Claude Skills by HolobiomicsLab

Use when when you have SQL-inspired query strings that encode domain-specific assumptions and need to validate, transform, or execute them against data repositories. Specifically: input is human-readable DSL text containing mass spectrometry-specific clauses (e.

Use when when performing open modification spectral library searching and you need to sensitively match query spectra that may carry unknown post-translational or chemical modifications to an unmodified spectral library.

Use when when you have just loaded the rawrr R package and need to confirm that the bundled .NET 8.0 assembly (rawrr.exe) is present and functional before performing any mass spectrometry data extraction operations.

Use when you have a C# GUI application targeting .NET Framework 4.8 (Windows-only) and need to execute it on macOS or Linux hosts without modifying the source code. The application requires compilation from source and GUI display support via X11 forwarding or headless CLI execution.

Use when you need to compile and test a .NET-based metabolomics or bioinformatics desktop application (e.g., MS-DIAL version 5 series) from source code, or you are implementing new parsing or data-processing modules that must integrate with WPF UI frameworks and ReactiveExtensions patterns.

Use when when processing large collections of mass spectra from multiple Open Mass Spectra Libraries (OMSLs) or databases that may contain redundant spectral records with identical m/z–intensity peak patterns but potentially different metadata annotations.

Use when after EIC candidate generation from LC/HRMS data (mzXML, mzML, or netCDF formats), when you need to localize discrete peaks within chromatographic profiles and assign retention time boundaries, apex intensities, and quality scores prior to peak annotation or cross-sample alignment.

Use when when you have FT-ICR MS peak data with assigned molecular formulas (e.g., from CoreMS, Formularity, or similar formula assignment tools) and need to compute thermodynamic indices (DBE, GFE, AImod, NOSC) or classify peaks by elemental composition.

Use when after loading a numeric elemental ratio column (H/C, O/C, N/C, etc.) from a CSV file into Punc''data, when you need to visualize the distribution of molecular formulas across a sample and want bin spacing to adapt automatically: narrow bins (0.1) for low-range data (span ≤ 3.

Use when after a CNN model has generated predicted molecular embeddings from mass spectrometry data, and you need to identify the most likely candidate molecules from a reference database.

Use when when you have tokenized mass spectra (peak-mass and peak-intensity pairs from experimental or in-silico libraries such as NIST 2017 or MassBank) and need to perform rapid similarity searches or spectrum matching at scale.

Use when you have a query mass spectrum and need to retrieve the most chemically similar reference spectra from a large experimental or predicted library (thousands to millions of entries).

Use when after installing a Python package or cloning its repository, to verify that the primary command-line interface is functional and discoverable before attempting analysis workflows.

Use when when implementing or auditing a data replacement method (e.g., `mz<-`, `intensity<-`) in an MsBackend subclass that must enforce ordering or format constraints on peak data.

Use when you have predicted retention times from one or more machine learning models (DNN, Gaussian Process, or ensemble) applied to small-molecule chromatography data, along with corresponding experimental ground-truth retention times, and need to quantify prediction accuracy and rank competing.

Use when you have computation-time metrics from a gallery or benchmark suite comparing multiple plotting backends on the same data types (e.

Use when you have acquired targeted mass spectrometry data with measured ion intensities for known standard compounds at multiple concentration levels, and you need to convert sample intensities into absolute or relative concentrations.

Use when after isolating TIC peak regions via sliding window analysis and peak expansion (isolatePeaks), apply this skill when you need dataset-specific XCMS parameter estimates.

Use when after performing spectral library matching (whether unmodified or open modification search) and ranking candidate matches by similarity score, apply FDR control when you need to report a curated set of identifications with quantified confidence.

Use when performing untargeted metabolomics annotation (i.e., matching observed spectra to a compound database without a pre-defined target list) and you need to assign statistical significance or confidence to candidate metabolite identifications.

Use when when reconstructing or validating the ColumnFinder component in mwtab, you need to assess whether the combined NameMatcher.dict_match and ValueMatcher.series_match operations are correctly populating database-ID columns. Apply this skill after defining NameMatcher patterns (e.

Use when you need to systematically enumerate all possible lipid species within a defined analytical scope—specifically when you have specified one or more lipid classes (e.g., phosphatidylcholine, triacylglycerol) and fatty acid composition ranges (e.

Use when rescoring PSMs with machine learning or statistical models where: (1) you want to consider multiple candidate PSMs per spectrum (e.

Use when after performing retention-time-based feature grouping (e.g., 10–20 second windows), when you observe large feature groups that may conflate multiple independent compounds with coincidentally similar retention times.

Use when you have detected multiple ion peaks from replicate injections of the same sample in untargeted metabolomics and need to consolidate them into a single feature table.

Use when you have extracted multiple per-sample feature tables (in CSV format, each with feature ID, m/z, intensity, and retention time columns) and need to identify which features are the same compound detected across different breath samples.

Use when after mzRAPP has exported a benchmark CSV file from centroided mzML files and you need to confirm the benchmark was constructed correctly before using it to evaluate NPP tool performance. Specifically, when you have a reference expectation (e.

Use when you have a raw XCMS CentWave feature extraction table with m/z values, retention times, and intensities, and you observe that multiple features cluster around the same nominal mass and retention window.

Use when after batch correction and concentration normalization have been applied to a merged m/z peak table and metadata file, but before statistical testing or machine learning.

Use when when you have high-resolution mass spectra that must be rapidly searched against large spectral libraries with open modifications, and you need to reduce the dimensionality of spectral data without losing the ability to retrieve spectrally similar peptides.

Use when when you have loaded a raw MS quantification table (feature-by-sample intensity matrix) into QuantyFey and are applying drift-correction strategies (Internal Standard correction, statistical drift correction, Custom Bracketing, or Weighted Bracketing) but need to maintain traceability.

Use when after feature extraction from mzML/mzXML files when you have identified candidate peaks in breath spectrometry data but need to separate true VOC signals from instrument noise.

Use when when you have raw mass spectrometry spectral data (peak intensities across m/z values) and need to feed it into MLP or GNN models for metabolite annotation, or when you need to generate LDA topic labels as auxiliary multi-task learning targets.

Use when after RAMClustR clustering and do.findmain molecular weight inference have been completed on XCMS-detected metabolomics features.

Use when after temporal correlation has identified feature pairs with matching intensity profiles across direct-injection MS experiments.

Use when you have a set of training LC-HRMS chromatograms (retention time × m/z matrix format) and a manually curated reference list of isolated single chromatographic peaks, and you need to update the reference peak properties (retention time, m/z, peak shape) to match the actual peak signatures.

Use when when processing MZmine2/MZmine3 peak tables from LC–MS metabolomics data where you observe inflated feature counts due to multiple ionization states, isotopic satellites, and neutral losses of the same parent compound, particularly before computing extract-level metrics (e.

Use when after generating a filtered feature table from raw mass spectrometry data in openNAU.

Use when when you have high-resolution mass-spectrometry (HRMS) breath data in mzML or mzXML format from multiple subjects or conditions and need to prepare a sample-by-feature intensity matrix for downstream statistical comparison, biomarker discovery, or classification tasks.

Use when after executing an MZmine batch processing workflow on raw metabolomics data (mzML/mzXML format), when you need to convert the in-memory feature detection and alignment results into a shareable, schema-compliant tabular format suitable for downstream statistical analysis, figure.

Use when you have raw feature tables exported from NPP tools (XCMS, MZmine 2, MS-DIAL, OpenMS, etc.) in their native formats and need to compare their peak detection and alignment performance against a mzRAPP benchmark dataset.

Use when after loading an MS-DIAL feature table when you need to separate features into two disjoint groups: one meeting a quantitative threshold (e.g., m/z decimal values outside [4, 8], coefficient of variation below a cutoff, or mass defects within acceptable bounds) and one not meeting it.

Use when you have generated or received mzPeak files from a Rust, Python, R, or other implementation and need to verify they comply with the published HUPO-PSI specification before integration into a production workflow, data repository, or downstream analysis pipeline.

Use when when building a file I/O abstraction layer that must support multiple serialization formats (e.g., uncompressed mzML, gzip-compressed mzML, indexed gzip mzML, or SQLite-backed mzML) and route each to its correct handler class without exposing format logic to downstream consumers.

Use when you have a parser library that must support multiple file formats (e.g., mzML, SQLite, compressed gzip) and want to avoid conditional logic scattered throughout the parsing code.

Use when when raw MS files are uploaded to MSConnect via the Raw File Uploader and must be verified for compatibility with downstream processing tools (e.g., Proteomics_Data_Processor) before routing to the processing queue.

Use when after writing parsed spectra to a new MSP file using mssearchr's MSP writer, or when integrating MSP files from external sources into an R analysis pipeline.

Use when you need to open an mzML file in pymzML and must automatically select the correct handler based on file extension (.mzML, .mzML.gz, .db) and—for gzip files—indexed vs. non-indexed compression status. Use it whenever FileInterface.

Use when you have mass spectrometry data stored in a non-standard format (SQLite database, custom indexed gzip files, or other database backends) and want to enable pymzML's Reader to access it with both random-access by spectrum ID and sequential iteration capabilities, avoiding the need to.

Use when when you need to parse mzML data stored in a format beyond the built-in mzML, mzML.