Claude Skills by HolobiomicsLab

Use when when you need to deploy a Java web application packaged in a Tomcat Docker container to a specified HTTP endpoint, and must verify that the container starts successfully, the Tomcat server initializes, and the application becomes accessible at the mapped host port before proceeding with.

Use when you have standalone metabolomics analysis tools (Python scripts, R packages, MATLAB compiled applications) that you want to expose through Galaxy's UI and make composable into reproducible workflows.

Use when after LDA topic inference has assigned dominant topic labels to mass spectra, and before those labels are passed to MLP or GNN multi-task training.

Use when when you have a large list of SMILES strings but lack corresponding experimental mass spectra, and you need to generate 30,000+ training pairs to train a deep learning model for molecular structure prediction.

Use when after mass-difference network generation has matched pairwise peak mass differences to a reference biochemical transformation key with mass error ≤1 ppm, and you need to distinguish metabolic transformations driven by microbial activity from those arising from non-biological chemical.

Use when when you need to verify that a wrapper package (e.g., rawrr) correctly bridges R and a managed .NET assembly (such as RawFileReader), specifically to confirm that internal dispatch functions can retrieve assembly location and version string before attempting actual spectral data extraction.

Use when when you have a Ubuntu-based software package (e.g., MetumpX) that advertises bundling a specific number of tools (~89) but the individual tool names, versions, installation sources, and installation methods are not enumerated in the README or primary documentation.

Use when you have trained a DNN retention time predictor and need to rank candidate metabolites for an unknown compound: the DNN outputs both point estimates and uncertainty bounds for each candidate''s RT, and you need to convert these into probabilistic scores that reflect confidence in each.

Use when when you have parallel mass spectra and molecular structure data (e.g., CANOPUS or MassSpecGym datasets) and aim to train a single encoder-decoder model (e.g., BART) that must handle both modalities as input and output tokens.

Use when when implementing or modifying metabolomics feature detection pipelines (e.g., adduct detection, m/z matching, feature labeling) where correctness directly impacts downstream analysis.

Use when after implementing a custom Filter subclass (e.g., MetabolomicsFilter, TanimotoFilter) in minedatabase/filters.py, you must write unit tests to validate that _choose_cpds_to_filter correctly identifies compounds to retain/remove.

Use when when implementing or extending file format parsers in a spectral data pipeline, you need unit tests to ensure that format-specific parsers correctly instantiate spectrum objects with expected m/z arrays, intensity arrays, and metadata attributes before releasing to users or integrating.

Use when when you have implemented parser functions for one or more mass spectrometry file formats and need to verify that metadata and peak lists are correctly extracted and converted into matchms Spectrum objects before committing to a feature branch.

Use when after implementing or modifying basic peak filtering operations (e.g., low-intensity peak removal, intensity normalization) on mass spectrometry spectral data in supported formats (mzML, mzXML, msp, MGF, JSON).

Use when you have FT-ICR MS peak data with assigned molecular formulas (C, H, O, N, S, P elemental counts) and want to characterize the structural saturation and aromaticity of metabolites to assess their biochemical degradation potential, compare chemodiversity across samples, or stratify.

Use when punc'data's automatic keyword-based column recognition produces incorrect semantic role assignments—for example, when a column header contains a non-standard keyword that the tool fails to recognize, or when a column's true semantic role (e.

Use when you have a repository of structured records (e.g., mass spectrometry data, metadata, or domain-specific formats) and need to enforce validation rules systematically across all records.

Use when when designing a custom MsBackend subclass (e.g., MsBackendTest) that stores spectral data in multiple slots (a data.frame for spectra variables, NumericList objects for m/z and intensity peaks). Use this skill to guard against slot desynchronization—e.

Use when when implementing or auditing S4 replacement methods (e.

Use when raw MRM lipidomics data arrives in vendor-specific export formats (e.g., Sciex, Waters, Thermo TSV/CSV) with inconsistent or proprietary column naming, numeric encoding, and lipid nomenclature.

Use when when you have cloned a multi-framework .NET project (e.g., MsdialWorkbench using .NET Framework 4.7.2, .NET Core 3.1, and .NET 6) and need to set up the build environment in Visual Studio, restore dependencies, select a specific build configuration (e.

Use when you have execution-time data for the same set of plotting operations (e.g., chromatogram, spectrum, peakmap rendering) across two or more backend implementations (e.

Use when after merging separate vocabularies for distinct data modalities (e.g., spectral tokens for m/z values and intensities, structural tokens for SMILES or graphs) and before deploying the unified vocabulary in a language model.

Use when after statistical analysis (e.g., edgeR) has produced a results table containing lipid identities, fold-change values, and p-values for pairwise or multi-condition comparisons.

Use when when you have cloned a .NET Framework or .NET Core WPF project from a GitHub repository and need to compile it into an executable binary for Windows deployment. Specifically, when the project uses ReactiveExtensions/ReactiveProperty packages, declares a .NET Framework 4.7.2 or .

Use when you are deploying a Windows .NET application (e.g., AirdPro CLI) inside a Docker container on a non-Windows host and need to understand whether Wine initialization completes, how long it takes (documented as >30 minutes), whether .

Use when when you have a collection of mass spectra (e.

Use when you need to verify that a GitHub Actions workflow (such as 'dev_build_release.

Use when when you have cloned the MsdialWorkbench repository and need to produce an executable WPF GUI application (MsdialGuiApp) for Windows.

Use when you have raw untargeted metabolomics data (at least 3 samples in mzML, mzXML, or CDF format) from qTOF, orbitrap, or Fourier transform ion cyclotron resonance mass analyzers and need to obtain optimized XCMS processing parameters tailored to your specific instrument and dataset rather than.

Use when you have spectrum or chromatogram data stored as XML strings (e.g., in a SQLite database indexed by spectrum ID) and need to access individual spectra by ID or iterate through them sequentially while working with a library like pymzML that expects Spectrum or Chromatogram objects.

Use when when spectrum or chromatogram data is stored as serialized XML strings in a database or file system and must be converted into pymzML Spectrum or Chromatogram objects for programmatic access.

Use when when you have retrieved a decompressed XML data block from an indexed gzip file (via GSGR bracket notation) and need to convert that raw XML string into a usable Python spectrum or chromatogram object for downstream analysis, filtering, or comparison.

Use when a task needs a skill from ASB Metabolomics — mass-spectrometry — search this unit's 804 evidence-grounded skills, then apply and optionally ground the one that fits.

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 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 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 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 after loading or merging spatial metabolomics data into an AnnData object (via read_sm_csv_as_anndata, read_sm_imzml_as_anndata, or merge_sm_pos_neg), and before filtering or normalization steps.

Use when when you have imzML and accompanying ibd (ion binary data) files from spatial metabolomics experiments and need to load them into a unified AnnData object representation for integration with spatial transcriptomics or other spatial multi-omics data.

Use when you have preprocessed and filtered ST and SM AnnData objects with spatial coordinates and features, and you need to establish spot-level correspondence between the two modalities to enable downstream joint analysis.

Use when you have imaging mass spectrometry (IMS) datasets with peak intensity features organized as spatial graphs (nodes = pixels/voxels, edges = spatial adjacency), and you need to discover latent peak patterns for automatic peak picking or marker ion identification without manual feature.

Use when when working with imaging mass spectrometry (IMS) datasets where you need to extract latent low-dimensional peak features from high-dimensional peak intensity data while preserving spatial adjacency information.

Use when when you have multiple CDF imaging files (e.g., from mass spectrometry imaging scans of biological samples) that need to be read into a single Matlab workspace with consistent structure and metadata (spectral intensity, m/z arrays, spatial coordinates).

Use when you have parsed imzML XML metadata and loaded the corresponding .ibd binary intensity file, and need to extract specific ion images at one or more target m/z values.

Use when you have loaded MSI data into napari, defined one or more ROIs of biological interest (e.g., tumor margin, specific tissue layer), extracted mean or summed intensity spectra from those regions, and need to identify the putative biochemical compounds corresponding to detected m/z peaks.

Use when when you have processed Cardinal MSI data (normalized peak intensities, optional SSC segmentation results) and need to transition to Seurat-based workflows for differential expression, pathway analysis, or integration with spatial transcriptomics data.

Use when constructing or enriching a chemical formula database that must capture not just structural similarity (DBEdges) but also biological co-occurrence patterns.