Claude Skills by HolobiomicsLab

Use when when you have a set of small molecule structures (as SMILES or molecular graphs) and need to predict their elution order in RPLC systems with eluent pH around 2.

Use when when you have retention order predictions from multiple ensemble members (e.g., ROASMI_1 through ROASMI_5) for a set of candidate compounds and need to assign a per-compound uncertainty score that reflects how consistently the ensemble members rank that compound relative to others.

Use when you have observed compounds (from LC-MS, GC-MS, or spectroscopy) and a set of predicted metabolite structures from BioTransformer, and need to assign identities to the observed compounds by matching their experimental features (mass-to-charge ratio, retention time, spectral signature) to.

Use when when you have seed metabolite structures (SMILES or MOL format) from metabolomics data and a curated biotransformation rule database (each rule specifying reactant SMARTS, product SMARTS, and transformation type), and you need to systematically enumerate plausible biotransformation.

Use when when you have a mass spectral library (MSP format) that lacks SMILES annotations but is paired with a folder of MOL structure files (from Lib2NIST export or similar source).

Use when when you have a dataset of molecular structures encoded as SMILES strings that will be processed downstream (e.

Use when you have LC–QTOF mass spectra from real environmental or biological samples (e.g., wastewater, complex mixtures) and need to assign molecular structures to spectra where traditional library matching fails or reference compounds are unavailable.

Use when you have generated a TPs object (via generateTPs) containing transformation products with structural information and need to export these structures for MetFrag database creation, suspect screening list construction, or chemical similarity filtering.

Use when you have a set of anchor feature pairs (m/z and retention time values) from two disparately-acquired LC-MS datasets and need to fit a smooth, nonlinear RT correction spline.

Use when you have multiple mzML or mzML.gz files from LC-IMS-MS/MS instruments and need to apply DEIMoS feature detection, alignment, and calibration operations in a reproducible, traceable manner.

Use when after composite map peak detection has generated a full unfiltered peak list with SNR values computed for each candidate peak.

Use when you need to verify the scope and completeness of a software platform's analytical capabilities—particularly when the project claims to support multiple input modalities (e.

Use when you need to assess whether a newly developed FT-ICR MS pipeline (e.

Use when when a tool claims to be 'scalable' or 'performance-conscious' but lacks published performance benchmarks, or when you need to confirm that runtime and memory scale linearly (or predictably) with sample count before deploying the tool on large LC-MS datasets (e.g., >100 samples).

Use when when you need to verify that a specific data transformation (e.g., precursor m/z zeroing, feature scaling, or field masking) is applied consistently across multiple execution workflows (training, evaluation, inference) in a codebase.

Use when you have a large collection of MS/MS spectra (hundreds of thousands to millions) that need to be clustered, you have already constructed nearest neighbor indexes on low-dimensional spectrum vectors (via feature hashing), and you need to compute only the relevant pairwise distances between.

Use when you have computed a sparse pairwise distance matrix from nearest neighbor indexes (containing only cosine distances between neighboring spectra, not exhaustive pairwise comparisons) and need to partition spectra into groups of similar ions or peptides.

Use when when you have tandem mass spectra (mz/intensity pairs with precursor m/z) and need to train an interpretable model (e.

Use when clustering large collections of high-resolution MS/MS spectra (thousands to millions) and you have already converted spectra to low-dimensional vectors via feature hashing.

Use when when converting high-resolution tandem mass spectra to vectors for clustering or similarity searching, you must decide whether to output sparse or dense vectors. Use this decision point after binning spectra into mass bins but before constructing nearest-neighbor indexes.

Use when after LC-MS feature detection, alignment, quantification, and optional filtering/normalization are complete, and you have intensity values for molecular features that correspond to spatial positions (e.g., tissue coordinates, imaging pixel locations).

Use when after identifying candidate parent–adduct mass-difference pairs (via massdiff, histogram binning, and adductMatch), apply this skill to discriminate true molecular adducts from coincidental mass matches.

Use when your spatial metabolomics dataset contains raw m/z features (e.g., from MALDI-MS imaging or LC-MS/MS) without metabolite annotations, and you have selected a reference database and adduct type appropriate for your ionization mode and biological sample.

Use when you have discovered Mass2Motifs or other fragmentation pattern representations via LDA and need to generate vector embeddings to query a reference motif database (MotifDB) for structural annotation candidates.

Use when after discovering Mass2Motifs via LDA on preprocessed MS/MS spectra, use this skill to assign putative substructure annotations by matching each motif's fragmentation pattern against a pre-indexed spectral library using learned spectral embeddings.

Use when when loading MS/MS spectra from MGF files for FIDDLE model training or evaluation, or when preparing spectrum–annotation pairs for rescore model data augmentation (TCN train/test sets).

Use when you are creating a new backend to expose MS data (m/z, intensity, retention time, and other spectral properties) from a specific storage format or data source (e.g., mzML files, SQL databases, in-memory matrices, or spectral libraries) to Spectra objects.

Use when when you have downloaded a GNPS molecular networking archive (GNPS1 or GNPS2 workflow output) and need to reconstruct spectral records for integration with genomic data (BGCs, antiSMASH results) or for computing molecular family links and spectral similarity scores.

Use when you have mass spectrometry spectral data (m/z and intensity pairs, precursor m/z, MS level, and metadata) in R memory or in a file format (mzML, mzXML, CDF, MGF, MSP), and you need to organize it into a queryable, processable object before performing spectral similarity calculations.

Use when when you have extracted and concatenated MS/MS spectra from multiple replicates for a set of metabolomic features (stored in a preprocessed list), and need to apply intensity-based filtering (e.

Use when when building a new mass spectrometry data backend for the Spectra package that must store and serve multiple spectra with their associated m/z and intensity peaks. Use this when you need a backend that combines efficient metadata lookup (via data.

Use when when you have two MS/MS fragmentation spectra (with precursor m/z values and fragment ion lists) and need to establish correspondence between their fragment ions beyond simple pairwise comparison.

Use when you have paired MS/MS spectra (known compound and its structural analog) with assigned precursor m/z, charge, and SMILES; you want to quantify which parts of the molecular structure could have undergone modification by scoring peak alignment quality.

Use when after running GNPS molecular networking, SIRIUS compound identification, or ISDB in silico annotation on LC-MS/MS data, when you have provisional annotations for features but need to filter them to retain only high-confidence matches before calculating novelty scores, detecting chemical.

Use when you have run two or more annotation pipelines on the same MS/MS spectral dataset (e.

Use when you have a raw or preprocessed peak table from tandem MS/MS data (e.g., from Progenesis QI, MS-DIAL, or Bruker Metaboscape) and observe features that may represent detector artifacts, incorrectly merged/split isotopic patterns, or sample carryover contaminants (e.

Use when after performing spectral library matching of mass spectrometry peaks against a fragmentation library (e.

Use when after running spectral networking on tandem MS data and obtaining a network graph, when you need to assess which spectra cluster together, determine cluster representatives, and propagate RiPP identifications across clusters at distance 1 or 2 to enlarge the set of identified RiPPs beyond.

Use when you have computed a sparse pairwise distance matrix from MS/MS spectra (via nearest neighbor indexing) and need to partition spectra into homogeneous clusters—typically when clustering bottom-up proteomics data with the goal of grouping spectra from the same peptide sequence or when you.

Use when after extracting and filtering top-TIC spectra for a given feature (e.

Use when you have a collection of tandem mass spectrometry spectra in mzML or similar format and need to prepare them for LDA-based motif discovery.

Use when you have completed a ViMMS simulation run or processed real LC-MS/MS data and need to quantitatively assess whether one DDA controller (e.g., WeightedDEWController with exclusion) outperforms another (e.g., TopNController) in terms of spectral coverage and signal recovery.

Use when you have quantification tables (with feature IDs and abundance values), metadata tables (with sample annotations), and separate spectral data files (e.g., MS/MS spectra or fragmentation patterns), and you need to integrate them into a unified input for msFeaST.

Use when you have completed msFeaST pipeline preprocessing and generated a JSON output file (dashboard_data.

Use when after running RAMClustR clustering on XCMS-processed metabolomics data, export spectral data when you need to share clustered spectra with external annotation software (MSFinder, Sirius), perform spectrum matching against reference databases, or prepare results for collaborative analysis.

Use when you have loaded a full set of MS scans (e.g., all MS1 scans from readAllScanMS1 in Aerith) and need to restrict analysis to a specific retention-time window (e.g., 9–10 min with 0.

Use when you have raw tandem mass spectrometry data in one or more of the vendor formats (MGF, mzML, or msp) and need to apply unsupervised topic modeling (LDA-based motif discovery) or comparative fragmentation analysis.

Use when when you have raw LC-MS/MS data files in mzML, mzXML, or vendor-specific formats and need to load them into a Java-based mass spectrometry analysis framework for downstream spectral analysis, fragmentation tree computation, or metabolite identification.

Use when you have raw mass spectrometry data in one or more supported formats (mzML, mzXML, msp, metabolomics-USI, MGF, or JSON) and need to convert it into a standardized in-memory representation that can be processed, validated, and compared using matchms.

Use when when you have raw mass spectrometry data in one or more of the six supported file formats (mzML, mzXML, msp, metabolomics-USI, MGF, JSON) and need to transform it into matchms spectrum objects for processing, cleaning, or similarity comparisons.