Claude Skills by HolobiomicsLab

Use when you have mzML spectral data stored in a non-standard format (SQLite database, custom binary store, or indexed archive) and need pymzML's Reader to parse and iterate over spectra as if they were in native mzML files. Specifically, when FileInterface.

Use when you have raw mass spectrometry quantification tables (feature abundance matrices), sample metadata tables, and spectral data files (e.g., from the omsw_pleurotus dataset) that must be ingested into the msFeaST pipeline;

Use when you need to filter compounds during Pickaxe network expansion based on domain-specific criteria (e.g., mass matching, similarity thresholds, retention time windows) that are not covered by built-in filters.

Use when you have a mature Python package (e.g., Mummichog 2.x) that needs to be relocated to a new GitHub organization (e.

Use when when you have an indexed gzip file (mzML.gz with internal index structure) and need to retrieve individual spectra or chromatograms by integer index without decompressing the entire file.

Use when you have a pretrained PyTorch model with released weights (e.g., JESTR on NPLIB1), a prepared dataset with input features (spectra m/z–intensity arrays, molecular graphs), a GPU environment with CUDA 11.

Use when you have txt files exported from Sciex MultiQuant (>v3.0.

Use when you have a SummarizedExperiment object containing pooled quality control samples with measured compound and internal standard peak areas.

Use when when you have extracted a peak feature table (CSV or tabular format) with mass-to-charge ratios, retention times, and intensity values across multiple samples, and you need to distinguish genuine differential metabolic signals from instrumental noise or low-abundance background before.

Use when you have raw quantification data (abundance or intensity values across samples and features) from mass spectrometry or similar high-dimensional assays and need to prepare it for msFeaST's feature selection workflow, which requires standardized internal data structures compatible with.

Use when when you have metabolomics results from multiple studies reporting compound identifiers, p-values, fold-changes, and study sizes (N), and you need to prepare them for quantitative meta-analysis using weighted Fisher's method and logarithmic fold-change combination.

Use when you have raw MS intensity tables showing systematic drift during a measurement sequence (e.g., declining or variable ion counts across a run), particularly in targeted quantification workflows where external calibration standards are available.

Use when when you need to enable non-programmer mass spectrometry users to express complex spectral search patterns (e.g., isotope patterns, precursor mass constraints, scan-type filters) without writing procedural code.

Use when when you have implemented or obtained a spectral library search algorithm (such as Flash Entropy Search) and need to empirically verify its performance against reported benchmark metrics, or when comparing query performance across different library sizes, mass spectral file formats (.mgf, .

Use when after parsing a MassQL query string into an abstract syntax tree or intermediate representation, before executing it against mass spectrometry data files (mzML, mzXML, etc.). Validation is essential when the query contains complex MS-specific patterns (e.

Use when when you have autoQ output containing peak area measurements for isotopologues in data frame format and need to prepare data for metBarPlot visualization or cross-sample comparison. Specifically, use this skill when val.to.

Use when you have mzPeak files (Parquet-based archives containing mass spectrometry spectra and chromatogram data) that you want to analyze in R, and you need to convert the Arrow columnar representation into native R objects that can be passed to downstream analysis functions (e.

Use when after AutoTuner has completed peak identification (TIC analysis), peak isolation, and EIC parameter extraction on at least 3 raw mass spectrometry samples (qTOF, orbitrap, or FTICR formats converted to mzML/mzXML/CDF), and you need to export the tuned parameters in a format ready for XCMS.

Use when you have raw lipidomic and metabolomic data files generated by the Multi-ABLE barocycler-based concurrent multiomics method and need to perform integrative preprocessing (spectral normalization and alignment across samples) followed by multivariate analysis to identify differential lipids.

Use when when you need to verify or retrieve package-internal metadata about compiled .NET assembly location and version before processing raw mass spectrometry files, or when testing the R↔C# dispatch mechanism in isolation without loading actual Orbitrap .raw data files.

Use when you have extracted metadata or spectral information from a Thermo Fisher Scientific .

Use when evaluating alternative implementations of data storage or retrieval strategies in R objects—specifically when deciding whether to eagerly populate all columns in a data frame slot (e.

Use when before running any R function that wraps compiled C# methods or system executables (e.g., rawrr::readSpectrum), especially when the package depends on language runtimes (.NET, Mono) or proprietary third-party assemblies that must be downloaded and configured separately.

Use when you have multiple MSP (mass spectrum) library files to read and merge in R, and your computational task is time-consuming (e.g., structure extraction, SMILES assignment, or RI assignment) and you have a multi-core system available.

Use when when you need to support fast, non-sequential lookups (e.

Use when you have a large mzML file or text corpus (e.g., Moby Dick, proteomics run) stored in compressed or database format and need to retrieve specific spectra or chapters by ID without sequential iteration or full decompression.

Use when you have a metabolomics count table (rows=metabolites, columns=samples) with associated metadata containing a categorical grouping factor (e.

Use when you have: (1) a set of spectral library matches (seed identities) mapped to molecular structures, (2) a candidate structure graph derived from in silico fragmentation (e.

Use when you have a large collection of mass spectrometry spectra (m/z and intensity pairs) and need to rapidly retrieve similar or candidate spectra from the archive in response to queries.

Use when when you have a raw mass spectrometry file (e.g., Thermo Orbitrap .raw) and need to establish the measured throughput of a spectral reading function (e.

Use when you have collected raw MS files via an uploader tool and need to automatically forward them to a data processor for proteomics analysis, ensuring format compatibility and maintaining linkage to the original upload session and user project.

Use when you have raw mass spectrometry data in mzML, abf (Reifycs), or cdf (NetCDF) format and need to load it into MS-DIAL or a similar open-source metabolomics platform.

Use when you have raw metabolomics data in mzML or mzXML format and need to convert it into a normalized feature table (CSV or mzTab) via automated batch processing.

Use when you have Thermo Orbitrap .raw files and need to extract specific spectral features (base-peak m/z, intensity values, chromatographic traces, scan-level metadata) for downstream statistical analysis or visualization in R. Use this skill when you want to avoid lossy format conversion (e.

Use when when annotating .msp mass spectrometry files with chemical structure metadata and you need fast, offline molecular transformations (SMILES↔InChI, canonical SMILES generation) without network latency or service availability constraints;

Use when when you have a set of compounds represented as SMILES strings and need to compute their molecular properties (neutral mass, adduct-adjusted masses, mordred descriptors) for comparison against experimental peaks, retention time predictions, or similarity-based filtering thresholds during.

Use when you have a formula-assigned dataset from FT-ICR MS or other mass spectrometry with molecular formulas assigned to each mass feature, and you want to identify and quantify the molecular transformations (e.

Use when when you have a table of JSON records and need to select a subset matching a specific condition—for example, filtering records where 'status=active' before building a string value, or filtering 'experiment_type=MS' before constructing a matrix of dictionary objects.

Use when after peak detection on LC/HRMS data (mzXML, mzML, netCDF formats) when m/z values require refinement for improved mass accuracy in untargeted metabolomics workflows.

Use when you have a formal grammar specification for a domain-specific language (or can extract one from reference implementations) and need to convert user-written query strings into structured, machine-processable representations (ASTs or JSON) that preserve domain-specific semantics—such as mass.

Use when after network partitioning, when you have identified connected subnetworks of features matched by isotope or adduct patterns and need to sanitize and categorize the relationships before tree construction. Use it when redundant features (e.

Use when after formatting raw mass spectrometry transition data into the EISA-EXPOSOME schema (NAME, PrecursorMZ, ProductMZ, Intensity, RT, ID columns), and before exporting the final database file.

Use when after a CNN model has generated predicted molecular embeddings from mass spectrometry data, use this skill to match those predictions against a curated reference database of known molecular embeddings.

Use when when you have isolated, high-confidence reference chromatographic peaks (ground-truth) from reference LC-HRMS chromatograms that have been matched across multiple samples, and you need to train a CNN model to detect peaks in new chromatograms but lack sufficient labelled instances.

Use when when you have a processed mass spectrum object (e.g., from Bruker FT-ICR acquisition) and a reference peak list (e.g., SRFA.

Use when when performing m/z domain calibration on FT-ICR or high-resolution MS data and the initial calibration attempt finds fewer than 5 reference m/z matches within the standard PPM window (typically ±1–5 ppm).

Use when you have training LC-HRMS chromatograms (rt × m/z matrix format) from which you have already extracted peak candidates using smoothing and gradient-descent peak detection, and you possess a curated reference list of isolated single chromatographic peaks (ground truth).

Use when when you have PSM files (output from search engines like MaxQuant, MSGFPlus, or Sage) and spectrum files in mzML or MGF format with non-trivial or inconsistent naming schemes, and you need to establish which PSMs correspond to which spectra.

Use when when you have trained a regression model on experimental retention times or similar continuous molecular property predictions and need to quantify its generalization performance on held-out test data.

Use when when you have fitted one or more regression models (linear or polynomial) to external calibration standards in MS data and need to verify model adequacy before applying it to unknown samples.