Claude Skills by HolobiomicsLab

Use when after acquiring a PRM experiment on a Thermo Fisher Orbitrap instrument when you need to verify that the mass spectrometer's data acquisition controller executed the scheduled method with correct temporal spacing.

Use when after instantiating a specXplore dashboard session layer with a loaded session data object from disk, before conducting visual exploration of LC-MS/MS spectral data.

Use when your mass spectrometry DataFrame contains m/z, retention time (or mobility), and intensity columns, and you need to generate an interactive HTML figure for exploration, web-based presentation, or interactive supplementary material.

Use when after structural clustering (isotopologue grouping, adduct detection, cross-assay linking) and correlation clustering of LC-MS features, when you need to inspect and communicate the topology of structural relationships—particularly when the number of features or link types is too dense for.

Use when after building a SummarizedExperiment object containing LC-MS peak areas and internal standard assignments, when you need to identify study samples with anomalous Internal Standard signal (indicating syringe failure, capillary clogging, or injection system malfunction) before applying.

Use when after batch correction of metabolomics QC samples using pooled study quality control (SQC) samples, when you have multiple candidate internal standards and need to determine which one stabilizes the compound/internal standard ratio for each compound.

Use when you have (LC-)IM-MS lipidomics data from samples spiked with U13C-labeled yeast extract, measured CCS values for both labeled and unlabeled lipids, and need to quantify systematic CCS bias between your instrument and a reference library before applying bias correction.

Use when your IM-MS lipidomics samples have been spiked with fully labeled isotopic internal standards (e.

Use when when a new file format specification exists as a living document (e.

Use when when you have ensemble predictions (e.g., from Monte-Carlo Dropout inference with N ≥ 10 forward passes per input) and need to distinguish high-confidence from uncertain predictions before downstream analysis.

Use when when you have paired MS2 spectra and BGCs with structural candidates (e.g., from MIBiG homology), and you want to rank which BGC likely produces which spectrum using a compound-class-agnostic method.

Use when after applying one or more mpactr filters (mispicked, group, cv, insource) to an mpactr object and generating a qc_summary() data.

Use when when preparing ion image data from mass spectrometry imaging for contrastive self-supervised representation learning, and you need to generate augmented image pairs that reflect either co-localization relationships between different molecular ions (COL mode) or isotopic relationships.

Use when you have 512-dimensional representation vectors output from ResNet18 encoders processing paired augmented ion images, and you need to prevent trivial solutions (representation collapse) during contrastive learning—specifically when optimizing for maximized similarity between augmentations.

Use when you have a feature table containing raw ion mobility arrival time measurements paired with experimentally assigned biomolecular class labels (e.g., lipid, protein, carbohydrate), and you need to compute CCS values for downstream structural or comparative analysis.

Use when you have raw or processed TWIM-MS data (arrival time and m/z pairs) from multiple lipid, protein, or metabolite classes and need to classify features by biomolecular type before—or instead of—performing feature identification.

Use when you have (LC-)IM-MS lipidomics data from samples spiked with U13C labeled internal standards (e.g., fully labeled yeast extract) and need to quantify and correct systematic CCS bias before downstream lipid identification or quantification.

Use when when you have mass spectrometry data with ion mobility (drift time or 1/K₀) measurements as a continuous dimension and want to visualize intensity distributions across the ion mobility axis.

Use when you have raw arrival-time data from TWIM-MS and need to convert it to collision cross section (CCS) values for multi-omic analysis.

Use when you have tunemix reference data acquired in both positive and negative ion modes and need to establish independent CCS calibration curves for each mode. The skill is required when downstream CCS assignments must achieve R² ≥ 0.

Use when after calling MsmsSpectrum.annotate_proforma() to assign fragment ions to a mass spectrum, verify that each annotated peak has the correct ion_type ('b' or 'y'), charge state, and m/z deviation from the theoretical mass computed for that peptidoform.

Use when when evaluating whether a mass spectrometry analysis platform (such as mzmine) has comprehensive module support across multiple ionisation and separation techniques (LC, GC, IMS, MALDI MS imaging), or when planning a multi-technique MS study and needing to confirm that all intended.

Use when when converting MS/MS spectra from .msp format library files (e.g., MassBank) into a custom fragment library for metabolite annotation, and the source spectra are tagged with ionization mode information (positive or negative).

Use when when beginning preprocessing of a new LC-MS dataset with mzML files or raw acquisitions and you need to determine which ionization mode was used before running feature detection (Asari) or empirical compound grouping (khipu).

Use when when you have loaded a raw mass spectrum (e.g. ESI_NEG_SRFA.

Use when when you need to assess whether retention times measured on a given LC-MS run follow the expected linear relationship defined by iRT peptide standards (e.g., Pierce or Biognosys iRT peptides).

Use when after completing peak picking, sample alignment, and before final MS2 spectrum extraction, when you have identified individual ion peaks across samples and need to link isotopic variants (e.

Use when after feature detection has produced a TSV feature table (from Asari or equivalent) containing m/z, retention time, and intensity columns, and before MS1 or MS2 annotation.

Use when you have a peak table from tandem MS preprocessing (e.g., MS-DIAL, Metaboscape) and suspect that isotopic patterns have been incorrectly split during feature detection.

Use when after isotope detection has enumerated C13 isotopologue patterns across m/z, drift time, and retention time dimensions, and you need to reduce false positives by retaining only well-populated isotopic signature clusters before annotation or export.

Use when after sample alignment and peak picking have produced an aligned feature table with m/z and retention time coordinates.

Use when after structural cluster assignment and correlation clustering are complete, and you need to represent the full set of structural relationships (isotopologues, adducts, cross-assay links, and correlation co-membership) in a single unified graph for interactive visualization, network.

Use when you have a peak-picked feature table (HDF5 format with m/z, drift_time, retention_time, intensity columns) and need to identify and label isotopic signatures within singly charged species.

Use when you have preprocessed, statistically significant LC-MS features (from multiple assays or a single assay) and need to group features that represent the same metabolite in different isotopic labeling states.

Use when when you need to confirm that a Java project's GitHub Actions workflow (e.g., 'dev_build_release.yml') has completed successfully and generated usable build artifacts;

Use when when you need to verify that a Java project's automated build pipeline (GitHub Actions workflow) executes without errors and generates distributable artifacts (e.g., .deb installers, portable binaries, or .jar files).

Use when when you need to understand how a Java application routes input data to processing modules based on declared data types, conditionally branches on instrument or format types (e.

Use when you have discovered Mass2Motifs via LDA and need to (1) load a pre-computed motifset JSON file (e.g., motifset_optimized.

Use when when you have extracted intermediate JSON conforming to the Experiment Description Specification and need to restructure, filter, sort, or aggregate records (e.

Use when when your project JSON document contains public identifiers (genome IDs, biosample accessions, etc.) that lack human-readable or linked metadata, and you need to populate those fields programmatically before storage or publication to enable full-text search, validation, or cross-linking.

Use when you have completed the MS2LDA LDA modeling phase and possess motifset.json or motifset_optimized.json files containing inferred Mass2Motifs.

Use when when you have NMR peak assignments (1H and 13C chemical shift values) and need to submit them to the /api/smart3/search endpoint for automated structure classification. Use this skill before making API calls to ensure peak data conforms to the expected JSON schema.

Use when you receive a JSON response from a REST API endpoint (e.g., TensorFlow Serving /model/metadata) and need to extract and validate specific fields such as model input names, signatures, or outputs.

Use when after sending HTTP requests to API endpoints (such as /classify or /model/metadata on an NP-Classifier server) to verify that the response is parseable JSON and contains the expected output fields and metadata before attempting to extract or process the data programmatically.

Use when after extracting tabular data into intermediate JSON form or after applying matrix conversion directives (e.

Use when after extracting tabular data into intermediate JSON representation (via tagging), or after manual JSON editing, and before converting to a target format (e.g., mwTab for Metabolomics Workbench).

Use when you have inferred or discovered structured results (e.g., LDA-derived motif sets with mass compositions, neutral-loss patterns, and ranked database matches) that must be shared between tools, stored durably, or consumed by visualization or annotation pipelines.

Use when after serializing empirical compound collections to JSON format via khipu's build_empCpds command, or before ingesting empCpd.

Use when you need to persist and communicate the health status of multiple external web services queried during an annotation run.

Use when after enriching a project JSON document with external metadata (e.g., organism names, genome identifiers) or before writing enriched JSON to disk.