Claude Skills by HolobiomicsLab

Use when when you have BGC sequences (FASTA or GenBank format) or protein

Use when when you have pre-computed Word2vec spectrum embeddings and

Use when you have a feature list (m/z, retention time, intensity) from

Use when after data normalization (Step 7) on the preprocessed feature

Use when you have a machine learning training workflow (e.g., k-fold

Use when you have a filtered set of conformers (100s–1000s) from ASE-ANI

Use when when you have raw HPLC column specifications from RepoRT or

Use when you have raw or processed HRMS/MS data from Q-Exactive, Agilent

Use when you have LC- or GC-HRMS data in mzML format and a feature list

Use when you have a dashboard_data.json file (JSON export from the msFeaST

Use when after completing batch normalization and quality control filtering

Use when when you need to expose a multi-step spectral processing workflow

Use when you have deployed a microservice (e.g., TensorFlow Serving,

Use when when your application needs to enrich or predict spectral properties

Use when you need to confirm that a documented web service URL is live

Use when after deploying a web service in a Docker container with port

Use when when you have source code access to a webservice component (such

Use when you are building the initial data ingestion step of a high-throughput

Use when you have NMR peak data (1H and 13C chemical shift values) that

Use when you need to submit structured chemical compound data (identifiers

Use when you have 1H NMR spectral data from complex mixtures and need

Use when after MS-CleanR has filtered and clustered LC-MS features and

Use when when clustering large-scale mass spectrometry datasets (millions

Use when you have preprocessed mass spectra (mz/intensity pairs in MGF

Use when when you have raw strain correlation scores (or similar overlap-based

Use when when you have raw strain correlation scores computed across

Use when when you have raw strain correlation scores computed across

Use when when implementing multiple competing model architectures (e.

Use when when training a fresh NeatMS CNN model from scratch on LCMS

Use when you receive mass spectrometry data through heterogeneous identifier

Use when when you have lipid names or abbreviations sourced from multiple

Use when you have experimental MS/MS spectra and need to assign definitive

Use when after computing pairwise correlations across features (10,000+

Use when you have mzML mass spectrometry files that need both compression

Use when when implementing an igzip parser, decoder, or validator that

Use when when you have IM-MS lipidomics data acquired on samples spiked

Use when you have a two-dimensional MS map (m/z vs retention time) from

Use when when you have multi-channel LA-ICP-MS images and need to isolate

Use when when preparing ion images (single-channel 2D arrays or multi-channel

Use when you have GC–MS or LC–MS data represented as a two-dimensional

Use when when you have raw GC–MS data in two-dimensional m/z × retention

Use when you have loaded a laser ablation ICP-MS image into pewpew and

Use when you have loaded a normalized or raw pixel array (NumPy format)

Use when you have paired cdf files (raw mass spectrometry imaging data)

Use when you have raw mass spectrometry data files (mzML, NetCDF, or

Use when you have imaging mass spectrometry data from spatial metabolomics

Use when after mzML-to-imzML conversion has produced barebones imzML

Use when when you have raw Agilent MassHunter (.d) or UIMF IM-MS data

Use when you have a metabolomics dataset with left-censored missing values

Use when when you have imputed a metabolomics dataset using multiple