Claude Skills by HolobiomicsLab

Use when when you have implemented the same visualization (e.g., chromatogram, spectrum, peakmap, mobilogram) across multiple plotting backends in pyOpenMS-viz and need to determine which backend minimizes rendering latency.

Use when when you have received a GitHub repository URL for a computational workflow (e.g., ENPKG full workflow) and need to prepare your local machine to execute the workflow.

Use when when you need to understand how a complex feature or architectural pattern was implemented in a codebase, particularly when the current README or documentation does not fully explain the control flow, decision criteria, or parameter passing between subsystems.

Use when you need to validate that a published software tool (e.g., MassQL) executes correctly in your environment, reproduce published results, or contribute to development.

Use when when you have high-dimensional replicate experimental data (e.g., metabolomics, proteomics, genomics assays) where technical or biological variability threatens reproducibility, and you need to distinguish genuine reproducible signals from noise without assuming normality.

Use when you have high-dimensional replicate experiment data (metabolomics, proteomics, or genomics) with multiple biological or technical replicates per sample, and you need to assess which features are reproducible across replicates and which sample pairs show consistent reproducibility patterns.

Use when you have run an NPP tool (XCMS, MZmine 2, etc.) on mzML files and need to assess whether the alignment stage preserved peak detection fidelity and isotopologue abundance ratios.

Use when you have an XCMS CentWave feature extraction output table containing multiple features with near-identical m/z values and retention times (e.

Use when when you have imported mass spectrometry data in .raw, .d, or mzXML format and need to generate a TIC plot to visualize overall sample ionization intensity as a function of retention time.

Use when you have extracted feature tables (m/z, intensity, retention time) from multiple breath samples (mzML or mzXML files) via feature extraction, and need to identify which features represent the same volatile organic compound (VOC) across samples to enable cross-sample intensity comparisons.

Use when you have LC-HRMS profile-mode data (e.g., netCDF or mzML format) and need to convert detected or reference chromatographic peaks into fixed-size 2D arrays (rt × mz regions) to train or apply a convolutional neural network for peak classification and bounding-box prediction.

Use when you have XCMS-aligned feature tables with retention time values and need to compute pairwise feature similarity.

Use when you have a complete TIC (total ion current) table indexed by retention time and need to visualize or analyze only a specific time interval—for example, when isolating a chromatographic peak region (9–10 min) before plotting or when a mass spectrometry run spans a time range wider than the.

Use when you have a normalized peak-abundance matrix from FT-ICR MS data (peaks as rows, samples as columns) and need to compare the number and diversity of detected molecular species across experimental conditions—for example, to test whether inoculation or environmental perturbation alters the.

Use when you have implemented or are validating a reader/writer library for a mass spectrometry file format (such as mzPeak, mzML, or similar), and need to confirm that data parsed from disk can be written back without loss of information.

Use when when a new version or variant of a tool claims performance improvements over a prior version (e.g., MASST+ vs. MASST), and you need empirical evidence that the claimed speedup (e.g., ~100-fold reduction in search time) is real, reproducible, and quantifiable.

Use when when you need to empirically validate that one mass spectrometry data processing library achieves higher throughput than competing alternatives. Specifically: you have multiple candidate libraries (e.

Use when you have obtained a Rust source repository (e.g., mzpeak_prototyping) and need to compile it into a working command-line converter tool or library. Use this skill when the source includes a Cargo.

Use when when you have access to Rust source code in a repository with a Cargo manifest (Cargo.toml) and need to verify that a library's read and write APIs produce byte-equivalent or structurally equivalent output.

Use when you need to create a new backend for the Spectra package that provides MS data from a new source (file format, database, or computational resource) not covered by existing backends (MsBackendMzR, MsBackendMemory, MsBackendSql, etc.).

Use when you are building a new data representation or storage strategy for MS spectra (e.g., on-disk HDF5, SQL database, remote file access) and need to integrate it seamlessly into workflows that use the Spectra package.

Use when you need to verify that an S4 replacement method (e.g., `mz<-`) in a bioinformatics backend class correctly validates input data using vectorized operations on NumericList or similar container objects, rather than inefficient loops or apply functions.

Use when when designing or optimizing S4-based data backends (such as MsBackend subclasses) and you need to decide whether to pre-populate all slots with complete data structures or defer initialization until data access.

Use when you have high-throughput replicate measurements (e.g., mass spectrometry metabolomics) on biological replicates and need to identify which sample pairs exhibit reproducible feature signals across a threshold (typically 75% reproducibility).

Use when when working with feature abundance tables (rows=features, columns=samples) where the feature count exceeds 10,000 and you need to infer a network of correlations between features.

Use when you have a collection of records in a standardized format (e.g., MassBank plain-text or structured records) that must be validated before commit or publication.

Use when you have txt files exported from Sciex MultiQuant (>v3.0.3) containing QCpool samples measured at regular intervals during mass spectrometry sequences, and you need to validate whether the file structure is compatible with the QComics package before parsing and quality assessment.

Use when after computing a scoring function over all possible genomic-metabolomic candidate pairs (e.g., all 2966 MIBiG-GNPS BGC-spectrum pairs), when you have a subset of known validated links and need to assess whether the scoring function ranks them significantly higher than expected by chance.

Use when you have PSM output files from one or more search engines (e.g., MaxQuant msms.txt, MSGFPlus .mzid, Sage .sage.

Use when when you have a calibrated FT-ICR mass spectrum (e.g., ESI-NEG mode) and need to decide between rapid single-assignment (first_hit=True) and exhaustive multi-assignment (first_hit=False) modes.

Use when when you have two or more implementations of a spectral search tool (e.g., MASST vs. MASST+) and need to quantify whether claimed performance improvements (e.g., '100-fold speedup') are reproducible.

Use when you have an unknown metabolite with unknown mass spectrum and need to prioritize structural candidates from databases (PubChem, HMDB) by their likelihood of being the true compound.

Use when you have uploaded a delimited data file (comma-, semicolon-, or tab-separated) with a header row into Punc'data and need to ensure that each column is correctly mapped to its semantic role (m/z, intensity, formula, or other mass spectrometry attributes) before proceeding to analysis.

Use when before executing a bioinformatics pipeline that depends on multiple R packages with strict version constraints (e.g., DaDIA, which requires R ≥4.0, XCMS ≥3.11.4, and metaMS =1.25.1).

Use when when you have a list of polypeptide sequences (plain text, CSV, or FASTA format) and need to compute their monoisotopic mass and average mass for downstream mass spectrometry interpretation, database matching, or physico-chemical property annotation.

Use when when parsing mzML or other blockwise-structured scientific data files where analysis requires both sequential scanning (e.g., iterating all spectra) and random direct access by identifier (e.

Use when when you need to support custom data storage backends (e.g., SQLite, HDF5, or proprietary databases) for mzML-equivalent spectra, but the pymzML parser expects only standard file formats (mzML, mzML.gz, indexed gzip).

Use when matching query mass spectra to a spectral library in the presence of unknown post-translational modifications (PTMs) or non-enzymatic modifications.

Use when you have raw total ion current (TIC) traces extracted from mass spectrometry samples (e.g., from qTOF, orbitrap, or FTICR instruments) and need to identify and flag scans with anomalous peak intensities before feature detection.

Use when when you have a 1D intensity array from a mass spectrum (m/z or retention time dimension) and need to identify and rank local maxima that are biochemically meaningful rather than noise-driven.

Use when immediately after peak detection in the IDSL.IPA workflow, when you have a list of candidate peaks extracted from EIC data and need to remove noise-dominated signals before downstream peak property evaluation and annotation.

Use when you have a preprocessed unknown sample spectrum (m/z peaks and intensities) and need to identify the most likely species or reference entries by scoring it against a database of known spectra.

Use when when you have a peak intensity matrix (rows = metabolite features, columns = samples) with corresponding compound annotations (peak ID → KEGG/ChEBI ID mapping), and you need to rank pathways or metabolite set groupings (KEGG pathways, Reactome pathways, GNPS Molecular Families, MS2LDA.

Use when building a transformer-based neural network for chemical formula ranking or classification from mass spectrometry spectra, and you need to encode categorical chemical formulas (e.

Use when after running SIRIUS on a mass spectrometry feature set and obtaining compound_identification.tsv output containing Zodiac and Cosmic confidence scores, apply this filter to eliminate low-confidence SIRIUS annotations before downstream prioritization or chemical class analysis.

Use when you have SMILES strings for candidate novel psychoactive substance structures and need to convert them into a machine-readable molecular representation before computing descriptors, generating mass spectra, or calculating chemical fingerprints.

Use when you have MSBERT-preprocessed spectral datasets (GNPS, MoNA, or MTBLS1572 format) with SMILES annotations before training a spectral embedding or compound identification model.

Use when you have a mass spectral library in MSP format (e.g., from NIST, SWGDRUG, or other sources) exported alongside a folder of MOL files, and you need to populate the SMILES field in each library record to enable structure-based filtering, annotation, or downstream MS-DIAL analysis.

Use when you are preparing to run the MetaboDirect pipeline for the first time on a new machine, or you need to reproduce a published benchmark or analysis on a different OS (Windows, Linux, MacOS) and want to guarantee that all six pipeline steps (data pre-processing, diagnostics, exploration.

Use when after making code modifications (bug fixes, new features, or refactoring) to the MS2Query codebase, or when contributing changes via pull request. The skill is essential before pushing feature branches to the repository or merging changes into master.