Claude Skills by HolobiomicsLab

Use when you have raw or processed arrival-time data from a TWIM-MS instrument and need to convert it to CCS values for comparison across experiments or biomolecular classes.

Use when you have raw or processed TWIM-MS experimental data (arrival times, m/z, ion mobility parameters) and need to compute class-conditioned CCS values or assign biomolecular class labels directly from high-dimensional ion mobility measurements.

Use when you have TWIM-MS data (arrival time and m/z values) from a multi-omic sample and need to: (1) establish a CCS calibration curve from known standards, (2) assign unidentified features to biomolecular classes (e.

Use when you have measured CCS values from (LC-)IM-MS samples spiked with U¹³C labeled internal standards (e.

Use when you have IM-MS measurements of samples spiked with U13C-labeled internal standards (e.g., fully labeled yeast extract) and need to assess whether measured CCS values systematically deviate from their true reference values.

Use when your analysis pipeline must ingest mass-spectrometry data from mixed vendor sources (e.g., Thermo RAW, Agilent .d, Waters .raw, and open mzML) without writing separate parser logic for each format.

Use when you have a collection of raw mass spectrometry data files from multiple instrument vendors (Agilent, Bruker, Thermo) and/or mzML exports that need to be converted to a standardized, cross-platform format for downstream software development, AI research, or multi-vendor meta-analysis.

Use when you have mass spectrometry raw data in a proprietary vendor format (Thermo .raw, Agilent .d with or without ion mobility, Bruker ion mobility .d, or mzML) and need to enable reproducible, language-agnostic access to multidimensional spectra (e.

Use when when you have a multi-step computational chemistry or molecular modeling pipeline (3+ sequential or parallel stages) that must process many molecules, each requiring repeated tool invocations with different parameters, and you need reproducibility, fault tolerance, and the ability to.

Use when when you have raw mass spectrometry data files from multiple acquisition modalities (LC-MS, GC-MS, ion mobility, or imaging) and need to automatically route each to the correct downstream analysis module without manual intervention.

Use when you have raw LC-MS data files (mzML, netCDF, or raw vendor formats) from multiple samples and need to extract, align, and quantify chromatographic features across the cohort.

Use when a task needs a skill from ASB Metabolomics — ion-mobility-MS — search this unit's 390 evidence-grounded skills, then apply and optionally ground the one that fits.

Use when when you have raw GCxGC-MS data imported from NetCDF into a 2D-TIC chromatogram object and need to remove chemical and instrumental noise (column bleeding, baseline drift, detector contamination) to reveal metabolite differences between sample groups for downstream multiway PCA or.

Use when you have raw LC-IM-MS/MS data files from sterol lipid analysis and need to identify unsaturated sterol isomers by matching experimental collision cross section values against a quantum chemistry calculation-assisted CCS prediction database.

Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.

Use when after peak detection and MS1 feature extraction from FIA-MS, GC-MS, LC-MS(/MS), or CE-MS data, when you need to identify unknown metabolites by matching observed m/z values to a reference database and want to recover HMDB identifiers, molecular formulas, and structural annotations for.

Use when you have acquired the same sample(s) using multiple LC-MS, LC-IMS-MS, or direct infusion methods (e.

Use when you have an LC-MS peak-intensity matrix with observed m/z values (from negative or positive mode ionization) and need to map each peak to candidate neutral masses in KEGG. Use this skill when you have a curated Cpd.

Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.

Use when after accurate mass searching has assigned multiple detected m/z features to the same metabolite via positive and negative adduct libraries, and before sample-level feature merging.

Use when you have a characterized lipid species (with defined class and fatty acid composition) and need to predict which adduct forms will ionize under your experimental ionization mode (positive or negative ESI), and you require accurate precursor m/z values for DDA method configuration or.

Use when when you have unidentified LC/MS features (m/z, retention time, intensity) and need to disambiguate which metabolites they represent by accounting for the fact that observed m/z values may correspond to different adduct forms (e.

Use when after temporal correlation has identified candidate feature pairs with matching intensity profiles across time-resolved DBDI-MS experiments.

Use when when initializing an mWISE annotation pipeline with a new or custom KEGG database, or when you need to reconstruct the Cpd.Add matching table with modified adduct/fragment specifications (e.g., subset to instrument-specific adducts or adjust frequency thresholds).

Use when when annotating m/z features against a metabolite database (HMDB, Lipidmaps, etc.) and the sample preparation, ionization method, or polarity mode favors specific adduct species. For example: negative-mode LC-MS or MS imaging will preferentially generate M-H and halide adducts (M+Cl);

Use when after identifying statistically significant LC-MS features (e.g. via MB-VIP permutation testing) when you need to consolidate redundant measurements of the same metabolite arising from different ionisation adducts (e.g. [M+H]+, [M+Na]+, [M−H]−).

Use when when you have an LC-MS feature table with m/z and retention time columns and need to identify which observed ions correspond to the same neutral compound under different ionization conditions and isotopic enrichment.

Use when when processing LC-MS metabolomics feature tables where adduct annotation is absent or incomplete, and you need to identify which ionization adducts are present in your mass spectrometry data.

Use when when you have a list of observed m/z values from LC/MS feature detection and need to identify candidate metabolites by testing whether those m/z values correspond to known database compounds in specific ionization forms.

Use when ingesting mass spectrometry spectra from heterogeneous databases or libraries where adduct annotations may be incomplete, incorrectly formatted, or inconsistent with the ionization mode. Use it before downstream analysis (e.

Use when you have statistically significant LC-MS features (e.g., filtered by p-value < 0.01) from multi-assay metabolomics datasets and need to group features that represent the same metabolite ionized under different ESI conditions (e.g., [M+H]+, [M+Na]+, [M+NH4]+).

Use when when you have access to annotated MS/MS spectra from a specific ionization mode (e.g., negative ESI) or adduct class (e.

Use when you have an unknown MS/MS spectrum with a measured precursor m/z and want to determine which positive-mode adduct type ([M+H]+, [M+Na]+, [M+K]+, etc.) is most likely responsible for ionization.

Use when you have a feature table from untargeted LC–MS all-ion fragmentation (AIF) chromatograms processed by xcms and RamClustR, and you want to assign metabolite annotations to individual features by comparing their experimental MS/MS spectra against curated fragment libraries (e.

Use when you have vendor mass spectrometry raw files (e.g., .raw, .d, .

Use when you have multiple independent peak-picking algorithms available and need to allow end-users to select among them for the same analytical task (peak detection in untargeted LC-MS data) without coupling the rest of your pipeline to each algorithm's API.

Use when you have refactored or reimplemented a core computational method (e.g., entropy similarity calculation) and need to verify that the new implementation produces mathematically equivalent results to the original before deploying it to production.

Use when after multi-sample alignment has been completed in JPA (Part 5), when you have an aligned feature table containing consolidated features across samples and need to extract ion chromatograms, perform CAMERA annotation, or validate feature assignments prior to MS2 annotation.

Use when after retention time and m/z-based clustering have been applied to group features across samples in a multi-sample metabolomics study.

Use when you have predicted peptide sequences from a de novo sequencing tool (e.g., Casanovo) and want to understand the fine-grained accuracy of the predictions beyond exact-match peptide-level scoring.

Use when after MS2Query has ranked and scored library matches against query spectra, when you need to apply different confidence thresholds, interpretation strategies, or downstream workflows depending on whether the result is an exact match (precursor m/z difference ≈ 0) or a chemical analog.

Use when you have raw chromatography–mass spectrometry data (GC-MS or LC-MS) in 2D m/z–retention time format and need to identify and discriminate multiple analytes while avoiding false peak detections inherent in conventional peak picking.

Use when after applying a stringent Q-value quality filter (e.

Use when after completing feature m/z grouping and pairwise alignment detection on two LC-MS datasets acquired under non-identical conditions.

Use when you have LC–MS all-ion fragmentation chromatograms already processed by xcms and clustered by RamClustR, a feature table (targetTable.csv format) listing features to annotate, and you need rank-1 metabolite or lipid identifications with confidence metrics.

Use when after running an end-to-end annotation workflow (matching, clustering, filtering, and prioritization) on untargeted LC-MS peak tables, when you have access to a curated reference dataset (df.Ref) containing validated peak assignments for the same biological sample.

Use when you have pseudo-MS/MS spectra from LC-MS all-ion fragmentation (AIF) data that have been matched against one or more ion fragment databases (e.g., LipidPos, MassBank), generating multiple candidate annotations per feature.

Use when you have MS imaging or LC-MS data with pre-annotated m/z values that include multiple isomer or metabolite names per m/z (stored as semicolon-delimited or multi-record strings), and you want to measure whether a refinement step (e.

Use when after MS-FINDER in silico annotation has been executed on exported LC-MS features and multiple database matches (with HRR scores) have been returned.

Use when after recursive annotation propagation has assigned metabolite labels to previously unannotated nodes in a two-layer metabolomic network, and before reporting final annotated metabolite identities.