Claude Skills by HolobiomicsLab

Use when after you have aligned fragment ion pairs between two MS/MS spectra using maximum weight matching and need to compute statistical significance scores for the matched pairs.

Use when after submitting MS/MS data and feature tables to GNPS and receiving a molecular networking job result.

Use when processing raw MS/MS spectra (in MGF, mzML, mzXML, JSON, or MSP format) prior to MS2Query library matching or MS2Deepscore embedding calculation.

Use when after removing precursor and noise peaks from an MsmsSpectrum object when the spectrum contains peaks with highly variable intensities (e.g., one or two dominant peaks with many weaker fragments).

Use when after submitting an LC-MS/MS fragmentation spectrum to the MSNovelist web service and receiving a JSON response containing ranked de-novo structure candidates.

Use when when you have downloaded a GNPS archive (GNPS1 or GNPS2 workflows) and need to map experimental spectra to known library compounds for annotation enrichment. Apply this skill after spectral data (spectra.

Use when after MS2Deepscore has selected the top 2000 candidate spectra from a library based on spectral similarity, and you need to re-rank these candidates to surface the single match (either exact or analogue) rather than rely on raw spectral similarity alone.

Use when when migrating an existing file-based spectral library (stored as JSON, CSV, or binary formats) into a production system that requires frequent subset queries by metadata filters, similarity scoring across large spectral collections, or integration into downstream tools like MS2Query that.

Use when you have one or more MS/MS query spectra (in mzML, mgf, msp, mzxml, json, or pickled matchms format) and a pre-built spectral library stored in SQLite with precomputed MS2Deepscore embeddings.

Use when you have an experimental or public MS/MS spectrum (e.g., from MassBank in msp format, or a raw centroid-mode chromatogram) and need to create a reusable library entry for a known metabolite.

Use when after theoretical spectra have been generated for lipid–adduct combinations with enumerated fragment masses and intensities, and you need to deploy them for downstream MS/MS analysis on Orbitrap (DDA via Excalibur) or SkyLine (targeted/PRM) workflows.

Use when when you have mass spectral libraries from multiple sources (NIST, MoNA, RIKEN, GNPS) in disparate formats (MSP, MGF, MOL folder structures) or with misaligned metadata (e.

Use when when you have experimental UHPLC-HRMS/MS data targeting lipid species not adequately covered by LipidMatch's built-in library (500,000+ species across 60+ lipid types), or when working with specialized applications requiring custom lipid definitions (e.

Use when when you have run MS2Query on query MS/MS spectra and obtained results with library matches that need to be disambiguated into two categories: (1) exact matches (precursor m/z difference near zero) versus (2) analog matches (chemically related but different precursor m/z).

Use when you have MS2 spectral data (precursor m/z, retention time, and fragment ion patterns) from UPLC-HRMS analysis of environmental or biological samples and need to assign compound identities by comparing against known reference spectra.

Use when you have a trained spectral embedding model (e.g., MSBERT, Spec2Vec) and need to benchmark its library matching accuracy against reference spectra on a test dataset.

Use when you have tandem mass spectrometry data (LC-MS/MS in MGF, mzXML, mzML, or mzData format) and genomic data from a target organism, and you want to identify RiPPs by matching experimental spectra against a database of predicted post-translationally modified RiPP structures derived from.

Use when you have MS/MS spectra (.mgf) and candidate metabolite annotations (with m/z, retention time, chemical identifiers) linked to a known organism or taxon, and you want to rank annotations by both spectral similarity AND biochemical likelihood in that organism's lineage.

Use when you have one or more MS/MS spectra in .mgf format (or USI identifiers) and need to: (1) identify unknowns by searching against domain-curated reference data; (2) assign Level 2 metabolomics annotations via GNPS library matches; (3) aggregate matches across organism lineages (e.

Use when when building a comprehensive reference spectral library for metabolomics or chemical identification, you have multiple source libraries in different formats (msp, mgf, NIST binary) and ionization modes (positive/negative MS/MS or EI) that need to be combined into a single.

Use when you have a compiled EI or MS2 library object (read from MSP format via read_lib) and access to NIST ri.dat and USER.DBU files; you need to populate RI values for capillary GC-MS workflows.

Use when when ingesting mass spectral libraries (EI or MS2) where SMILES information is embedded in the Comment field rather than in a dedicated SMILES metadata field—particularly common in MoNA GC-MS and LC-MS/MS exports.

Use when you have deconvolved GC-MS spectra (from overlapping chromatographic peaks) in MGF or mzTab format and want to group chemically related compounds, visualize their similarity relationships, and identify spectral families without prior library matching.

Use when after generating in-memory lipid spectra (with m/z, intensity, and metadata such as lipid class, fatty acid composition, and adduct type) when you need to export those spectra as a reusable MSP-format spectral library for downstream identification tasks in Excalibur, Skyline, or NIST MS.

Use when when evaluating a trained spectral embedding model on publicly available datasets (GNPS, MoNA, MTBLS1572, MassBank, or MassSpecGym) and you need to report averaged performance metrics with standard deviation to demonstrate robustness and reproducibility.

Use when you have extracted MS1 and MS2 scans (in mzML/mzXML format) from raw chromatogram files and possess user-provided metadata (retention time, m/z, compound name, molecular weight, annotation fields) that you need to bind together into a queryable spectral library record for local compound.

Use when after harmonizing MS/MS spectra and metadata fields (compound identifiers, adduct annotations, collision energies, instrument types) to a common schema, and before exporting the spectral library to standardized formats (mzML, mzTab, or repository-native format).

Use when you have an unknown experimental mass spectrum (e.g., from liquid chromatography–mass spectrometry) and need to retrieve the most structurally similar candidate molecules from a database of millions of predicted or experimental spectra.

Use when you have obtained search results from one or more domain-specific MASST web applications (microbeMASST, plantMASST, tissueMASST, microbiomeMASST, foodMASST) for a query mass spectrum and need to consolidate, rank, and visualize those matches to infer the identity and biological source of.

Use when you have executed batch searches of MS/MS spectra against multiple domain-specific MASST indices and need to integrate the resulting match outputs into a single coherent view.

Use when when building or extending MS/MS library search tools that must re-rank top candidate spectra (e.g., top 2000 from MS2Deepscore) to identify the analogue or exact match.

Use when after running spectral matching (e.g., GNPS library search, SIRIUS in silico annotation) and obtaining an annotation table with confidence scores, apply threshold filtering to remove low-confidence or spurious matches before downstream analysis (e.

Use when you have .msp spectrum files with minimal metadata (e.g., only compound name and mass) and need to augment them with chemical structure descriptors, identifiers, and properties from external databases.

Use when after isotopologue and adduct grouping has been completed and you need to associate MS2 spectra with consolidated feature groups in DDA LC-MS experiments.

Use when you are implementing a file parser or data ingestion module that must read .raw (Thermo) and .mzml files from heterogeneous LC-MS instruments and pass structured data to downstream tools (e.

Use when you have mass spectrometry spectra stored across multiple, disparate metabolomics repositories (GNPS, MassBank, MetaboLights, Metabolomics Workbench, ProteoXchange, MS2LDA) and need to retrieve them using a single identifier scheme, or you are publishing spectrum figures and need.

Use when you have MS/MS spectra in MGF or similar format and a reference library of molecular structures (SMILES or SDF), and your goal is to retrieve the most likely structures for an unknown compound spectrum by learning a joint embedding space rather than using traditional spectral similarity or.

Use when when you have pre-processed genomic data (GCFs from AntiSMASH/BigScape clustering) and metabolomic data (spectra and molecular families from GNPS molecular networking) and need to systematically score and rank putative relationships between biosynthetic gene clusters and their.

Use when you have preprocessed MS/MS spectral data (converted to bag-of-fragments format with neutral losses extracted and noise filtered) and seek to identify recurring fragmentation patterns indicative of molecular substructures across a spectral cohort.

Use when you have loaded an MsmsSpectrum object from a proteomics or metabolomics dataset and need to focus the analysis window on a specific m/z range relevant to your experiment (e.g., 100–1400 m/z for typical tryptic peptides).

Use when when you have resolved mzML or mzXML spectrum files and need to isolate signals for a target m/z value (e.g., 870.954) across all retention times or a specific scan.

Use when after running MetaMiner's Dereplicator stage to identify some RiPPs via direct database matching against a constructed structure database, apply this skill to enlarge the set of identifications by propagating those matches through spectral clusters and visualizing the connected components.

Use when you have MS/MS spectra contaminated with noise ions and need to improve compound identification accuracy.

Use when you have raw MS/MS spectra in supported formats (.mgf, .mzML, or .msp) containing both chemical signal and experimental noise, prior to structural motif discovery via topic modeling.

Use when you have raw MS/MS spectra in multiple formats (.mgf, .msp, .mzML) that contain background noise, instrument artifacts, or low-abundance fragments that would degrade downstream LDA motif discovery.

Use when you have raw MS/MS spectral data and plan to calculate spectral similarity scores (whether using entropy distance, dot product, or other algorithms) for compound identification.

Use when when working with raw or partially processed tandem mass spectrometry (MS/MS) spectra that contain low-intensity background noise peaks.

Use when when you have clean, baseline MS/MS spectra and need to create test datasets with known noise characteristics to benchmark denoising algorithms, compare denoising search performance, or validate that electronic and chemical noise removal functions correctly identify and remove injected.

Use when after generating tandem mass spectrum predictions from a neural model (ICEBERG, SCARF, or baseline), and before attempting retrieval ranking, metric computation, or validation against experimental spectra.

Use when when you have aligned peak-alignment data from a preceding molecular networking task (structured as a table with peak intensity, m/z, retention time, and alignment quality metrics) and need to visualize and interactively filter peaks across multiple spectra to support comparative mass.