Claude Skills by HolobiomicsLab

Use when you have a formula-assigned dataset from FT-ICR MS (or other compound-annotated mass spectrometry) and you want to characterize molecular transformations and their co-occurrence patterns—particularly in studies of DOM reactivity, fermentation, or oxidative treatment of organic mixtures.

Use when when you need to generate synthetic MS/MS spectra with chemical noise for validating denoising performance, benchmarking library matching algorithms, or testing edge cases where clean reference spectra exist but must be augmented with realistic contaminants.

Use when you have calibrated m/z peak lists, configured adduct transformations (e.g., [M+H]+, [M+Na]+, [M-H]−), and need to annotate peaks with molecular formulae from KEGG, PubChem, or custom databases.

Use when you have an observed m/z value from spatially-resolved mass spectrometry imaging and need to assign one or more plausible molecular formulae with confidence metrics.

Use when use this skill after training or fine-tuning a chemical formula transformer model on annotated tandem MS/MS spectra, when you need to measure whether the model's ranked formula candidates match ground truth.

Use when you need to generate a set of candidate chemical formulas for LC-MS/MS simulation—specifically when you want to populate a virtual mass spectrometer with realistic chemical structures drawn from a reference database (HMDB) or a uniform m/z distribution, and you need to apply m/z filtering.

Use when when preparing MS/MS spectral training data where: (1) the initial TCN train/test split contains imbalanced positive and negative examples, (2) certain molecular formulas are over-represented in the positive class, (3) you are training a Siamese architecture rescore model that requires.

Use when you have tandem mass spectra (MS/MS) with unknown precursor formulas and need to rank chemical formula candidates conditioned on observed fragment m/z values and precursor mass. Use this skill when fragmentation tree computation (e.

Use when you have raw LC-MS fractional abundances of isotopologues (FAM) from a stable isotope labeling experiment (e.

Use when you have replicate MS/MS spectra with labeled fragment recurrence frequencies and need to select an optimal frequency threshold (beyond the default 0.1) that maximizes spectral quality metrics while minimizing false positive noise.

Use when after extracting raw MS/MS spectra from mzML files but before consensus spectrum generation, when you observe high-resolution fragment lists where nearby peaks (within a specified mass tolerance, typically 0.

Use when you have centroid-mode LC-MS AIF chromatograms processed through xcms and RAMClustR, a feature table with target m/z and retention time values, and access to fragment libraries (e.g., LipidPos for lipids).

Use when when preparing tandem MS/MS data for spectral alignment and similarity comparison, particularly when you have loaded raw fragmentation spectra and need to extract and quantify mass difference patterns that capture the fragmentation process.

Use when you have peak-picked experimental MS/MS data (m/z, retention time, intensity) from UHPLC-HRMS/MS instruments (Orbitrap or Q-TOF from Agilent, Bruker, SCIEX, or similar vendors) and need to annotate detected features with specific lipid identifications using in-silico fragmentation.

Use when when comparing experimental spectra to reference library spectra and fragment ion m/z values show systematic drift or measurement noise that could distort neutral loss peaks or cosine similarity scores.

Use when denoising MS/MS spectra and you have: (1) a precursor ion with measured m/z and known molecular formula (from SMILES or direct formula input), (2) an adduct type ([M+H]+, [M+Na]+, etc.), (3) a list of fragment ions with observed m/z values, and (4) a need to distinguish chemically valid.

Use when you have a tandem mass spectrum (MSMS) of a known or hypothesized peptide, along with its ProForma 2.

Use when you have two tandem mass spectra (query and reference) with precursor m/z and fragment ion peaks, and you need to identify which fragment ions correspond between them to assess spectral similarity, detect structural variants, or validate compound identifications.

Use when immediately after loading raw MS/MS spectra from .mgf, .msp, or .mzML files, before generating the bag-of-fragments corpus or extracting neutral losses.

Use when when you have an experimental MS/MS spectrum (centroid mode) and need to convert it into a scored fragment library entry, or when you must rank candidate fragments by confidence before performing spectrum-to-spectrum matching in metabolite annotation workflows.

Use when after pointwise correlation analysis and exact mass difference refinement have identified candidate ion-species pairs that may share a common analyte origin.

Use when when building a comprehensive lipid fragment ion library covering all chain composition and positional isomer variants (e.g., 168.6 million entries).

Use when you have an tandem MS spectrum with unidentified peaks and a known or hypothesized peptide sequence (in ProForma 2.0 format, including post-translational modifications).

Use when when you have a molecular structure (SMILES, InChI, or chemical formula) and need to predict its tandem mass spectrum for structural elucidation or mass spectrometry validation.

Use when when implementing fragment ion annotation in proteomics workflows and needing to determine whether neutral loss annotation (e.g., H2O: -18.010565, NH3: -17.026549) should be enabled to maximize peak interpretation.

Use when after denoising MS/MS spectra at multiple frequency thresholds and matching each thresholded spectrum against a -matching reference spectrum.

Use when after generateComponents with algorithm='tp' has tentatively paired parent features with TP candidates based on retention time and spectrum similarity, and when formula annotations are available for both parents and candidates.

Use when you have MS/MS spectra with assigned precursor formulas and need to annotate the chemical composition of individual fragment peaks for metabolite structure elucidation or fragmentation pathway analysis. Apply this skill when you want to avoid external fragmentation tree computation (e.

Use when you have a list of fragment peak m/z values and intensities from tandem MS/MS data and need to assign chemical subformulae to each peak for downstream formula ranking or structure inference.

Use when after extracting and grouping fragments from top x% TIC-filtered replicate spectra for a given feature, to quantify which fragments consistently appear across replicates. Use this when you have multiple MS/MS spectra for the same precursor (e.

Use when you have preprocessed mass spectrometry fragmentation data (neutral losses and fragment masses extracted and noise-filtered) and want to discover hidden structural motifs across a spectral dataset in an unsupervised manner.

Use when when you have an experimental MS/MS spectrum (query spectrum as m/z–intensity pairs) and need to identify the compound by comparing its fragmentation pattern to a spectral library.

Use when you have tandem mass spectra for compounds with known binary or categorical molecular properties (e.

Use when you have a collection of MS/MS spectra (≥2 spectra) and wish to identify fragmentation signatures common to subsets of those spectra.

Use when after converting a decision tree path into a MassQL query string, before deployment to production mass spectrometry workflows.

Use when when you have detected m/z values from LC-IM-MS/MS that match a candidate CCS prediction database but require structural confirmation. Apply this skill to disambiguate isomers (e.

Use when you have raw or peak-detected mass spectrometry data (mzXML, mzML, or netCDF format) from untargeted metabolomics or exposomics studies and need to separate composite fragmentation spectra into individual constituent spectra for annotation.

Use when you have two MS/MS spectra (precursor m/z and fragment ion lists) and need to identify the non-overlapping set of fragment ion alignments. Use this skill when: (1) you want to move beyond monotonic alignment to capture complex ion relationships;

Use when you have extracted a chemical mixture from a real mzML acquisition (e.g., Beer1pos), simulated the same chemicals through ViMMS using a chosen controller (e.

Use when when you have raw or processed FT-ICR MS peak-abundance .

Use when you have FTICR-MS direct injection (mzML) data with identified chromatographic peaks and need to correct systematic m/z bias.

Use when you have received raw FT-ICR transient data from Bruker Solarix or ThermoFisher instruments and need to perform signal processing, apodization, calibration, or molecular formula assignment in CoreMS. The data must be in native vendor format (.d directory with ser/fid files, or .

Use when you need to assess MS1-level ionization efficiency, peak detection sensitivity, and chromatographic separation without the overhead of MS/MS fragmentation. Use it to benchmark full-scan detection across chemically diverse samples (e.

Use when when you have Spectra objects in R and need to apply Python MS library functionality (spectral similarity scoring, filtering, normalization) without leaving the R environment, or when you want to create custom hybrid workflows that leverage both R and Python MS packages within a single.

Use when you have an untargeted metabolomics feature table (m/z and retention time columns) and a statistical test result (p-value) per feature, but lack confident metabolite identifications.

Use when when you have experimental MS/MS spectra and want to discover structurally similar compounds beyond exact spectral library matches—particularly useful for identifying chemical analogs, homologs, or isomers that share fragmentation logic but differ in molecular structure.

Use when when processing untargeted LC-MS data with SLAW and observing incomplete feature detection across the sample cohort—i.e., features present in some samples but with missing values (zeros or NAs) in others due to signal dropout, retention time drift, or mass calibration drift.

Use when after peak detection on a composite mass track has identified candidate peaks in a mass chromatogram, and before compiling the final feature table.

Use when after you have accumulated experimental MS data from ≥2 LC gradient trials, extracted separation efficiency metrics (retention time spacing) from each trial, and encoded each gradient as a feature vector.

Use when after collecting observed separation efficiency scores at sampled gradient conditions and you need to propose the next gradient to evaluate.