Claude Skills by HolobiomicsLab

Use when you have a feature list with assigned molecular formulas and m/z values from non-target HRMS analysis, and you need to identify and rank potential PFAS compounds among thousands of detected features.

Use when you have a trained decision tree model on ChemEcho sparse feature vectors and need to convert a specific decision path (root to leaf) into a deployable query.

Use when after peak detection and feature extraction have produced a composite feature table with m/z, retention time, and intensity values for individual samples.

Use when you have a feature list (m/z values, retention times, and optionally molecular formulas or neutral masses) from LC- or GC-HRMS data and need to rapidly identify features matching characteristic chemical signatures (e.

Use when you have a feature-based molecular network generated from non-targeted LC-MS/MS metabolomics data (e.

Use when after matching mass-to-charge ratios to a KEGG database and obtaining multiple candidate metabolites per feature, but before filtering quasi-molecular adducts.

Use when you have two or more CSV feature tables from separate metabolomic experiments (each containing mass, retention time, intensity, isotope, and adduct columns), and you need to align and merge them into a single feature-by-sample matrix where features from different experiments are matched if.

Use when after imputing missing values and before assigning Cluster_IDs in the notame preprocessing pipeline.

Use when you have vendor-independent centroided DDA mzML files from LC- or GC-HRMS and need to delineate chromatographic peaks across the mass-to-charge and retention-time dimensions before applying mass defect analysis, diagnostic fragment matching, or other prioritization rules.

Use when after constructing a MetaboSet object with LC-MS peak abundances, sample metadata (pData with QC labels), and feature metadata (fData), and after marking missing values as NA.

Use when when you have raw untargeted LC/MS data in mzML or mzXML format and need to detect and quantify metabolite signals across mass-to-charge and retention time dimensions without prior knowledge of instrument parameters, batch effects, or optimal detection thresholds.

Use when you have a feature table (CSV with m/z and retention time columns) and corresponding .mzXML or .mzML mass spectrometry files from an LC-MS metabolomics experiment, and you need to filter out false or low-quality chromatographic peaks before downstream analysis.

Use when after drift correction in non-targeted LC-MS metabolomics workflows, when you need to decide which molecular features are sufficiently reproducible (low instrument/QC variance) and biologically informative (high QC-versus-biological signal ratio) to retain for downstream statistical.

Use when after sample alignment has established consensus m/z and retention time coordinates, and after grouping of isotopologues and adducts is complete.

Use when you have a feature table from LC-MS analysis (containing m/z, retention time, and intensity values) and need to identify which detected features represent the same molecular species ionized under different adduction states.

Use when you have a detected LC-MS feature table (with m/z, retention time, and intensity columns) and need to identify which features are derivatives of the same parent molecule rather than distinct metabolites.

Use when you have a feature table from nontargeted LC-MS peak detection (containing m/z, retention time, and intensity values) and need to disambiguate whether detected features represent the same molecular entity under different ionization/modification states or are true independent signals.

Use when after initial retention-time-based feature grouping (e.g., ±20 s window) when you need to separate co-eluting features that are chemically distinct. Triggers include: (1) large feature groups (>2–3 members) suspected to contain multiple compounds;

Use when after sample alignment and isotopologue/adduct grouping are complete, when you need to associate MS2 spectral data (DDA-acquired) with the consolidated feature groups to enable MS/MS-based compound annotation or to bundle MS1 quantification with MS2 evidence.

Use when you have a feature list with m/z values from HRMS data and need to identify homologous PFAS series to prioritize suspect screening.

Use when after peak picking and sample alignment have produced an aligned feature table with m/z and retention time coordinates.

Use when after LCMS feature alignment (e.g., Eclipse output) when you have a feature table with retention times and intensity profiles across multiple injections, and you need to collapse redundant features (e.

Use when you have high-resolution tandem mass spectra (mzML, mzXML, or MGF format) and need to cluster or index millions of spectra efficiently without exhaustive pairwise comparison.

Use when when you have high-resolution tandem MS/MS spectra in mzML, mzXML, or MGF format and need to cluster or search millions of spectra efficiently.

Use when after constructing MetaboSet objects from Excel-formatted LC-MS peak tables and before drift correction or quality flagging.

Use when after blank masking and sample dropping, when you have a feature table with intensity values that exhibit systematic variation across sample collection batches or instrument runs.

Use when after features have been identified in LC-MS data via peak picking, MS2 recognition, or targeted-list matching, and you need to measure their signal magnitude (peak height or area) across samples for quantitative comparison, normalization, or statistical testing in metabolomics studies.

Use when after generating a feature table from LC-MS/MS data when your experiment includes blank control samples and you need to remove features driven by background ions or instrumental contamination.

Use when after sample alignment in untargeted LC-MS workflows, when the aligned feature table contains missing (NA or zero) intensity entries for features that are detected in some samples but fall below the instrument detection limit or are absent in others.

Use when a user supplies a custom feature list from external feature-finding software (vendor tools, alternative open-source pipelines) instead of using pyOpenMS automatic detection, or wishes to augment/replace pyOpenMS results with pre-processed features.

Use when you have feature lists in CSV format originating from different acquisition methods (e.

Use when you have a heterogeneous feature matrix combining molecular descriptors (from RDKit/mordred) and chromatographic metadata (column length, temperature, pH, flow rate, particle size) with different physical units, ranges, and scales.

Use when you have preprocessed metabolomics feature abundance data and want to train multiple classifiers (traditional ML or deep learning) to predict sample phenotype or disease status.

Use when you have processed mass spectrometry data consisting of three separate tables (quantification features, sample metadata, and spectral annotations) and need to combine them into a single, queryable artifact that preserves relationships between features, samples, and their chemical.

Use when after completing sample alignment in JPA (Part 5) or when ingesting a peaklist or aligned feature matrix from prior peak-picking runs, parse feature metadata to enable EIC export or multi-sample feature annotation.

Use when after loading and formatting raw peak-picked LC-MS metabolomics data frames (via metabData constructor) when you need to eliminate features with poor sample coverage before feature alignment.

Use when you have a preprocessed LC-MS feature table (m/z, retention time, intensity columns) and need to identify which features belong together as isotopes or adducts of the same neutral compound.

Use when you have a preprocessed feature table (tab-delimited: feature ID, m/z, retention time, intensity columns) from LC-MS data and need to annotate which observed features represent the same underlying compound via isotope or adduct relationships.

Use when you have created a feature-based GNPS molecular network and a corresponding MS2LDA experiment, and you need to propagate substructural motif annotations from the MS2LDA output back to the network nodes by matching feature IDs.

Use when when you have parsed MS2 spectra from a single metabolomics sample (via matchms or similar) and need to generate a sample-level feature vector that represents the chemical composition independently of chromatographic alignment.

Use when after anchor selection and retention-time spline mapping have produced a candidate list of feature pair alignments, but before final scoring and reduction of the combined table.

Use when use this skill after XCMS feature detection and alignment on non-targeted LC-MS or GC-MS metabolomics data, when you have aligned features with quantitative profiles across samples and need to group features that co-originate from the same compound (accounting for isotopic peaks, adducts.

Use when you have two LC-MS feature tables (each with m/z, retention time, and intensity columns) and need to establish reliable correspondence between features across datasets.

Use when after blank subtraction and background drift removal in an MS-DIAL peak list, when you need to exclude features with high within-class measurement variability. Apply this when you have replicate samples assigned to distinct classes (e.

Use when after nontargeted peak detection and segmentation has generated a feature table from raw LC-MS data (mzML or vendor format), apply quality assessment when you need to rank or filter features by confidence before annotation, adduct grouping, or MS/MS matching.

Use when you have loaded search results from an upstream proteomics database search (e.g., OpenSwath, DIA-NN) containing feature identification data with Q-value scores, and you need to populate analyte dropdown menus or restrict downstream analysis to only statistically confident identifications.

Use when when you have loaded search result files from one or more DIA-MS analysis tools and need to assess the quantitative performance of identified features.

Use when after signal drift correction and batch effect removal (step 4) have been completed and per-feature D-Ratio values are available, but before normalization (step 7).

Use when you have a filtered MS-DIAL peak list (post-generic filtering, containing m/z, retention time, and peak intensity metrics) and need to identify groups of co-eluting or structurally similar features before extracting parental signals or annotating metabolites.

Use when when you have a quantitative feature table from MZmine2/MZmine3 with peak area and m/z data aligned across multiple extract samples, and you need to identify which features are characteristic of individual samples (high specificity) versus ubiquitous across the extract set.