organism name, cluster description).

Use when after extracting header metadata from a Thermo Fisher Scientific .

Use when when you have completed a computational step (e.g., corpus feature extraction or gensim LDA inference) and need to persist the result for later retrieval, pass it to a separate process (e.g., Celery task), or load it into a database.

Use when after pre-annotation grouping (e.g., via khipu) has assigned features to empirical compounds, or when exporting feature tables and metadata from asari for downstream analysis in MetaboAnalyst or custom R/Python workflows.

Use when after serializing empirical compound collections to JSON format via khipu's build_empCpds command, or before ingesting empCpd.

Use when when you have raw LC-MS/MS spectral data in .

Use when when fitting a multi-block PLS discriminant model on multi-assay LC-MS metabolomics data and you need to determine the number of latent variables to retain without overfitting.

Use when after cluster-based filtering has produced a set of candidate KEGG compounds for each feature cluster in untargeted LC-MS data, and you need to rank these candidates by their metabolic plausibility using network context rather than mass accuracy alone.

Use when you have raw LC-MS peak intensity data with mass-to-charge ratios and need to match them to known metabolites. This skill must be applied before the matching stage if you are working with a KEGG database (KeggDB or sample.keggDB) and require a precomputed adduct/fragment lookup table.

Use when you have untargeted MS2 spectral data (in MS2MP-compatible format) and need to assign KEGG pathway annotations to unknown metabolites.

Use when you have a feature list from LC- or GC-HRMS analysis (with m/z, retention time, and exact mass columns) and you want to detect homologous series of PFAS compounds that repeat by CF₂ mass increments (typically ≈34 Da).

Use when when extending an existing neural network class (e.g., SiameseModel) that lacks user-configurable regularization, and you need to prevent overfitting on moderate-sized training datasets (e.

Use when when you have a training set of MS2 spectra with known chemical structures (e.

Use when you have a set of ions detected in LC-MS data that are suspected to derive from the same neutral compound via different isotope and adduct patterns. Use this skill after ions have been matched to isotope and adduct patterns and assigned to grid positions (isotope row and adduct column).

Use when you have raw LC-MS data (mzML, NetCDF) from multiple runs that require sequential feature detection, alignment, quantification, and optional filtering (e.g., blank exclusion, QC reproducibility, retention-time outlier removal) before spatial mapping or annotation.

Use when you have untargeted metabolomics data (MS/MS spectra) and need to annotate metabolites at scale.

Use when after completing all per-sample annotation steps (molecular networking, ISDB/spectral matching, SIRIUS/CSI:FingerID, and compounds metadata enhancement with Wikidata IDs and NPClassifier ontology).

Use when designing a metabolite annotation workflow that must simultaneously leverage established biochemical knowledge (pathway databases, reaction networks) and experimental evidence (mass spectrometry feature similarity, co-occurrence patterns).

Use when when preparing mass tracks for retention-time (RT) alignment across multiple LC-MS samples.

Use when you have (1) genomic data from a Streptomyces or other RiPP-producing organism in raw FASTA format or annotated GenBank format, (2) high-resolution LC-MS/MS spectra in centroided MGF, mzML, mzXML, or mzData format, and (3) a known or predicted lantibiotic core peptide sequence you wish to.

Use when you have two or more MS/MS fragmentation spectra (with precursor m/z, fragment m/z values, and intensities) and need to identify which fragment ions match across spectra while quantifying the statistical confidence of those matches.

Use when you have multiple competing spectral similarity scoring methods (e.

Use when you have a query mass spectrum (or a metabolite reference spectrum from public data) and need to search it against a large-scale spectral repository (≥billions of spectra, e.g., GNPS library) where execution time and resource efficiency are critical.

Use when you have preprocessed mass spectra (peak-filtered, metadata-cleaned) in supported formats (mzML, mzXML, msp, MGF, JSON) and need to compare all-pairs or many-to-many spectrum similarity to identify related compounds, build spectral libraries, or perform large-scale library searching.

Use when when you have preprocessed MS/MS spectral data (filtered, noise-reduced, with neutral losses extracted) and need to discover recurring fragmentation patterns across a spectral dataset.

Use when you have a preprocessed corpus of mass spectrometry spectra converted to bag-of-fragments format (with neutral losses extracted and noise filtered), and you seek to discover recurring fragmentation patterns or substructures that characterize multiple spectra without prior knowledge of the.

Use when you have raw LC-HRMS metabolomics data in .mzML or .abf format and need to perform peak detection, feature alignment, and metabolite annotation in a reproducible, containerized environment.

Use when you have processed the same set of untargeted LC/HRMS files (mzXML, mzML, or netCDF format) with two or more peak-picking tools and need to validate which tool produces higher-quality peaklists for organic small molecules.

Use when you have LC-HRMS raw data files (.mzML or .abf format) from metabolomics experiments and need to extract, align, and annotate features in a reproducible manner across multiple computational environments.

Use when when you have statistically significant features from multi-assay LC-MS metabolomics data (with m/z and retention time annotations) and need to group features that may represent the same compound ionized as different adducts (e.g., [M+H]⁺ vs. [M+Na]⁺).

Use when when you have paired LC-MS measurements from labeled and unlabeled samples of the same analytes, and you need to isolate the true isotopic labeling contribution by removing confounding signal from naturally occurring isotopes and tracer isotopic impurity.

Use when you have vendor-format LC-MS acquisition files (.raw, .d, .ms) from instrument runs and need to set up an end-to-end data quality control system that converts proprietary formats into open mzML, processes spectral data, and surfaces QC failures in real time.

Use when you have raw mzML files and corresponding feature tables (CSV format, mzmine-formatted) from untargeted LCMS experiments, and you need to convert them into uniformly-shaped peak matrices (2 × 120 per peak: margin + signal regions) as input for neural network classification of MS1 peak.

Use when before launching TARDIS peak detection on a new LC–MS dataset or target compound list. Apply this skill when you have raw MS data files in vendor formats (e.g., .raw, .d) and/or a spreadsheet-based target list (.xlsx or .

Use when when beginning an untargeted LC-MS metabolomics study and need to assemble a cohort of mzML files for processing; particularly when establishing performance baselines across sample counts (10, 50, 100+ samples), validating reproducibility, or preparing data for publication.

Use when after executing TARDIS in screening_mode = TRUE on centroided .mzML files with a defined target compound list.

Use when you have two peak-picked, conventionally aligned LC-MS metabolomics datasets (e.

Use when you have centroid mzML files from LC-MS experiments (converted from Thermo .raw or other vendor formats) and need to identify and quantify individual chemical features across multiple samples.

Use when after chromatographic peak detection on preprocessed LC-MS data, when you have hundreds or thousands of individual m/z × retention-time peaks and need to associate them into biologically meaningful feature groups.