Claude Skills by HolobiomicsLab

Use when you have received raw or vendor-converted centroid mzML files from LC-MS, GC-MS, or DI-MS platforms and need to extract MS1 spectra before building mass tracks, performing peak detection, or constructing composite feature maps.

Use when when you have an untargeted metabolomics dataset with MS2 fragmentation spectra and need to annotate metabolites beyond what reference databases alone provide.

Use when you have raw mass spectrometry data from an instrument not yet validated in your pipeline (e.

Use when you have multi-sample MS1 data (LC-MS, LC-IMS-MS, or direct infusion across any omics domain) and need to detect samples with abnormal global ion intensity patterns or unusual per-ion metric behavior (intensity distribution, signal-to-noise, retention time stability) that may indicate.

Use when when you have raw mass spectrometry data from direct-infusion (DI-MS) or ambient surface analysis probe (ASAP-MS) instruments and need to identify which m/z peaks are biologically or chemically informative for sample classification, rather than processing the entire spectrum including.

Use when working with imaging mass spectrometry (IMS) datasets where you need to (1) automatically identify marker ions without manual annotation, (2) reduce peak intensity dimensionality while preserving spatial relationships between measurement points, or (3) apply iterative peak picking.

Use when you have centroided LC-MS/MS spectral data (in MGF, mzXML, mzML, or mzData format) and want to identify known or predicted natural product structures present in your sample.

Use when you have raw or processed MS spectrum data (mz/intensity pairs) from direct infusion MS (DI-MS), ASAP-MS, or other high-throughput ambient ionization methods, and need to identify which m/z signals represent true peaks of interest rather than noise or baseline drift.

Use when when you have centroided mzML files from LC-MS metabolomics and need to construct high-mass-resolution mass tracks for each sample before alignment. Apply this skill at the start of an untargeted metabolomics workflow, before building a cross-sample MassGrid.

Use when when you have centroid mzML files from LC-MS metabolomics acquisition and need to construct sample-level mass tracks before cross-sample alignment. Specifically: you are starting fresh with vendor-converted or pre-processed mzML input;

Use when after XCMS feature detection, grouping, retention time correction, regrouping, and missing value filling on LC-MS or GC-MS data, when you have an aligned feature table with retention times and intensity profiles across multiple samples and need to collapse redundant features into.

Use when immediately after generating a feature table (m/z, retention time, intensity) from centroided mzML data when you need to collapse multiple feature detections of the same compound (arising from different ionization states, charge states, or isotope patterns) into unified empirical compound.

Use when after XCMS feature detection and retention time correction, when you have a feature table (CSV or XCMS object) with m/z and retention time values aligned across samples.

Use when after peak detection and MS1 feature picking from merged FIA-MS spectra (typically 0–30 s acquisition window), when you have a list of accurate monoisotopic masses and need to assign HMDB compound identifiers, molecular formulas, and structural annotations to support mzTab output.

Use when when you have raw profile LC-MS data in .mzML format and need to prepare regions of interest (ROI) as input for a CNN-Transformer peak detection network.

Use when you have a Galaxy installation (specifically Galaxy Master branch commit c429777c93680dcee449fe410f5360afbe673758 or compatible) and need to add metabolomics analysis capabilities including tools for XCMS integration, mass spectrometry file reading (via MSFileReader), and metabolite.

Use when you have normalized peak intensities (with assigned molecular formulas) from FT-ICR MS analysis of treated and control bacterial samples (or environmental microbial communities), grouped by two or more treatment factors (e.g., phage type: HP1, HS2, control;

Use when after feature alignment across multiple LC-MS/MS runs, when the unified feature list contains zeros or nulls for specific feature–sample pairs because peaks were not detected in those individual runs, but the feature was detected in other samples in the cohort.

Use when when you have raw LC-MS/MS instrument output files (e.g., .mzML, .

Use when you need to determine the complete set of validated instrument/vendor and acquisition mode combinations for a mass spectrometry analysis tool, when assessing whether your specific instrument platform (vendor, model, acquisition method) is supported before committing to a workflow, or when.

Use when you have FIA-MS, LC-MS, or GC-MS full-scan data in mzML format and need to identify unknown molecular features by accurate mass.

Use when you have merged MS1 spectra (output from spectral binning/merging steps) from a full-scan FIA-MS or LC-MS acquisition and need to identify distinct molecular features before accurate mass annotation or background filtering.

Use when when you have MS1 mass spectrometry data from multiple samples (a cohort) acquired across an instrument run or batch, and you need to identify which samples deviate from cohort norms or which ion targets show anomalous behavior.

Use when when you have acquired LC-IMS-MS/MS data (mzML or mzML.gz format) from multiple samples and need to detect features that exploit simultaneous separation in m/z, drift time, and retention time to improve detection sensitivity and reduce false positives.

Use when you have raw MS data in vendor formats (Agilent .d, Thermo .raw, Bruker .

Use when when invoking Asari to process centroid mzML files for the first time in a PCPFM experiment, or when RT and m/z accuracy characteristics of your LC-MS instrument differ from the pipeline defaults (e.

Use when after completing peak detection, MS1 feature picking, and accurate mass database search (e.g., against HMDB) on FIA-MS or LC-MS(/MS) data.

Use when you have raw LC-MS data in vendor or mzML format and need to systematically discover and extract all detectable metabolite features across the full retention time range, without predefined target lists.

Use when you have raw MS data in instrument-native or mzML format (Agilent .d, Thermo .raw, Bruker .d) and need to isolate specific analyte regions defined by precise m/z windows, RT windows (in seconds or minutes), and/or ion mobility arrival-time windows.

Use when when you have raw LC-MS/MS data files (.mzML, .raw, or vendor formats) from multiple samples and need to identify reproducible molecular features across the cohort before annotation or statistical analysis.

Use when you have a metabolomic feature table (rows=features, columns=samples) with peak height and peak area measurements from chromatographic processing, and you suspect data quality issues such as misaligned features or erratic peak integration across your sample cohort.

Use when immediately after automatic peak detection on a raw or processed MS spectrum when you have a list of candidate peaks with m/z and intensity values but lack systematic identifiers, confidence estimates, or ranked ordering.

Use when when you have mass spectrometry raw data (DI-MS or ASAP-MS format) from plant samples that are easily confused due to morphological similarity, or when you need to verify or authenticate the species identity of a plant material against a reference database.

Use when analyzing GC-MS data containing overlapped peaks where two or more components co-elute within the same retention time window, making direct spectral assignment impossible.

Use when after composite-map peak detection has produced an unfiltered peak list with SNR, peakshape (goodness_fitting), peak_height, and prominence values.

Use when you have a set of small-molecule compounds (e.g., from MS/MS library matching or database annotation) that require retention time validation or ranking to resolve ambiguous identifications.

Use when after filtering LC-MS features by statistical significance (e.g., p-value < 0.01) and you need to link individual m/z features into structural clusters representing the same metabolite in different ionization states or isotopic forms.

Use when when you have picked and annotated MS1 features from replicate injections of the same sample and need to produce a unified feature matrix indexed by sample (not injection).

Use when when you have pairs of mass spectrometry spectra and need to predict their molecular structural similarity as a scalar Tanimoto score in the range [0, 1].

Use when you need to determine the full scope of hardware and methodological compatibility for a bioinformatics tool before designing an analytical workflow.

Use when after identifying candidate parent–adduct mass-difference pairs (via massdiff, histogram binning, and adductMatch), apply this skill to discriminate true molecular adducts from coincidental mass matches.

Use when when you have high-throughput mass spectrometry data (DI-MS, ASAP-MS, LDI-MS, or other ambient ionization formats) from unknown biological samples and need to determine their species identity against a curated reference database.

Use when you have an unknown sample spectrum (m/z peaks and intensities from DI-MS, ASAP-MS, or other high-throughput mass spectrometry modalities) and a reference species database of known spectra, and you need to identify the most likely species or authenticate the sample by ranking how well each.

Use when you have acquired multiple MS1 spectra over a defined acquisition time range (e.g., 0–30 s in FIA-MS) and need to combine them into a unified spectrum before feature detection.

Use when you have raw or processed MS spectrum data (mz/intensity pairs) from direct-injection MS (DI-MS), ASAP-MS, or other ambient ionization instruments (AI-MS, LDI-MS), and you need to identify peaks of interest, assign confidence scores, and prepare the data for database matching or species.

Use when you have preprocessed mass spectrometry data (peak-picked, baseline-corrected) from DI-MS, ASAP-MS, or LDI-MS instruments and need to identify unknown samples by comparing their spectral fingerprints against a validated reference database of known species or compounds.

Use when after noise filtering and baseline correction have been applied to mass spectrometry data (DI-MS, ASAP-MS, LDI-MS, or other high-throughput MS formats in mzML, mzXML, or vendor formats).

Use when you have multi-sample MS1 data (from Agilent, Thermo, Bruker, or mzML formats) and need to quantify ion-level quality attributes—such as signal consistency, noise characteristics, or chromatographic stability—to either flag outlier samples or validate data fitness for downstream.

Use when when you have raw LC-MS or LC-IMS-MS data in instrument format (Agilent .d, Thermo .raw, Bruker .

Use when you have a CSV-formatted target list with m/z, retention time, or ion mobility identifiers and need to locate and extract peak abundances from raw MS data files (Agilent .d, Thermo .raw, Bruker .d, mzML) acquired across LC-MS, LC-IMS-MS, DDA, DIA, or direct infusion modes.