Claude Skills by HolobiomicsLab

Use when you have mass-spectrometry data files in mixed formats (e.

Use when when you have PSM files from a search engine (e.g., MaxQuant, MSGFPlus, Mascot) and corresponding spectrum files in mzML or MGF format, and the spectrum identifiers in both files use different naming conventions or formats.

Use when after importing raw mass spectrometry data from mzML, mzXML, msp, MGF, or JSON formats using matchms, when you need to ensure that metadata fields (e.

Use when when you have parsed XML elements from an mzML or mzML.gz file (via ElementTree or a similar XML parser) and need to convert those elements into pymzML Spectrum objects for spectrum-level operations such as random access, spectral comparison, or data extraction.

Use when after successfully parsing raw spectral data from one of the supported file formats (mzML, mzXML, msp, metabolomics-USI, MGF, JSON) into intermediate dictionaries containing m/z arrays, intensity arrays, and metadata key-value pairs.

Use when when building a mass spectrometry data import pipeline that must ingest spectra from multiple file formats and produce a unified, queryable representation suitable for metadata validation, peak filtering, and similarity comparisons.

Use when you have mass spectrometry spectral data loaded into a Pandas DataFrame with columns representing m/z (mass-to-charge ratio) and intensity values, and you need to visualize the spectrum to inspect peak patterns, identify high-abundance ions, or compare spectral profiles across samples or.

Use when when selecting a spectrum processing library for high-throughput proteomics or metabolomics workflows, or when optimizing an existing pipeline for computational efficiency. Apply this skill when you have access to representative raw MS data (e.

Use when when you need to retrieve specific spectra from mzML files by custom identifier (integer or string) rather than sequential iteration, especially when the mzML file uses non-standard index formatting that requires a regex pattern to parse spectrum IDs and file offsets.

Use when when you have a mass spectrometry data file (such as mzPeak) that has been read by two or more independent implementations (e.g., Rust, Python/pyarrow, R/arrow) and need to verify that all implementations produce identical spectrum metadata, data types, row counts, and numerical values.

Use when when you have a large MsBackend object and need to (1) select a contiguous or non-contiguous range of spectra for focused analysis, or (2) combine spectra from multiple independently-loaded backends (e.

Use when when you have raw mass spectra from experimental libraries (e.

Use when you have a Sphinx-based documentation project with multiple gallery scripts (e.

Use when you have an mzML file and need to enable random-access spectrum retrieval by integer or string identifiers without holding the entire mzML in memory or decompressing indexed gzip files.

Use when you have mass spectrometry data stored in a SQLite database indexed by spectrum ID and need to retrieve specific spectra by ID (random access via __getitem__) or iterate through all spectra sequentially (via read method).

Use when you have centroided high-resolution Orbitrap mzML files from stable isotope labeling experiments and need to measure isotopologue abundances (M+0, M+1, M+2, etc.) for a defined list of target compounds with 13C or other isotopic labels.

Use when you have LC-HRMS profile-mode data with detected local maxima (from gradient-descent peak finding) and need to prepare them as input for a convolutional neural network trained to classify peaks vs. background signal or to estimate peak boundaries and centers.

Use when when you have prediction scores (softmax probabilities, uncertainties) from a trained deep learning model evaluated on a heterogeneous dataset and you need to determine whether prediction confidence or accuracy varies systematically across structurally distinct or novel compound.

Use when you have a metabolite abundance table (rows=metabolites, columns=samples) from Metabolomics Workbench format and need to test whether specific metabolites or metabolite classes are significantly enriched in particular biological pathways or conditions, beyond what would be expected by.

Use when after statistical analysis has produced a results table with lipid identities, quantitative expression values, p-values, fold-changes, and experimental condition labels.

Use when when you have computed rank-based accuracy metrics (top-1, top-5, top-10 hit rates) for two or more competing methods on the same test dataset (e.g., Orbitrap spectra), and need to determine whether performance differences are genuine rather than noise.

Use when when implementing or initializing an MsBackend subclass to handle mass spectrometry data, you must assign dataStorage and dataOrigin variables after loading m/z and intensity values.

Use when preprocessing open mass spectrometry libraries (OMSLs) or aggregated spectral datasets where structural identifiers are inconsistently populated.

Use when you have a mass spectrum of an unknown metabolite with a known or inferred precursor m/z, you have run a deep-learning semantic similarity model (e.

Use when when a scientific software repository documents multiple standalone tools, web applications, or resources with associated metadata (URLs, publications, taxonomic coverage) in its README, and you need to create a machine-readable inventory for downstream indexing, validation, or reuse.

Use when you have generated or received a mass spectrometry data file in a structured format (e.g., mzPeak, Parquet-based archive) and need to verify it conforms to the published specification before use in analysis pipelines, sharing with collaborators, or publishing.

Use when when you have mwTab-formatted Mass Spectrometry or Nuclear Magnetic Resonance experimental data from the Metabolomics Workbench that must be converted to JSON for API integration, data sharing across systems, or validation against a defined JSON schema.

Use when when you need to quantify and compare the effect of multiple filtering thresholds (e.g., Match.Factor ≥65, ≥80, ≥90) on the size of a retained compound set.

Use when you have a domain-specific language (DSL) grammar specification and raw query strings that must be converted into structured intermediate representations for validation, transformation, or execution.

Use when when you have a small set of matched reference features (isolated, high-quality chromatographic peaks from reference chromatograms that have been aligned to a ground-truth reference list) and need to train a CNN model for peak detection in LC-HRMS profile mode data.

Use when when setting up imzML Writer for the first time on a new machine, or when raw vendor mass spectrometry file conversion fails with ''msconvert not found'' or Docker image unavailable errors. Specifically: on Windows/PC systems before invoking RAW_to_mzML conversion;

Use when you have multiple independent implementations of the same data format reader (e.g., Rust, Python, R versions) and need to verify they produce identical or equivalent output.

Use when when you have received raw MRM lipidomics export files in vendor-specific formats (TSV, CSV) with inconsistent column naming, unparsed lipid identifiers (e.g., 'PC(36:1)' as a single string), and unknown data quality issues.

Use when you have a raw list of target compounds (in .xlsx, CSV, or database form) with heterogeneous column names and layouts, and you need to prepare it for targeted peak detection, EIC extraction, or quality metric calculation in TARDIS or similar LC–MS metabolomics tools.

Use when beginning a targeted LC–MS metabolomics or lipidomics study with a predefined list of compounds (e.g., 10+ targets) and you have centroided .

Use when you have centroided LC–MS data in .mzML format, a validated table of target compounds with adjusted expected retention times (RT in minutes), and you need to extract peak areas and quality metrics across all sample runs.

Use when you have a GNPS DBResult file with spectral library matches that lack ClassyFire superclass, class, and subclass annotations, and you need to augment those matches with standardized chemical taxonomy for consensus classification or downstream analysis.

Use when you have executed a spectrum search against one or more domain-specific MASSTs (microbeMASST, plantMASST, tissueMASST, microbiomeMASST, or foodMASST) and need to systematically extract taxonomic lineages (kingdom, phylum, class, order, family, genus, species, strain) and sample metadata.

Use when raw MS quantification data (feature-by-sample intensity matrix) shows systematic variation in detector response across the run sequence—i.e., when the same analyte produces different intensities at different timepoints in the measurement despite constant sample concentration.

Use when when implementing a multi-task deep learning model that predicts charge, isotope count, and retention time simultaneously from mass spectrometry data, and separate feature extraction branches produce tensors of different semantic dimensions that must be integrated.

Use when when you have an existing tool or library with file-based storage (JSON, CSV, binary formats) that needs to support efficient querying across large datasets, and you want to migrate to a relational database without breaking existing functionality or performance expectations.

Use when you have a mass spectrum scan containing observed precursor ions (m/z, intensity pairs) in a narrow isolation window, a known peptide sequence with specified charge state and stable isotope labeling parameters (atom type, incorporation probability), and you need to determine which observed.

Use when after molecular formula assignment and filtering of FT-ICR MS peaks, when you have elemental composition (C, H, O, N, S, P counts) and need to characterize metabolite thermodynamic stability, degree of oxidation, aromaticity, and unsaturation to compare compound reactivity across samples.

Use when when you have peak-abundance .csv files with assigned molecular formulas (elemental composition: C, H, O, N, P, S) from FT-ICR MS or high-resolution MS and need to characterize the redox and structural properties of the molecular pool—e.

Use when when you have raw MS files (e.g., .raw, vendor-specific formats) stored in a centralized repository and need to invoke a third-party proteomics analysis tool—such as a mass spectrometry feature detector or quantification engine—without manually managing file transfers or output.

Use when after computing pairwise similarity scores between query molecular embeddings and a reference database, apply this skill when you need to distinguish high-confidence candidate matches from spurious matches.

Use when after sliding-window analysis has identified candidate TIC peaks but before parameter extraction from Extracted Ion Chromatograms (EICs).

Use when you have loaded raw mass spectrometry data (mzML, mzXML, or CDF format) into AutoTuner and need to identify peak regions in the TIC trace prior to extracted ion chromatogram (EIC) analysis.

Use when you have raw mass spectrometry data (mzML, mzXML, or CDF format) from at least 3 samples and need to automatically identify candidate peak regions in the TIC chromatogram before extracting ion-level parameters for XCMS or MZmine2 processing.

Use when working with raw FT-ICR transient data (e.g., ESI_NEG_SRFA.d format) prior to noise thresholding and mass-domain calibration.