Claude Skills by HolobiomicsLab

Use when when designing or integrating a file parser for mass spectrometry formats (.raw Thermo RAW format, .mzml XML-based format) in a metabolomics processing pipeline, or when reading legacy or heterogeneous instrument output where file integrity cannot be guaranteed.

Use when when you have retrieved a user database entry (sequence or structure data) from the MassSpecBlocks backend and need to enable mass spectra analysis in the open-source CycloBranch program, or when you need to export NRP sequences and building-block annotations for consumption by external.

Use when after generating candidate formula/adduct pairs from m/z database matching but before final metabolite annotation. Use it when you have observed isotopic patterns from feature detection (e.

Use when when you have loaded a chemical database (e.g., HMDB pickle file) and need to understand how many distinct molecular formulas remain after filtering for a specific m/z range (e.g., 100–1000) and MS acquisition level (typically MS level 1).

Use when you are evaluating or selecting FT-ICR MS software for a specific metabolomics workflow and need to assess which tools support your required analytical dimensions (e.g., Van Krevelen diagrams, PERMANOVA, thermodynamic indices, chemodiversity metrics, transformation networks).

Use when you have FT-ICR MS peak abundance data in Formularity .

Use when after applying mass calibration functions (LedFord, linear, or quadratic equations) to an FT-ICR transient or magnitude-mode dataset, before running SearchMolecularFormulas. Specifically, validate recalibration when: (1) comparing recalibrated spectra against reference calibration files (e.

Use when when you have a processed Bruker Solarix FT-ICR mass spectrum object (from ReadBrukerSolarix) and a reference peak list file (SRFA.

Use when you have loaded an FT-ICR raw spectrum (e.g., ESI_NEG_SRFA.d in Bruker or ThermoFisher .raw format) and need to identify the m/z positions and intensities of individual mass spectral peaks.

Use when you have a Bruker Solarix FT-ICR transient file in .d format (e.g., ESI_NEG_SRFA.d containing ser and fid files in CompassXtract format) and need to programmatically load it into a Python environment for signal processing, calibration, and mass spectrum generation.

Use when when you have abundance-normalized FT-ICR MS peak data with assigned molecular formulas and need to distinguish between richness (total number of distinct metabolites) and functional diversity (diversity in metabolic potential).

Use when you have a Galaxy Master branch installation (or specific commit c429777c93680dcee449fe410f5360afbe673758) and need to add metabolomics tools from Galaxy-M.

Use when you have access to a computation-times table or performance log documenting rendering execution times for multiple visualization examples across different plotting backends (e.

Use when after fitting candidate GAM splines with B-spline basis functions across a range of basis dimensions (k values 12–20) to anchor feature pairs (m/z and retention time coordinates).

Use when you have vendor-format GC-CI-MS raw data from a stable isotope labeling experiment, a list of targeted compounds with known monoisotopic m/z, retention time, and molecular formula, and you need to extract per-isotopologue area and intensity values across multiple samples with consistent.

Use when when you need to verify that a GitHub Actions workflow (such as a development build or release pipeline) executes without fatal errors and produces expected artifacts. Use this skill when the workflow is already configured in a repository (e.g., a .yml file in .

Use when you have a GitHub repository containing scientific records (e.

Use when when searching unknown mass spectra against large high-resolution spectral libraries for open modification identification and you need to reduce search latency from seconds to milliseconds while maintaining sensitivity and specificity.

Use when you have a large spectral library and many query spectra to search against it, and you need to identify both unmodified and open-modification peptides with strict false discovery rate control.

Use when after constructing a NetworkX graph object from structural clusters (via MamsiStructSearch), when you need to interactively explore feature relationships or publish a static network diagram showing isotopologue links, adduct relationships, and cross-assay connections.

Use when you have a fitted alignment model (e.g., metabCombiner object with pre-aligned feature pair candidates and RT spline mapping) and known shared compound identities (ground truth) from reference datasets, and you need to determine which combination of three or more continuous parameters (e.

Use when when you have an indexed gzip-compressed mzML file (mzML.gz with an internal index) and need to retrieve specific spectra or chromatogram data blocks by integer index without decompressing the entire archive.

Use when you have imported mass spectrometry spectra from multiple file formats (mzML, mzXML, msp, metabolomics-USI, MGF, JSON) and need to standardize their metadata fields before performing spectral comparisons, similarity scoring, or library construction.

Use when when processing open mass spectrometry library (OMSL) data that may contain duplicate spectral records (e.

Use when when you have normalized or vectorized mass spectrometry spectral data (m/z and intensity pairs converted to feature vectors) and need to construct an LSH index for fast nearest-neighbor retrieval of similar spectra.

Use when when you have a large spectral library (thousands to millions of spectra) represented as high-dimensional vectors and need to match unknown query spectra against it, but exhaustive pairwise comparison is computationally prohibitive.

Use when you have implemented or are evaluating an algorithmic or system optimization (e.g., MASST+) that claims to reduce execution time, and you need to quantify and statistically validate the speedup against a baseline system (e.g., MASST).

Use when you have high-resolution mass-spectrometry data (Orbitrap, GC-CI-MS) from stable-isotope labeling experiments and need to quantify isotopologue abundances for a defined list of target compounds.

Use when you have uploaded a delimited data file (CSV, TSV, or semicolon-separated) containing at least m/z values and intensity columns from HRMS analysis of a complex sample, and you need to (1) verify that column keywords are correctly recognized, (2) compute derived metrics such as Normalized.

Use when you have uploaded a numeric column (e.g., H/C ratio, O/C ratio, or other derived properties from high-resolution mass spectrometry) and you are generating a histogram in Punc'data's Canvas tab.

Use when you have executed batch spectral searches against two or more domain-specific MASSTs (via the Fast Search API or individual MASST tools) and need to combine the resulting hit lists, match scores, and taxonomic/metadata annotations into a single ranked table for downstream analysis.

Use when when you have pre-computed Word2vec spectrum embeddings and need to perform fast approximate nearest-neighbor retrieval from a library of millions of spectra (e.g., NIST 2017, MassBank, or in-silico predicted spectra).

Use when you have a dashboard_data.json file (JSON export from the msFeaST pipeline) and need to interactively explore quantification tables, metadata, and spectral data on a desktop machine (macOS, Linux, or Windows).

Use when you are building the initial data ingestion step of a high-throughput MS platform and need to accept raw MS files from users or instruments via a web interface. Use this skill when you require automated validation of vendor-specific formats (Thermo .

Use when you have preprocessed mass spectra (mz/intensity pairs in MGF format) and need to convert them into a compact, fixed-dimensional representation suitable for fast similarity computation and clustering.

Use when you have mzML mass spectrometry files that need both compression and rapid random access by spectrum ID (e.g., direct retrieval of spectrum 2540 without sequential scanning).

Use when when implementing an igzip parser, decoder, or validator that must interpret the custom header format; when debugging igzip file corruption or encoding errors; or when extending pymzML's igzip support to handle new index schemes.

Use when when processing collections of in-silico mass spectra from OMSLs (Open Mass Spectra Libraries) where the adduct field is absent, null, or not explicitly specified in the source file;

Use when you have a GNPS DBResult file from spectral library matching that lacks ClassyFire superclass, class, and subclass columns, and you need to restore chemical ontology annotations by submitting compound identifiers to external batch classification services for re-annotation.

Use when gNPS has stopped supplying ClassyFire ontology information for spectral library matches (as of the ConCISE documentation snapshot) and you need to manually retrieve chemical classifications.

Use when you have candidate library matches from MS2Deepscore ranking (top 2000 spectra per query) with InChIKey annotations, and need to quantify structural similarity between query and candidate compounds to inform downstream match ranking and filtering by MS2Query's random forest model.

Use when you have compressed mzML.gz files and need to retrieve specific spectra by numeric identifier without decompressing the entire file.

Use when when you have implemented or modified a data ingestion module (e.g., mzML parser) and need to verify that file deserialization produces correct internal representations.

Use when processing raw MS intensity tables from long measurement sequences where you observe systematic, time-dependent changes in signal magnitude (e.g., progressive increase or decrease in peak intensity across a run).

Use when you have raw mass spectrometry intensity measurements from sample analyses and need absolute quantitative concentrations.

Use when after peak recognition has identified features (m/z and retention time pairs) across one or more MS replicates, and you need to collapse multiple intensity measurements per feature into a single representative value per sample.

Use when after applying batch correction (e.g., via pycombat) to a multi-batch feature table, to validate whether the correction has reduced systematic intensity differences between batches.

Use when when you have a web-based visualization of aligned mass spectrometry peaks (m/z, intensity, retention time, alignment quality metrics) and need users to interactively explore subsets of those peaks by applying constraints on intensity thresholds, alignment score cutoffs, or peak.

Use when when you have a high-resolution mass spectrometry dataset with m/z values and need to generate Kendrick mass plots where users should choose between plotting raw m/z or computed Normalized Kendrick Mass (NKM) on the x-axis.

Use when you have mass spectrometry data (chromatograms, spectra, peak maps, mobilograms) that you want to visualize interactively using pyOpenMS-viz, and you need to select between bokeh and plotly backends based on execution time and feature parity.