Claude Skills by HolobiomicsLab

Use when you have raw IM-MS data (Agilent MassHunter .d or UIMF format) and need to exclude early or late chromatographic regions—e.g., to skip dead volume, exclude blank runs, focus on a known analyte window, or reduce file size for faster processing.

Use when when aligning detected features across multiple LC-IMS-MS/MS samples and you need to identify which input samples contributed to each consensus feature cluster, especially to filter out spurious or low-confidence alignments, validate clustering completeness, or perform sample-specific.

Use when processing IM-MS data files (Agilent .d or UIMF format) that contain high-abundance ions suspected of signal saturation, particularly in untargeted or discovery proteomics/metabolomics workflows where dynamic range compression would obscure quantitative relationships.

Use when when executing peak integration on preprocessed GC-IMS data (after alignment and baseline correction) and you need to decide whether to include or exclude peaks that exhibit saturation artifacts from the RIP signal. Set a threshold (e.g., 0.

Use when after filtering retention time and drift time ranges on raw GCIMS samples but before decimation and alignment.

Use when when you have completed a DIA-MS proteomics search (e.

Use when when evaluating whether an MS data processing platform (such as mzmine) supports the full range of separation/ionization techniques your laboratory uses, or when assessing whether gaps exist in the software architecture that would require external pre- or post-processing for specific.

Use when after executing multidimensional smoothing, spike removal, or saturation repair on raw TOF-MS or IM-MS data (.d format from Agilent MassHunter) to confirm that signal quality has improved.

Use when you observe jagged or noisy peak profiles in low-abundance ions after loading raw IM-MS data (Agilent MassHunter .

Use when analyzing 1D signal arrays (e.g., extracted ion chromatograms, arrival time distributions, or MS1 spectra intensity profiles) where multiple peaks may overlap or where peak shape information (amplitude, position, width) is required beyond simple local-maximum detection.

Use when you have a set of metabolites or chemical formulas to analyze and want to evaluate how different MS/MS fragmentation strategies (e.g., TopN, exclusion lists, dynamic window selection) would perform without access to real instrument time.

Use when you have detected monoisotopic features (m/z, drift_time, retention_time, intensity) from LC-IMS-MS/MS data and need to identify and cluster their C13 isotopologues for charge state z=+1.

Use when when you have ionized adduct structures (SMILES or MOL format) from a prior ionization-state determination step and need to create multiple low-energy 3D conformations before filtering with machine-learning potentials (ASE-ANI) or quantum calculations.

Use when when you have SMILES structures of small organic molecules and need to predict CCS values for metabolite annotation in untargeted mass spectrometry workflows. Specifically, apply this skill when the same chemical entity may appear in multiple ionization states (e.

Use when when processing raw SMILES strings from external databases or user input that may contain non-canonical tautomeric forms, variable stereochemical notation, or redundant representations of the same chemical structure.

Use when you have a SMILES input file of small organic molecules and need to predict their collision cross sections or other molecular properties via quantum mechanics.

Use when you have multiple mzML or mzML.gz files from LC-IMS-MS/MS instruments and need to apply DEIMoS feature detection, alignment, and calibration operations in a reproducible, traceable manner.

Use when you need to verify the scope and completeness of a software platform's analytical capabilities—particularly when the project claims to support multiple input modalities (e.

Use when when analyzing imaging mass spectrometry datasets where you need to reduce high-dimensional peak intensity features while preserving spatial structure, and when automatic peak picking and marker ion identification are required.

Use when you have raw IM-MS data in Agilent MassHunter (.d) or UIMF format from drift tube (DT) or SLIM instruments, and you need to reduce data volume while preserving signal integrity for subsequent HRdm demultiplexing and peak deconvolution.

Use when when you have MS2 .mzML format data files from untargeted metabolomics or proteomics experiments and need to perform an initial annotation step by matching experimental spectra against known reference libraries (GNPS, HMDB, MassBank) with a defined precursor mass tolerance (e.g., 15 ppm).

Use when you have a detected feature table (m/z, drift_time, retention_time, intensity) and need to identify and label C13 isotopic clusters for singly-charged features (z=+1).

Use when after removing precursor and noise peaks from an MsmsSpectrum object when the spectrum contains peaks with highly variable intensities (e.g., one or two dominant peaks with many weaker fragments).

Use when you have raw line-scan MSI data from any supported vendor (Agilent .d, Bruker .tsf/.baf/.tdf, Thermo .raw, or .

Use when when building a comprehensive reference spectral library for metabolomics or chemical identification, you have multiple source libraries in different formats (msp, mgf, NIST binary) and ionization modes (positive/negative MS/MS or EI) that need to be combined into a single.

Use when immediately after extracting ion chromatograms (EICs) by binning mass spectral data across the full m/z range from raw LC/HRMS files (mzML, mzXML, or netCDF format).

Use when you have raw MS/MS spectra with variable numbers of peaks at continuous m/z values and need to feed them to a neural network (e.g., Siamese network for similarity prediction) that requires fixed-size vector input.

Use when when you have raw high-resolution tandem mass spectra (mzML, mzXML, or MGF format) that you intend to cluster or compare at scale, and you need to convert continuous m/z and intensity measurements into discrete bins suitable for feature hashing or similarity searching.

Use when after peak detection on individual GC-IMS samples, when you need to assign consistent cluster IDs to peaks detected across multiple samples to enable cross-sample comparison and quantification.

Use when raw Agilent MassHunter (.d) or UIMF mass spectrometry files exhibit jagged or noisy peaks, particularly for low-abundance ions where signal-to-noise ratio is poor.

Use when after generating TP candidates (via in-silico prediction or library lookup) and extracting MS/MS peak lists for both parent features and TP feature candidates, use spectral similarity scoring to quantify fragmentation pattern overlap.

Use when you have loaded multidimensional MS data (from MZA HDF5 files or other formats) and need to examine a specific m/z region—for example, to visualize a known lipid or metabolite mass range, perform peak detection within a narrow window, or reduce computational overhead by working on a subset.

Use when when preparing raw MS/MS spectra for input to a Siamese neural network trained to predict structural similarity scores (Tanimoto).

Use when processing raw IM-MS data (Agilent MassHunter .d or UIMF format) that exhibits isolated high-intensity noise artifacts or instrumental artifacts that appear as discrete, non-continuous signals in the retention time, ion mobility, or m/z dimensions.

Use when you have LC-IM-MS/MS raw data from sterol-containing tissue samples and need to assign detected peaks to specific structural isomers (e.g., distinct double bond positions or saturation patterns in C27–C29 sterols).

Use when you have LC-IM-MS/MS experimental data (raw mzML or vendor format) containing signals from N-Me derived unsaturated sterol lipids and need to assign double-bond positions and stereochemistry to individual sterol isomers rather than sum compositions.

Use when when you have a collection of N-Me derivatized unsaturated sterol structures from tissue samples or standards that must be fed into MS/MS fragmentation prediction or collision cross section (CCS) prediction workflows.

Use when you have obtained a raw reference library file (such as the DTCCS_N2 library for U13C labeled lipids) and need to validate its structure, verify that all expected lipid entries are present, and ensure CCS values fall within physically plausible ranges (typically 50–300 Ų for small lipids).

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 aligned features characterized across multiple dimensions (m/z, drift time, retention time) and need to: (1) resolve MS/MS spectra that may contain fragments from multiple co-eluting or co-mobilizing precursors; (2) identify and validate isotopic signatures (e.

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.

Use when when you have raw diaPASEF mzML files, a transition list with target analytes (protein, peptide, charge state), and search results (DIA-NN, OpenSwath output) containing feature metadata (retention time, ion mobility, m/z coordinates), and you need to isolate and visualize signal for.

Use when you have raw MS data in a supported instrument format (Agilent .d, Thermo .raw, Bruker .d, mzML) and a predefined list of molecular targets (CSV with m/z and/or retention time) that you need to quantify.

Use when you have acquired multidimensional mass spectrometry data (MS1, MS/MS, or data-independent acquisition) from a Thermo instrument saved in the proprietary '.

Use when you have LC-IM-MS/MS raw data from multiple tissue samples and need to identify and quantify unsaturated sterol lipids at the isomer level (distinguishing double-bond position and stereochemistry).

Use when your raw TOF-MS data (Agilent MassHunter .d format) exhibits jagged, artifact-prone peaks in low-abundance ions that compromise peak quality assessment or when you need to improve signal-to-noise before ion mobility demultiplexing or peak deconvolution.

Use when you have exported lipid identifications from MS-DIAL (version 4 or 5) and need to run LipoCLEAN quality filtering on that output.

Use when after parent chemical suspects have been identified in a non-target screening workflow, use this skill when you need to screen for downstream products formed by chemical or biological transformation.

Use when when you have loaded a TransitionGroup (extracted ion chromatogram or mobilogram from DIA mass spectrometry data) and need to identify precise peak boundaries, apex retention/drift time, and intensity values for quantitative feature detection.

Use when you have raw TWIM-MS data with arrival times (detector timestamps) rather than drift times, and you need to calibrate collision cross section values. TWIM platforms inherently record arrival time, not drift time, so this correction must precede CCS calibration workflows.