asb-skill-collections

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 when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.

Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.

Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.

Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).

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 when you have derivatized metabolite structures (SMILES or mol format) and need to predict their ionization products in MS imaging, particularly when the derivatizing matrix produces non-standard adducts (e.

Use when when you have binned mass spectrometry imaging peaks and want to understand which detected mass-to-charge ratios represent the same metabolite in different ionization states (parent vs. adduct form).

Use when when you have a metabolite SMILES structure and need to predict which adduct ions will appear in a mass spectrum acquired with a chemical derivatizing matrix.

Use when you have computed a histogram of pairwise mass differences from MS imaging data and need to (1) identify which observed mass differences correspond to biologically relevant or chemically known adducts, or (2) rank the most frequently observed mass differences to discover dominant adduct.

Use when you have computed a histogram of mass differences from all pairwise mass comparisons in your MALDI-MS imaging dataset and need to prioritize which mass differences are most frequent and likely represent genuine molecular adducts (e.g., metabolite + matrix ions) rather than noise.

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 when you need to evaluate how a specific algorithm parameter (such as SearchMolecularFormulas first_hit mode) affects the quantity and quality of molecular formula assignments on a given spectrum or dataset.

Use when you are evaluating a new or existing data analysis pipeline (e.g., MetaboDirect) and need to produce a transparent, evidence-based feature matrix showing which analyses it supports—particularly when the pipeline is positioned as an alternative to or improvement over established tools.

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 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 after completing metabolite annotation of LC-MS AIF features using the annotateRC function, when you need to persist ranked candidate matches, inspect multiple candidate annotations per feature, visualize matched ions in ranked spectra, or export pseudo-MS/MS spectra for external analysis.

Use when when you have raw PubChem compound records or other public chemical structure databases and need to supply candidate metabolite structures to a mass spectrometry annotation workflow like MAGMa.

Use when you have access to the source code of a webservice component (Python, configuration files, route definitions) and need to produce machine-readable API documentation (OpenAPI 3.

Use when after molecular formula assignment from FT-ICR MS peaks and elemental composition tabulation (C, H, O, N, S, P counts), when you need to quantify the degree of aromaticity and carbon-skeleton unsaturation for each detected compound to support Van Krevelen classification, chemodiversity.

Use when you have tabular experimental data (e.g., sample metadata, mass spectrometry parameters, NMR acquisition details) in JSON table format and need to produce a list of structured objects for submission to a data repository (e.g., Metabolomics Workbench) or downstream format conversion.

Use when you have raw or processed TWIM-MS data with arrival time and m/z dimensions, and you need to label each experimental feature by biomolecular class before feature identification or peak detection steps are complete.

Use when when you have raw TWIM-MS arrival-time data and need to transform it into absolute CCS values for downstream biomolecular class assignment or comparative analysis.

Use when when processing raw TWIM-MS experimental data that contains arrival time measurements but you need drift times for CCS calibration or class-specific CCS calculations.

Use when you have built multiple Docker image variants (e.g., cli, dev, linux, windows) from a multi-stage Dockerfile and need to verify that their uncompressed and compressed storage footprints fall within documented acceptable ranges (e.

Use when after calling buildExperiment on metabolomics data to construct a SummarizedExperiment object, before performing batch correction, outlier detection, or other QC calculations that depend on ratio assay values.

Use when you have a trained GNN model for molecular property prediction (such as CCS) and need to understand which atomic and bond features are most influential in driving predictions.

Use when you have canonicalized SMILES strings from a chemical database (e.

Use when you have mass spectrometry data loaded into a pandas DataFrame with m/z, retention time, and intensity columns, and need to confirm that pyOpenMS-Viz can produce visualizations (spectra, chromatograms, or peak maps) using a specific plotting backend (matplotlib, Bokeh, or Plotly) on real.

Use when when you have execution time data for visualization scripts across multiple backends (matplotlib, Bokeh, Plotly) and need to determine which backend offers the fastest median performance for specific mass spectrometry plot types (chromatogram, mobilogram, peakmap, peakmap-marginals.

Use when you have execution-time metrics (from a benchmark table or profiling logs) across multiple visualization backends for the same set of plots (e.

Use when you need to support multiple plotting backends for the same data visualization task, and you want to centralize backend selection logic so that users can specify their preferred rendering engine (matplotlib, bokeh, or plotly) at call time without modifying the core plotting logic.

Use when after batch correction of metabolomics data when you need to identify and remove compounds whose signal in background/blank samples exceeds a tolerable contamination level.

Use when when training a regression or neural-network model on paired microbiome and metabolome data, and you need to establish a statistically principled cutoff for identifying metabolites (or other features) whose prediction correlations are significantly better than random chance.

Use when when predicting one data modality (e.g., metabolite abundances) from another (e.g., microbiome composition) and you need to distinguish genuine microbe–metabolite associations from false positives driven by data compositionality, sample size artifacts, or confounding variation.

Use when use when the workflow requires background-distribution-significance-thresholding.