Claude Skills by HolobiomicsLab

Use when you have raw LC-MS/MS spectra from vendor instruments (mzML, mzXML, MGF, or MSP format) with variable peak quality and intensity distributions, and you plan to perform library matching, molecular networking, or spectral similarity comparison.

Use when you have raw MS/MS spectra (in formats like mzML, json, mgf, msp, mzxml) that contain background noise or numerous low-intensity peaks before running MS2Query library matching.

Use when when you have tandem mass spectra (mz/intensity pairs with precursor m/z) and need to train interpretable machine learning models—particularly decision trees or regression models—where each feature must correspond to a concrete chemical entity (peak or neutral loss) rather than a latent.

Use when you have raw metabolomic LC-MS data processed through XCMS CentWave feature extraction and want to improve true positive feature recovery while controlling false positive rate and crash likelihood.

Use when you have a filtered MS-DIAL peak list (post-generic filtering) containing m/z, retention time, and peak intensity metrics, and you need to group LC-MS features into clusters to reduce redundancy before parental signal extraction.

Use when after executing feature detection and quantification on raw LC-MS data (mzML or NetCDF format) using an automated pipeline such as MetaboAnalystR 4.0, and before proceeding to downstream normalization, scaling, or functional analysis.

Use when when training Word2Vec embeddings on mass spectra represented as peak-word documents, and you need to preserve the quantitative intensity relationships between fragments without allowing a single dominant peak to overwhelm the learned word associations.

Use when you have raw MS/MS spectral peak lists with absolute intensity values and need to compare spectra using entropy similarity, dot product, or other distance metrics.

Use when when you have imported raw mass spectrometry spectral data (in formats like mzML, mzXML, msp, MGF, or JSON) and need to clean peak lists before metadata validation, similarity scoring, or library comparison.

Use when you have loaded raw mass spectrometry spectral data (in MGF, MSP, mzML, or mzXML format) and need to decide which intensity threshold(s) to use for filtering out noise and low-abundance peaks.

Use when you have acquired MS/MS spectra containing suspect noise ions—either electronic noise (ions with identical intensities occurring >4 times in a single peak list, a signature of detector artifacts) or chemical noise (fragment ions chemically implausible given the precursor molecule's.

Use when after successfully resolving a USI string to a specific mass spectrum scan, and before performing spectral matching, library search, or comparative analysis.

Use when when importing raw mass spectrometry data from vendor or open formats (mzML, mzXML, msp, MGF, JSON, metabolomics-USI) and you need to extract peak m/z and intensity pairs into a standardized representation.

Use when you need to validate batch correction or normalization algorithms, require ground-truth condition/batch effect annotations for method benchmarking, or want to systematically evaluate how different batch types (linear, random) and condition allocations affect feature recovery in metabolomic.

Use when when you have LC-MS-MS metabolomics data in MGF format and need to prepare it for unsupervised analysis (e.g., topic modeling with LDA).

Use when after peak detection when you have a table of detected peaks with m/z values and need to improve mass accuracy for downstream annotation.

Use when when you have loaded mass spectrometry data (from mzML or Bruker .

Use when when you have raw MS2 spectra (m/z and intensity pairs) and a curated reference peak list from a large training dataset (e.

Use when when you have raw mzML files and a corresponding feature table (CSV format, e.g., from mzmine) and need to generate peak matrices with fixed dimensions (e.g., 2 × 120) that encode margin vs. peak signal regions for training a neural network classifier to filter false positive LCMS peaks.

Use when you have aligned MS2 spectra from multiple samples and need to create a matrix representation where rows are samples and columns are MS2 peaks or neutral losses (mass differences to the precursor), with counts of occurrences.

Use when you have claims in a paper or tool documentation that one peak picking method outperforms others (e.g., 'IDSL.IPA outperforms MZmine 2 and xcms'), but the specific comparison metrics, numerical results, and source tables are not provided in the abstract or introduction.

Use when at the entry point of SLAW processing when you have centroided mzML or netCDF LC-MS files and need to decide which peak-picking algorithm to use. Trigger this skill when: (1) raw LC-MS data must be converted into a feature matrix;

Use when analyzing raw 2D MS data (m/z vs. retention time maps) where conventional peak picking introduces unacceptable error rates, particularly in untargeted metabolomics or chemometrics studies requiring sensitive marker identification at trace levels (e.g., parts per billion).

Use when when reconstructing a metabolite fragment library entry from raw MS/MS spectral data (e.g., from MassBank or local acquisition), you need to define peak-picking thresholds to separate true fragment ions from baseline noise and assign occurrence scores.

Use when when you have completed non-targeted peak picking and alignment with tools such as MZmine 2, XCMS, or MS-DIAL on LC-HRMS mzML data and need to quantify performance metrics (found peaks %, degenerated isotopologue ratio, alignment errors) by comparing against a curated benchmark of known.

Use when when processing LC-MS mass tracks (EICs) and you need to identify genuine chromatographic peaks rather than noise artifacts.

Use when when you have identified the set of analytes (peptides, nucleosides, or other biomolecules) you wish to simulate in silico, and you need to prepare their chemical properties (m/z, intensity, retention time) in a machine-readable format before applying fragmentation and noise injection.

Use when you have a CSV file containing nucleoside or peptide molecular data (formulas, identifiers, retention times, intensities) that you want to simulate as LC-MS/MS runs. Use this skill as the mandatory first step before selecting a fragmentation model and noise injector in SMITER.

Use when after converting a CSV file of molecule definitions into a peak properties dictionary using csv_to_peak_properties, or after loading a serialized peak properties dictionary from pickle/JSON storage, before passing it to SMITER simulation functions (e.g., smiter.synthetic_mzml.write_mzml).

Use when after elution peaks have been detected on composite mass tracks using local maxima and prominence thresholds, and before mapping detected features back to individual samples or performing pre-annotation.

Use when after calculating 12 peak-quality metrics on a development set of extracted ion chromatograms (EICs) and labeled peaks, when you need to select both the classification algorithm and its optimal hyperparameters before training a final model on held-out test data.

Use when when you have manually labeled LC-MS peaks as 'High quality' or 'Low quality' using NeatMS's annotation tool and need to create training/validation/test batches.

Use when you have completed XCMS preprocessing (getEIC() and fillPeaks()) on untargeted LC-MS metabolomics data and need to assign per-peak quality scores prior to manual curation, classifier training, or downstream statistical analysis.

Use when when you have loaded aligned peak-alignment data from a molecular networking task and need to distinguish high-confidence, reproducible peak alignments from noise or spurious matches.

Use when after applying cluster-based filtering with quasi-molecular adducts and frequency thresholds on candidate metabolites from KEGG matching.

Use when after applying cluster-based filtering with quasi-molecular adduct constraints and frequency thresholds on LC-MS feature candidates, when some peaks have been entirely removed from the candidate pool and you want to prevent loss of true metabolites that failed to meet filtering criteria.

Use when when identifying landmark peaks for retention time alignment in multi-sample LC-MS metabolomics workflows.

Use when after peak detection in a nontargeted LC-MS workflow when you have a feature table with detected peaks and need to filter low-quality features or understand why certain features have inconsistent intensity or poor annotation confidence.

Use when your peak table contains ions with similar retention time and mass-to-charge ratios that likely represent isotopic patterns incorrectly split during preprocessing or detector artifacts.

Use when you have a table of detected chromatographic peaks (e.g., from CentWave peak detection in xcms) and need to isolate a single target m/z (e.g., m/z 304.1131 for a pesticide) or a narrow m/z range, or when you must restrict analysis to a known retention time window (e.

Use when after generating a peak table from XCMS peakTable() output in an untargeted LC-MS metabolomics workflow, if your experimental design includes quality control (QC) samples (SampleType='LQC') and you want to exclude noisy or unstable EICs before building a peak quality classifier.

Use when when receiving a peak or feature table output from an unknown or variable upstream peak-picking tool, and you need to route it to the correct ingestion adapter (e.g., in LipidMatch) to normalize and validate it before lipid identification.

Use when when you have extracted peak tables from multiple independent LC/HRMS analysis batches (each with retention time and m/z values) and need to align peaks across batches to create a unified feature matrix for downstream statistical analysis.

Use when you need to validate the reference-semantics behavior of mpactr filter functions, particularly when using copy_object=FALSE. Use it to confirm that a filtering operation (e.

Use when when using mpactr filter functions (e.g., filter_mispicked_ions, filter_group, filter_cv) with R6 reference semantics and uncertain whether the copy_object parameter controls deep copying or in-place modification.

Use when when you have a fragment peak list (m/z values and intensities) from MS/MS data and need to assign candidate chemical subformulae to each peak as part of a de novo chemical formula inference pipeline.

Use when you have a raw peak-intensity matrix from untargeted LC-MS data (organized as rows=peaks, columns=samples) and need to generate initial candidate metabolite assignments.

Use when you have raw or semi-processed m/z peak detection output from LC-MS/MS instruments (typically from XCMS, MSnbase, or other peak-picking tools) in one of several known formats (MetaboAnalyst-like, MetaboShiny native, or Metabolights) and need to ingest them into MetaboShiny for compound.

Use when when you have mass spectrometry data organized in a Pandas DataFrame with m/z values, retention time (RT), and intensity measurements, and you want to visualize the joint distribution and correlation of these three dimensions to identify peaks, assess separation, and detect patterns across.

Use when when performing reverse spectral search on MS/MS data suspected to contain chimeric spectra (multiple co-isolated precursors), and you need to increase the number of reliable spectral matches while filtering out false positives.