Claude Skills by HolobiomicsLab

Use when you have a raw feature table (TSV/CSV) derived from LC-MS peak detection (e.

Use when when you have a raw LC-MS peak table imported from vendor software (e.

Use when a Python-based metabolomics analysis package has been relocated to a new GitHub organization (e.g., metabolomics-cloud) and you need to confirm that the migration preserved package integrity, installation, and runtime correctness.

Use when when benchmarking or validating a pathway analysis method (such as PALS, ORA, or GSEA) on metabolomics data, you need quantitative evidence that the method's pathway rankings remain stable despite noise and missing peaks—conditions prevalent in real LC-MS/MS datasets.

Use when you have a metabolomics dataset and want to perform pathway enrichment analysis using ORA, but need to first understand its behavior, limitations, and correct application through reproducible simulation.

Use when when preparing to process raw LC-HRMS metabolomics data (.mzML or .abf files) with MS-DIAL within a Nextflow pipeline, before executing peak detection and chromatogram alignment.

Use when you have raw peak tables exported from a tandem mass spectrometry preprocessing tool (e.g. Progenesis, MS-DIAL, or Bruker Metaboscape) and need to integrate them with sample metadata for reproducibility filtering, mispicked-ion removal, or group-based feature exclusion.

Use when after completing outlier detection, batch correction, and quality metric calculation on a SummarizedExperiment object using mzQuality's doAnalysis function, and after manually or automatically filtering compounds and samples using the 'use' column in rowData and colData.

Use when after batch correction of metabolomics data using pooled study quality control (SQC) samples and calculation of compound/internal standard ratios, when you need to decide which compounds are reliable for reporting and which internal standard minimizes technical variation (RSDQC) for each.

Use when you have a MemoMatrix (sample-by-fingerprint matrix) from aligned MS2 spectra and need to visually compare sample similarity or clustering patterns, especially when samples show poor feature overlap, strong retention time shifts across different LC methods, or were acquired on different.

Use when when you have received Sciex Multiquant TXT export files from a completed metabolomics or lipidomics analytical run and need to verify that QC pool samples were injected at the designed regular intervals throughout the sequence(s).

Use when you need to reverse-engineer or document the architecture of a multi-component research software system where design information is embedded in repository structure, README declarations, setup files, or module docstrings rather than in a separate design document.

Use when reading mass spectral library files (particularly MoNA EI or MS2 libraries) where structural metadata like SMILES information is embedded in general-purpose fields (e.g., Comment field) rather than in the dedicated SMILES field expected by mspcompiler's downstream processing steps.

Use when you have tabular data (CSV or Excel) with column headers annotated using MESSES tagging syntax (#<table_name>.id for record identifiers, #.

Use when you have located a workflow definition file (YAML or JSON) from a versioned release and need to confirm that all mandatory workflow metadata fields (name, version, inputs, outputs, steps) are declared, properly formatted, and cross-references are resolved before validation or execution.

Use when you have completed independent batch searches across one or more domain-specific MASST tools (microbeMASST, plantMASST, tissueMASST, microbiomeMASST, foodMASST) and received multiple separate output files (_microbe.html, _plant.json, _matches.tsv, _library.tsv, _datasets.tsv, _count_domain.

Use when you have raw tabular experimental metadata (mass spectrometry or NMR sample descriptions, sample-to-treatment mappings, instrument parameters, etc.) that needs to be deposited into a structured online repository like Metabolomics Workbench, but the raw format does not conform to the.

Use when you have extracted raw tabular metadata into JSON form using the MESSES extract command and need to confirm the extraction is accurate before conversion to a repository-specific format. Specifically, use it when the conversion target format has strict schema requirements (e.

Use when when a tool like TARDIS extends its API to accept multiple input types (e.g., both file paths and MsExperiment objects), and you need to confirm that screening-mode diagnostic outputs (e.g., EIC plots, peak detection metrics) are identical regardless of which invocation pattern is used.

Use when when processing MGF-format MS2 spectral libraries (e.g., GNPS) that contain SMILES but lack the Molecular Formula field, and you need to prepare the library for MS-DIAL import or polarity-based separation workflows.

Use when when you have paired microbiome-metabolome datasets where only a subset of metabolites carry curated biochemical annotations (e.

Use when when you have trained multi-layer perceptron neural network models on paired microbiome-metabolome data (from ≥10-fold cross-validation iterations) and need to identify functional modules—groups of microbes and metabolites with co-varying or synergistic relationships—for systems-level.

Use when you have draft metabolic reconstructions (in SBML or standard format) for multiple organisms sampled from the same microbial community and need to produce a single consensus model per organism that reflects only metabolic capabilities agreed upon across the input reconstructions, or when.

Use when you have a bacterium-phage infection study with normalized peak intensities from FT-ICR MS across multiple phage treatment groups (minimum 2–3 conditions such as HP1, HS2, control) and sample replicates (n ≥ 6–8 per group), and you need to test whether phage-type factor explains.

Use when when you have raw count matrices from paired microbiome (16S rRNA or metagenomic) and metabolomic (LC-MS/MS) profiling data that will be used to train or apply a predictive model (e.g., MiMeNet, MelonnPan, Random Forest) to predict metabolite abundances from microbial composition.

Use when you have paired microbiome and metabolomic abundance tables (samples × features) with relative abundance or raw count values, and you are preparing data for downstream regression or neural network modeling of microbe-metabolite relationships.

Use when you have paired microbiome (16S rRNA, metagenomic) and metabolomic (LC-MS, GC-MS) abundance tables from the same biosamples, and you want to predict which metabolites are recoverable from microbial composition alone and identify groups of microbes and metabolites with correlated.

Use when immediately after importing raw LC-MS peak tables (e.g., Progenesis format) and before applying group or replicability filters.

Use when when comparing the robustness of multiple pathway ranking methods (e.g., PLAGE, ORA, GSEA) on metabolomics or other omics data, and you need to establish which method is least sensitive to peak dropout, instrumental noise, or annotation uncertainty.

Use when after converting MS/MS spectra to fixed-length vector representations using a pre-trained Word2Vec model (as in Spec2Vec), filter spectra before computing similarity scores to flag those where a large fraction of the observed intensity comes from peaks or neutral losses not present in the.

Use when after sample alignment and feature grouping in untargeted LC-MS workflows, when the aligned feature table contains missing intensity values (NA or zero entries) due to features falling below the detection limit in some samples but being present above-threshold in others.

Use when after feature extraction and quality control filtering (blank masking, sample dropping, normalization) have been applied, but before statistical analysis or machine learning.

Use when you are implementing a custom MsBackend subclass for the Spectra package and need to ensure that spectraData() returns all core spectra variables (e.g., centroided, polarity, collisionEnergy) regardless of which ones are explicitly stored in your backend.

Use when after loading a feature table into memory when the table contains zero or missing values that represent true signal loss (not genuine absence), and you need to impute them before normalization, batch correction, or statistical analysis.

Use when when you have a trained multitask model that accepts multiple input modalities (e.g., 1D NMR spectra in different nuclei or complementary analytical techniques) and you need to understand their relative importance for the downstream prediction task (e.g., molecular structure elucidation).

Use when you need to measure how much a specific model capability or architectural feature contributes to prediction performance, especially when that capability is non-obvious or orthogonal to baseline methods.

Use when you have a pre-trained Keras model and need to deploy it via a Docker-based TensorFlow Serving API (e.g., for molecular classification via SMILES), but the model's layer naming or format does not yet match the target runtime's expectations (e.

Use when you have a pre-trained GNN model for CCS prediction and need to verify that it generalizes to test data that was held out during training. Use it specifically when comparing model performance across different molecular datasets (e.

Use when a deep learning model for molecular structure prediction (e.g., NMR2Struct) has been trained and evaluated on a limited molecular size range (e.

Use when you have a trained baseline GNN model with established hyperparameters (dropout rate, learning rate, epochs, optimizer settings) and want to evaluate whether alternative message-passing GNN architectures (Graph Attention Networks, Message-Passing Neural Networks) achieve comparable or.

Use when when you need to programmatically interface with a TensorFlow Serving model instance and must discover or validate the expected input names (e.

Use when before submitting peak data or other inputs to a machine learning classification API for the first time, after a model update, or if you encounter unexpected prediction errors. It is essential when the underlying model's input names or structure may change and require code updates.

Use when after deploying a TensorFlow Serving container (especially within a Dockerized stack like NP-Classifier), before running classification or inference pipelines, to confirm that input layers are named 'input_2048' and 'input_4096' and output layer is named 'output'.

Use when you have a ProForma 2.0 peptidoform string (e.g., DLTDYLM[Oxidation]K) and need to extract the underlying peptide sequence and map modification positions to enable fragment ion annotation, mass calculation, or spectral matching.

Use when evaluating whether a mass spectrometry data analysis platform (such as mzmine) provides complete module coverage across all advertised separation and ionisation techniques.

Use when when you need to understand how a multi-instrument mass spectrometry platform (like mzmine) decides which processing module receives a given dataset based on its declared data type (LC vs. GC vs. IMS vs. MS imaging).

Use when releasing a new version of a Python package, validating packaging infrastructure changes, or confirming that distribution channels (PyPI, Bioconda) remain functional after upstream updates. Use it as a gate before finalizing a release to catch installation or import breakage early.

Use when after installing a Python package (especially one with optional dependencies) to confirm that: (1) core modules are accessible and importable;

Use when you have a curated dataset of small molecules with SMILES, optional 3D coordinates, adduct information, and experimentally measured CCS values (in Ångströms or similar units), and you want to train a GNN model to predict CCS on held-out test molecules.

Use when you have raw molecular structures in SMILES or SDF format and need to prepare molecular descriptors as input to a descriptor-based classifier (e.g., BitterPredict.m).