Claude Skills by HolobiomicsLab

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 when you have Thermo Fisher Scientific .raw files from an Orbitrap instrument and need to identify the m/z value and corresponding intensity of the most abundant ion in each MS1 scan for quality control, method optimization, or feature extraction in a modular R-based proteomics pipeline.

Use when your m/z peak data spans multiple batches (recorded in metadata as a batch ID column) or samples have varying concentrations that are documented in metadata.

Use when when analyzing untargeted LC/HRMS data from population-scale projects (n > 500) spanning multiple sample batches or instrument runs, peaks with identical or near-identical m/z values appear at systematically shifted retention times across batches due to instrument drift, column aging, or.

Use when when you have log-transformed metabolite abundance data from multiple batches (e.

Use when after applying CordBat batch correction to a log2-transformed metabolite matrix from multi-batch metabolomics data, you want to quantitatively and visually assess whether the correction successfully reduced batch effects.

Use when when you have a repository containing hundreds or thousands of structured records (e.g., MassBank records in standardized format) that must be validated for correctness before release or merge.

Use when you have generated a peak table or feature list output file from an external peak-picking tool (MZmine, XCMS, MS-DIAL, or Compound Discoverer) in its native export format and need to ingest it into LipidMatch for lipid identification without manual column remapping or format conversion.

Use when when ingesting spectra from multiple open mass spectrometry libraries (OMSLs) in .mgf, .msp, .json, or .csv format and you observe mixed experimental protocols, inconsistent adduct annotations, or partial metadata.

Use when when evaluating whether a new or candidate mass spectrometry processing library (e.g., spectrum_utils) offers faster spectrum processing throughput than established alternatives (e.

Use when you have a set of centroided mzML files from LC-HRMS analysis, a list of target metabolites with known molecular formula and retention time windows, and you need to create a reference dataset to benchmark the reliability of NPP tools (XCMS, MZmine, MS-DIAL, etc.) on the same files.

Use when when you need to quantify and document the computational cost of a multi-stage analysis pipeline (such as MetaboDirect) across varying input sizes (e.

Use when you have a compressed file format (e.g., igzip) with a custom binary header structure that encodes metadata (index-to-offset mappings) in a fixed layout, and you need to parse or generate compliant files.

Use when you have raw mass spectra data (MGF format with m/z/intensity pairs) that need to be clustered rapidly, especially on large-scale proteomics datasets (millions of spectra).

Use when when you have raw peak table data from mass spectrometry or other metabolomic instruments in either a standardized tabular format or one of 12 common software tool outputs (e.

Use when you have a known parent drug chemical formula and aim to predict its metabolite landscape prior to or during high-resolution mass spectrometry analysis (mzML).

Use when when you need random access into a large text or XML file that you want to keep compressed, where the file has natural logical divisions (chapters, spectra, records) that can be written independently.

Use when after feature extraction and alignment when you have a numerical feature table (CSV or dataframe) with intensity values across retention time or m/z dimensions, and you need to identify and rank peaks by signal quality and prominence rather than relying on all extracted features equally.

Use when you have received a Bruker Solarix FT-ICR raw data directory (.d format, containing CompassXtract output or native ser/fid transients) and need to import it into a Python-based analysis workflow for FT-MS signal processing, calibration, or molecular formula search.

Use when you have acquired Bruker Solarix FT-ICR-MS raw data (e.g., ESI_NEG_SRFA.d) and need to programmatically access the transient time-domain signal and metadata for zero-filling, apodization windowing (e.g., Hanning), or mass calibration.

Use when you have Bruker Solarix raw transient data (.d folders containing ser or fid files) from an FT-ICR instrument and need to load it into a Python environment for noise thresholding, peak picking, mass calibration, or molecular formula assignment.

Use when when you need to validate that a development build release workflow (such as dev_build_release.yml for a mass spectrometry data processing project) executes without failure and generates artifacts for downstream testing or distribution.

Use when you have retrieved a user database entry (sequence or building-block structure record) from the MassSpecBlocks backend and need to generate a file in CycloBranch format for mass spectra analysis, interpretation, or sharing with collaborators using the CycloBranch software.

Use when when you have an indexed gzip-compressed mzML file and need to retrieve individual spectra or chromatograms by index without sequential file reading or full decompression. Typical scenario: you want spectrum[42] from a 10 GB indexed mzML.gz file and need sub-second access time.

Use when you have a mature C++ library (like OpenMS) with stable APIs that you want to make accessible from Python environments, and you need to preserve performance-critical C++ execution while supporting rapid prototyping or integration into Python-based data pipelines (e.

Use when when integrating an R package that wraps a compiled .NET assembly (such as rawrr), you need to verify that the internal dispatch mechanism between the R layer and the C# layer is operational before attempting to read actual raw data files.

Use when you have raw mass spectrometry intensity data from targeted analytes and a set of calibration standard measurements with known concentrations.

Use when after applying polynomial m/z domain recalibration using a reference peak list (e.g., SRFA.ref) to a Bruker FT-ICR dataset. Use this skill to verify that calibration has converged and that mass error statistics support reliable downstream annotation.

Use when after a mass spectrum has been matched against a reference m/z file (e.g., SRFA.ref) and a sufficient number of calibration points (≥5) have been identified within a given PPM tolerance window.

Use when after generating candidate molecular formula and adduct pairs for detected m/z features, when you have observed isotopic patterns from feature detection and need to reduce annotation ambiguity by eliminating candidates with poor isotopic fit.

Use when when rescoring PSMs from a search engine with MS²Rescore and you need to (1) constrain computational cost by reducing the number of candidates fed to feature generators and rescoring engines, (2) control false discovery rate correctly by removing lower-ranking PSMs before final statistical.

Use when you have (1) a set of candidate structures generated by in silico fragmentation (e.g., MetFrag output), (2) one or more seed nodes with known spectral library matches or identity scores, and (3) a fragmentation relationship graph connecting candidates.

Use when when searching high-resolution mass spectra against spectral libraries and you need to identify both unmodified and post-translationally modified peptides while maintaining strict control over false positive identifications.

Use when when performing open modification spectral library searches on high-resolution mass spectra where computational cost is prohibitive if every query is scored against every library spectrum.

Use when you have retrieved chemical formulae and metadata from two or more of HMDB, ChEMBL, or PubChem and need to merge them into a single searchable database without formula duplication.

Use when when you have a parent drug's raw chemical formula and need to predict its potential metabolites in mass spectrometry data (mzML format) to match against observed peaks.

Use when you have .msp spectral library files with compound names but lack standardized chemical identifiers (SMILES, InChI, InChI Key, CAS number, IUPAC names, or molecular formulas).

Use when when you have an unknown metabolite compound with mass spectral data, have retrieved candidate structures from a molecular structure database (PubChem, HMDB), and have obtained predictions of structurally related metabolites from a deep-learning semantic similarity model (e.g., DeepMASS2).

Use when you have retrieved multiple candidate structures from a molecular structure database (e.g., PubChem, HMDB) for an unknown compound, and you have predictions of structurally related metabolites from a deep-learning mass spectral model (e.g., DeepMASS2).

Use when working with mass spectrometry spectral libraries (GNPS, MoNA, MTBLS1572, MassBank) that have been preprocessed by prior teams but may contain formatting errors or entries with missing/null SMILES fields.

Use when when you have .msp mass spectrometry metadata containing chemical identifiers (e.g., compound names or SMILES strings) and need to compute derived chemical properties (e.g., InChI, InChIKey, molecular formula) locally without network latency or service availability constraints.

Use when you need to export a stored chemical structure (sequence or building-block entry) from the MassSpecBlocks database to enable mass spectra analysis in CycloBranch or when preparing structures for import into other cheminformatics workflows that require a standardized structure interchange.

Use when you have a set of query chemical compounds (as SMILES, names, or ChemMine objects) and a reference library organized into type groups (e.g., Type A–E chemical sets), and you need to assign each query compound to its -matching type based on structural similarity.

Use when after compound database dereplication with SIRIUS or MetFrag has produced candidate annotations (CSV or JSON format), and you need to filter implausible structures, compute standardized molecular descriptors, and rank candidates by confidence before reporting final metabolite.

Use when you have a list of query chemicals (compound names or SMILES) and a reference library organized by chemical groups (e.g., Types A–E, GroupA/GroupB), and you need to assess which library compounds are structurally similar to your queries, retrieve their categorical annotations (e.

Use when you have a formula-assigned FT-ICR MS dataset (CSV or tab-delimited table with molecular formulas and mass values) and need to quantify the molecular chemodiversity, elemental composition diversity, or complexity of environmental organic samples for cross-sample comparison or.

Use when when you have sum-normalized peak-abundance matrices from FT-ICR MS data with assigned molecular formulas and need to compare metabolite diversity between treatment groups (e.g., inoculated vs. control samples).

Use when you have mass spectrometry data loaded as a pandas DataFrame with at minimum two numeric columns representing retention time and intensity values, and you need to generate a chromatogram visualization for exploratory analysis, quality control, or publication.