Claude Skills by HolobiomicsLab

Use when after applying deimos.calibration.tunemix() to positive-mode or negative-mode tune mix data with known CCS reference compounds (m/z range typically 118–1522), verify that the resulting calibration model achieves the expected r-squared coefficient.

Use when when you have N-Me derivatized unsaturated sterol lipid structures (as SMILES or molecular formula) and need to predict MS/MS fragmentation patterns with collision-energy-dependent m/z values and intensities for downstream CCS prediction or LC-IM-MS/MS library matching.

Use when when rendering a treemap of qc_summary() output showing ion counts and percentages by filter status (passed/failed), and you need a perceptually uniform, colorblind-friendly palette that clearly distinguishes filter categories while using sequential intensity to reinforce the magnitude of.

Use when after constructing a metabCombiner object by grouping features from two metabData objects by m/z, and before proceeding to anchor selection, RT mapping, or alignment scoring.

Use when you have a batch of raw LC-IMS-MS/MS data in mzML or mzML.

Use when you have tandem MS/MS spectrum data in standard peak file formats (mzML, mzXML, or MGF) and need to cluster spectra based on precursor mass and fragment ion similarity.

Use when you have a labeled peak quality dataset (development set with ground-truth pass/fail labels), a defined set of peak-quality metrics (e.

Use when you have developed a new machine learning model for predicting metabolomic profiles from microbiome data and need to quantify its performance improvement over existing methods.

Use when when you have feature lists (in CSV format) from two or more different MS acquisition methods (e.g., LC-MS vs. LC-IMS-MS), different processing software (e.

Use when when you have developed a new spectral similarity scoring method and need to quantify its retrieval performance relative to established methods (e.g., modified cosine, Spec2Vec). Use this skill if you have: (1) a test set of spectra with ground-truth structural similarity labels (e.

Use when after generateComponents has assigned candidate TP features to parent features and computed similarity metrics (spectrum similarity, fragment matches, neutral loss matches, retention time differences), use this skill to narrow the candidate pool to high-confidence parent–TP pairs that.

Use when after mass tracks have been aligned across all samples into a MassGrid (via sample-wise or centroid-based alignment), you have a unified set of m/z features tracked across the entire study.

Use when when processing a multi-sample LC-MS metabolomics project after mass-track extraction and retention-time calibration have been applied to all individual samples, and you need to detect peaks across the entire cohort.

Use when your untargeted LC/HRMS dataset contains Data-Independent Acquisition data (MS^E, AIF, or SWATH-MS) or MS1-only composite spectra where multiple precursor ions fragment simultaneously, and you need to deconvolve overlapping fragmentation spectra to enable accurate chemical structure.

Use when you have DDA raw mass spectrometry data (mzML, mzXML, or netCDF format) and need to reconstruct composite fragmentation spectra by associating fragment ions with their parent precursor ions.

Use when you have MS/MS spectra matched to a reference library via both identity search (exact or high-similarity matches) and fuzzy/analog search (structurally related compounds with similar fragmentation), and you need to prioritize which annotations to trust for downstream reporting, validation.

Use when after compound database dereplication has produced candidate annotations (from SIRIUS or MetFrag) and you need to select the most reliable candidates for final annotation.

Use when after submitting a fingerprint or spectrum query to the CANOPUS web service and receiving a structured response.

Use when after modifying a FIDDLE configuration file to add or remove instrument types from the allowlist (e.g., adding 'ftms' to gnps_orbitrap), run the full preprocessing pipeline and validate that the resulting training and test set sizes match documented targets.

Use when after feature extraction from LC–MS raw data (via XCMS, MS-Dial, or equivalent) has yielded a feature intensity table (samples × features) and feature metadata table (m/z, retention time, feature ID).

Use when you have MS2 .mzML spectral data from untargeted metabolomics and need to assign chemical identities to detected precursor ions.

Use when you have raw GC-MS or LC-MS data in vendor format (NetCDF, .raw, .d) or generic mass lists, and you need to assign chemical identities to detected peaks.

Use when you have LC-MS/MS data in mgf format and a custom spectral database prepared with CFM-id (or an in-built database), and you need to identify unknown compounds by comparing their experimental fragmentation patterns against predicted or reference spectra with quantified match scores.

Use when you have preprocessed MS/MS spectra (noise-filtered, normalized) and need to compute pairwise similarity or distance scores for compound library matching, when your goal is to rank candidate compounds by spectral match quality and maximize correct identification rate above dot-product.

Use when when you have an unknown MS/MS spectrum (in .

Use when you have an experimental MS/MS spectrum (m/z and intensity pairs in mzML/mzXML format from DDA or targeted acquisition on Thermo, Waters, or Bruker instruments) and need to identify the unknown compound by comparing it against a reference database.

Use when when integrating MS/MS spectra from multiple source repositories or instruments into a unified library, and the compound identifiers, adduct annotations, collision energies, and instrument types differ in format, terminology, or completeness across sources.

Use when after filtering a peak table to remove mispicked ions, group contaminants, and low-replicability features, you have a curated feature list with m/z, retention time, and MS/MS spectra ready for annotation.

Use when immediately after loading metabolomics measurements into a SummarizedExperiment object, before any batch correction or quality filtering.

Use when after XCMS feature detection, retention time correction, regrouping, and missing value imputation have produced an aligned feature table with multiple signals per compound. Use it when your data contains isotopic peaks (e.g., ¹³C variants) and multiple ionization products (e.

Use when after marker identification or feature selection has produced a list of discriminatory m/z features, and before pathway enrichment analysis (e.g., KEGG).

Use when when you have generated spectral embeddings for a query set and a reference spectral library, computed pairwise cosine similarity scores between them, and need to quantify retrieval performance across ranked candidates (top-1, top-5, top-10).

Use when you have completed XCMS grouping on LC-MS data and suspect misaligned features due to long acquisition periods (>1 week) or large sample cohorts (hundreds of samples).

Use when xCMS alignment produces suspected misaligned feature groups across hundreds of samples or long acquisition runs (>1 week), particularly when global XCMS warping functions fail to account for compound-specific or sample-neighborhood retention-time drift structures.

Use when you have raw peak area or intensity measurements for both compounds and their corresponding internal standards across all study samples (including QC and calibration samples), and you need to normalize for instrument variability and injection efficiency before batch correction or quality.

Use when you have a collection of preprocessed MS/MS spectra with structural annotations (InChIKey, SMILES, or InChI) and need to identify pairs of compounds that are structurally related above a specified similarity threshold.

Use when you have two MS/MS spectra from related compounds (e.g., a reference compound and a suspected modified version) and need to quantify where and how their structures differ.

Use when when you have SMILES strings or molecular structures for compounds and need to predict their retention behavior in reversed-phase liquid chromatography (RPLC) systems at pH ~2.7, but lack pre-computed molecular fingerprints or descriptor-based feature representations.

Use when when you have experimental MS/MS data (peak lists, precursor m/z, charge state, adduct type) paired with a chemical structure (SMILES or structural identifier), and need to create a unified Compound object for spectral alignment, modification-site prediction, or comparative fragmentation.

Use when when processing aligned LC-MS data across multiple samples where the computational bottleneck is repeated peak-detection algorithm calls (one per sample per m/z value). Typical scenario: >10 samples with >1000 m/z values each, where N individual find_peaks invocations dominate runtime.

Use when at the start of any DaDIA pipeline execution, or whenever you are preparing to run a complex multi-package R workflow on a new system or after updating package managers.

Use when when evaluating a new or updated version of a data processing tool (especially asari or similar LC-MS workflows) before production deployment, or when verifying claims about scalability, memory efficiency, or throughput on specific hardware classes (e.g., ≤16 GB RAM single-core systems).

Use when after peak detection and feature alignment in a metabolomic LC–MS/MS or GC–MS workflow, when you have a feature table (rows=metabolic features, columns=samples) split into separate .csv files for peak height and peak area.

Use when you have (1) a set of calibration samples with known spiked concentrations of target compounds, (2) measured compound/internal-standard ratios for both calibration and study samples, and (3) need to convert ratios to absolute concentrations for reporting.

Use when a mass spectrometry analysis pipeline must accept data from multiple sources with different identifier schemes (GNPS Task ID, Universal Spectrum Identifiers, or Feature-Based Molecular Networking task IDs), and you need to transparently route each to the correct loader without requiring.

Use when you have received a peak/feature table from an unknown or variable upstream peak-picking tool and need to ingest it into LipidMatch or a similar unified workflow. The input file format, column naming, or metadata structure is tool-specific (e.

Use when you have a user-submitted spectrum with domain-context metadata (e.

Use when you need to understand how a data-processing software system discriminates among multiple input types (LC, GC, IMS, MALDI) and selectively instantiates processing pipelines.

Use when you have received a USI string (formatted as mzspec:<namespace>:<resource>:<identifier_type>:<identifier>) and need to retrieve the corresponding mass spectrum data.

Use when when loading search results from Data-Independent Acquisition (DIA) mass spectrometry workflows (e.g., output from OpenSwath or similar feature detection tools) and you need to restrict the analyte pool to those meeting a strict false-discovery rate (FDR) threshold.