Claude Skills by HolobiomicsLab

Use when you suspect XCMS grouping contains misaligned features due to

Use when before peak detection on a composite or individual mass track

Use when your research proposes a new spectral embedding, matching algorithm,

Use when when you have trained a candidate model (e.g., an ensemble,

'Use when when you have implemented a novel annotation algorithm or network

Use when when you have imported a raw GCxGC-MS chromatogram as a 2D-TIC

Use when you have developed or adapted an analytical method (e.g., NPFimg

Use when you are introducing a novel spectrum prediction model and need

'Use when when you need to establish comparable performance baselines

Use when after auditing and optionally rescaling a mass track (composite

Use when when you have Thermo Fisher Scientific .raw files from an Orbitrap

Use when your LC-MS feature table exhibits intensity variations across

Use when preparing labeled LC-MS peak data for neural network training

Use when after batch correction has been applied to metabolomics data

Use when after doAnalysis() has been completed and batch correction applied

Use when after applying batch correction (e.g., ComBat, SVA) to a merged

Use when after applying pycombat-based batch correction to multi-batch

Use when before applying any batch effect correction function in dbnorm

Use when when you have deposited a collection of JSON project documents

Use when when you have processed metabolomics LC-MS/MS data organized

Use when your m/z peak data spans multiple batches (recorded in metadata

Use when you have a SummarizedExperiment object containing metabolomics

Use when when analyzing untargeted LC/HRMS data from population-scale

Use when your metabolomics matrix shows evidence of systematic variation

Use when you have a normalized count matrix (from Salmon or similar quantification

Use when you have a preprocessed and normalized lipid abundance matrix

Use when your metabolomics dataset exhibits samples analyzed across multiple

Use when your peak intensity matrix exhibits batch-to-batch variation

Use when when you have log-transformed metabolite abundance data from

Use when you have a feature table generated from LC-MS/MS non-targeted

Use when after merging feature tables from multiple LC-MS/MS analytical

'Use when when you need to generate synthetic metabolomics feature tables

Use when you have metabolomics data from multiple experimental batches

Use when after integrating feature matrices from multiple analytical

Use when after running pycombat batch correction on multi-batch metabolomics

Use when after applying CordBat batch correction to a log2-transformed

Use when you have multi-batch metabolomics data in SummarizedExperiment

Use when when you need to systematically extract a specific field or

Use when when you have multiple CDF imaging files (e.g., from mass spectrometry

Use when you have raw mzML files and a feature table (CSV) from LCMS

Use when after loading a raw metabolomics data matrix (samples × features

Use when when you have a metadata table with taxonomic annotations (species,

Use when apply batch normalization after dense hidden layers (but not

Use when when you have a trained molecular classifier (like BitterPredict)

Use when when you have raw mzML files and a feature table (CSV from mzMine

Use when when you have a repository containing hundreds or thousands

Use when you have generated a peak table or feature list output file

Use when you have a collection of N scripts (e.g., 19 gallery examples)

Use when you have acquired MS/MS spectra in .msp format (e.g., from MassBank

Use when when ingesting spectra from multiple open mass spectrometry