Claude Skills by HolobiomicsLab

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 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 when you need to understand the computational structure of a modular scientific application (especially one with multiple subprojects or plug-in architectures) and static code inspection alone does not reveal algorithm entry points, parameter propagation, or intermediate data.

Use when when you have a query MS/MS spectrum (m/z and intensity pairs) and need to find potential structural analogues or exact matches in a large spectral library. Apply this skill after preprocessing your spectra (e.

Use when after computing InChIKey and neighbourhood scores for library match candidates, you need to write results to a persistent format (CSV, JSON, or database) for storage, sharing, and interpretation.

Use when you have mass spectrometry imaging data in Cardinal format (versions 2.

Use when you have a flat table of structure-organism pairs or entity records and need to summarize their distribution across categorical bins (e.g., organism counts binned by number of associated structures in categories: 1, 1–10, 10–100, >100).

Use when after running qc_summary() on a filtered mpactr object when you need to understand the distribution of ions across filter status categories (passed vs. failed filters) and want to visualize or report the count and percentage composition of each status group.

Use when you have IM-MS lipidomics data acquired on samples spiked with fully labeled U13C lipid standards (e.g., U13C yeast extract), and you need to assess whether systematic CCS deviation exists between your instrument's measured values and the DT CCS N2 reference library for U13C labeled lipids.

Use when when you have received or cloned a CCS reference library (such as the DTCCSN2 library for U13C labeled lipids) bundled with lipidomics software and need to verify its integrity, understand its lipid class composition, or extract metadata before using it for CCS bias calculation or.

Use when apply CLR transformation when working with microbiome or metabolomic relative abundance tables that will be input to multivariate regression or neural network models.

Use when you have vendor-independent centroided mzML files from data-dependent acquisition (ddMS2) HRMS experiments and need to extract a reproducible feature list with mass, chromatographic, and intensity dimensions as input to PFAS prioritization, suspect screening, or other MS-based analyses.

Use when you have peak-picked features with m/z, drift_time, retention_time, and intensity columns, and you need to identify monoisotopic peaks and their charge-state-specific isotopologue members (e.g., singly charged C13-substituted species).

Use when training neural networks on MS/MS spectra (or similar scientific data) where you need to preserve model states that improve validation performance.

Use when you have computed low-dimensional embeddings (e.g., t-SNE coordinates) or clusterings of mass spectra and need to validate that the learned representation space organizes molecules by chemical taxonomy.

Use when you have a GNPS mass spectral molecular network (in graphml or cytoscape format) and want to enrich its nodes with chemical class information derived from GNPS public library spectral matches.

Use when after running formula inference on a benchmark dataset with known formula and adduct ground truth (e.g., NPLIB1, NIST20, or CASMI 2022). Apply this skill when you need to quantify ranking performance, isolate the contribution of specific model features (e.

Use when you need to feed chemical formulas into a neural network-based formula scorer (such as MIST-CF) that must learn data-dependent representations of formula structure and composition.

Use when after a machine learning model has generated predicted molecular structures (connectivity graphs and molecular formulas) from 1D NMR spectra. Use it to quantify accuracy on a held-out test set, measure degradation when applying the model beyond its training scope (e.

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 a list of chemically known compounds and need to validate that an MS processing pipeline (e.g., mzExacto) correctly retrieves their characteristic m/z, retention time, match factor, and area values from GC-MS data.

Use when when you have raw SMILES strings collected from multiple external databases that require standardization and deduplication before integration into a unified chemical structure database. Specifically, apply this skill when you need to convert interim/tables/0_original/structure/smiles.tsv.

Use when when you have query chemicals identified by GC-MS (with Match.Factor values) and need to verify structural similarity against a reference chemical library to confirm compound identity or detect structural analogs (e.g., isomers or homologs).

Use when when you have a GNPS molecular network (graphml or cytoscape format) and wish to annotate it with chemical class labels or MS2LDA-derived mass2motifs to highlight shared structural features or chemical families across spectral clusters.

Use when after spreadOut() has converted raw CSV peak data into a structured list, when you have one or more Compound.Name entries from GC-MS that may be ambiguous, non-canonical, or missing standardized properties (exact mass, published retention times, reactive groups, database presence).

Use when you have LC-MS/MS data preprocessed with MZmine2 into an MGF file (containing MS1 and MS2 spectra) and a feature table (peak areas per sample), and you want to relate MS1 features to each other based on predicted molecular substructures and chemical properties rather than arbitrary.

Use when after applying biotransformation rules to generate candidate product structures from input molecules, when the same transformed structure can be produced via multiple transformation pathways or rule applications, and you need a single canonical representation while tracking which rules and.

Use when when you have a set of molecules with known chemical structures and need to prepare them for classification or prediction tasks.

Use when you have SMILES strings or molecular structure files (e.g., from a synthetic drug database) and need to feed them into a deep learning model like PS2MS, NEIMS, or DeepEI that expects numerical feature vectors.

Use when when you have MS/MS spectra with known chemical structures (InChIKeys or SMILES) and want to validate whether a novel or existing spectral similarity scoring method actually reflects true chemical structural similarity.

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 have translated or raw SMILES strings from a chemical structure curation pipeline and need to remove invalid chemical structures, resolve sanitization errors (e.

Use when when you have a set of query chemicals (e.g., ethyl hexanoate, methyl salicylate) and need to find their -matched structural analogues within a reference library (e.

Use when you have a mass spectral library (EI or MS2 format) loaded into R via read_lib() and possess either MOL files (from Lib2NIST export) or an SDF file containing the corresponding chemical structures, but the library entries lack SMILES fields or have incomplete structure information.

Use when when you have raw molecular structures in SMILES or SDF format that must be prepared as input to a descriptor-based classifier (e.g., BitterPredict).

Use when when you have access to source code or algorithmic documentation of a metabolite generation pipeline (such as MAGMa's job subproject) and need to understand, validate, or reconstruct the transformations that convert a parent compound into enumerated metabolite candidates.

Use when you have computed a histogram of pairwise mass differences from MS peaks and want to determine which observed mass differences correspond to known chemical species such as matrix adducts (e.g. [M+Na]+, [M+K]+), salt ions, or neutral losses.

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 when you have access to the source code of a chemo-informatics tool (e.

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 when you need to simulate LC-MS/MS data for fragmentation strategy development and do not have (or wish to augment) real experimental chromatograms. Specifically: (1) you have a list of known or sampled chemical compounds with molecular formulae;

Use when you have mass spectrometry data loaded as a Pandas DataFrame with retention time and intensity columns, and you need to visualize the overall or mass-trace-specific signal intensity distribution across the chromatographic separation.

Use when after feature detection when you have a feature table with m/z, retention time, and intensity columns, and you need to group features into empirical compounds (putative metabolites) that account for isotopologue patterns and multiple adduct forms arising from a single underlying analyte.

Use when you have centroided mzML files from LC- or GC-HRMS instruments (acquired in data-dependent mode with ddMS2) and need to systematically identify chromatographic peaks, measure their mass and retention time coordinates, and quantify their intensities before applying PFAS-specific.

Use when you have retention time predictions from a source chromatographic method and need to predict retention times for a target chromatographic method, but have limited calibration data (10–100 molecules) measured on both methods.

Use when after running tardisPeaks() with screening_mode=TRUE on centroided .mzML LC-MS data, when you need to visually inspect whether the 10 target compounds (internal standards and endogenous metabolites) were correctly detected within their expected m/z and retention time windows.

Use when after loading centroided .mzML LC-MS data and defining a target list (compound ID, name, m/z, RT, polarity) when you need to: (1) automatically locate and integrate peaks for known compounds across multiple runs; (2) generate per-target and per-run quantitative metrics;

Use when you have raw mzML files from an FTICR-MS or other direct-injection MS instrument and need to identify discrete chromatographic peaks across the m/z and retention-time dimensions. Use this skill when you must isolate individual ion signals before applying calibration corrections (e.

Use when after constructing baseline-corrected mass tracks (either composite across samples or per-sample) when you need to identify individual chromatographic peaks for feature extraction in LC-MS or GC-MS metabolomics workflows.

Use when analyzing GC-MS data containing overlapping chromatographic peaks—a common scenario in untargeted metabolomics and environmental screening where sample complexity or chromatographic resolution limitations cause co-elution of structurally similar or temporally proximate compounds.