Claude Skills by HolobiomicsLab

Use when after importing mass spectrometry data in .raw, .d, or mzXML format into R and before peak analysis or quality control steps.

Use when you have extracted ion chromatogram (EIC) candidate data from untargeted LC/HRMS files (mzXML, mzML, or netCDF format) and need to identify genuine peaks within each EIC.

Use when after parsing a centroid mzML file into (m/z, scan_number, intensity) tuples, when you need to organize sparse MS1 data for efficient peak detection and cross-sample alignment. Apply this skill when high mass resolution (e.

Use when you have (1) a benchmark dataset of reference peaks with validated m/z, retention time boundaries, and isotopologue assignments, and (2) NPP output feature tables (unaligned and aligned) from tools like XCMS, MZmine 2, or MS-DIAL that you wish to evaluate.

Use when you have LC-HRMS profile-mode data (retention time × m/z matrix format) and need to automatically identify chromatographic peak locations and boundaries prior to feature extraction, reference matching, or training a peak-classification CNN model.

Use when you have centroided mzML data and a curated set of target molecules with known retention time (RT) boundaries, and you need to establish ground-truth peak detection performance metrics (e.

Use when after sliding-window analysis has identified candidate TIC peaks but before extracting chromatographic parameters (retention time, peak width, intensity) from extracted ion chromatograms.

Use when you have a user-defined reference list of isolated, high-confidence chromatographic peaks (ground-truth) matched across multiple LC-HRMS samples, and you need to produce a labelled training dataset large enough to train a CNN model that discriminates true peaks from background noise and.

Use when after peak detection when you have a table of detected peaks with m/z values and retention times from LC/HRMS data, and you observe systematic m/z drift across a batch or population-scale study (n > 500 samples).

Use when when you need to verify that a continuous integration pipeline for a scientific software project (e.g., mzmine) completes successfully, produces expected build artifacts, or fails in a reproducible manner.

Use when you are implementing multiple accessor methods on a backend class that logically depend on each other (e.g., one returns full data and another returns column metadata), and both methods are required by downstream code.

Use when after running inference on test mass spectrometry spectra with a trained deep learning model (e.g., PS2MS) to verify that class label predictions and confidence scores match expected reference outputs.

Use when gNPS has stopped supplying ClassyFire ontology information for your spectral library matches, or when ClassyFire data is missing for in silico structural annotations.

Use when you have a GNPS DBResult file with InChIKey identifiers but lack corresponding ClassyFire superclass, class, and subclass taxonomy annotations—particularly when GNPS spectral library matches no longer include this ontology information by default.

Use when when you have mass-spectrometry data (m/z and intensity pairs or spectral matrices) paired with ground-truth molecular fingerprints or InChIKeys, and you need to learn a non-linear mapping from spectral patterns to structural fingerprints for downstream metabolite ranking or annotation.

Use when you have a published research article describing a new FT-ICR MS analysis tool and need to verify which analytical and visualization features are actually implemented (not just claimed).

Use when importing a new delimited data file (CSV, semicolon-separated, or other formats) into Punc'data that contains high-resolution mass spectrometry results.

Use when after installing a Python package or before running a computational workflow for the first time, to verify that the CLI entry point is properly configured, the Python environment is correctly set up, and to discover available command-line arguments and their defaults (e.

Use when when you have implemented a new or optimized mass spectrometry data processing library and need to demonstrate its computational advantage over established alternatives (e.g., pymzML, pyOpenMS) on real proteomics data. Trigger on availability of: (1) a common input dataset (e.

Use when after mass track extraction and alignment across samples, when you have a MassGrid structure (m/z-aligned mass tracks) and corresponding retention time calibration dictionaries for each sample, and need to prepare input for composite peak detection rather than per-sample peak detection.

Use when after mass tracks have been aligned across samples into a MassGrid structure (m/z-aligned, same mass-to-charge ratio) and retention time calibration dictionaries have been computed for each sample.

Use when you have an unknown mass spectrometry spectrum (acquired experimentally or computationally) and need to assign it to a known drug class or identify candidate structures.

Use when when you have prepared raw mass spectrometry transition data (precursor m/z, product m/z, intensity, retention time, compound identifiers) and need to verify it conforms to EISA-EXPOSOME''s required schema before database import. Specifically: you are building or validating a custom .xlsx/.

Use when when you have a set of query chemicals (e.g., ethyl hexanoate, methyl salicylate, octanal, undecane) and need to evaluate them against reference compound categories, or when you want to restrict structural matching to specific compound type sets (e.

Use when apply this filter after batch correction of compound/internal-standard ratios when you have pooled study quality control (SQC) samples and need to determine which compounds are sufficiently reproducible for downstream reporting. Use it specifically when the nonReportableRSD threshold (e.

Use when you have a large indexed gzip file (igz format) with metadata encoded in the gzip header comment field, and you need to retrieve specific blocks or spectra by integer index without decompressing the entire file.

Use when when you need to validate that a newly published bioinformatics pipeline meets stated performance claims, or when you must characterize how execution time scales with dataset size (sample count, peak count, or formula complexity) and optional analysis features (e.

Use when when you need to compare the computational efficiency of different mass spectrometry libraries on identical data and processing pipelines, or when you want to establish baseline throughput for a library version and validate claims of performance improvement.

Use when you have loaded m/z peak data with metadata that includes a concentration column representing sample loading mass or volume, and systematic intensity variation across samples is suspected to reflect preparation differences rather than true biological variation.

Use when when you have loaded a raw mass spectrum (e.g., ESI_NEG_SRFA.d in Bruker or .raw format) and need to apply one of several noise-threshold strategies based on user preference or spectrum metadata.

Use when when visualizing numeric columns with unknown or highly variable ranges, and the visualization quality depends on parameter selection tied to data statistics. Specifically: after loading a numeric column (e.

Use when after matching MRM transitions against a lipid reference database, when you have candidate lipid identities for each transition and need to rank them by quality and select a single match per transition for export to the labelled lipid-identity table.

Use when after executing forward inference on preprocessed mass spectrometry spectra with a deep learning model (e.g., PS²MS), when you have per-spectrum predictions with associated confidence scores or per-class probabilities.

Use when you need to set up a LipoCLEAN analysis for MS-DIAL lipid identifications and do not yet have a configuration file, or you are switching between MS-DIAL versions 4 and 5 and need version-specific parameter defaults (e.g., column names and scaling factors that differ between versions).

Use when when you have PSM files from proteomics search engines (MaxQuant, MSGFPlus, Sage, etc.) that use non-standard modification notation (e.g., 'ox', '+57.02146', or mass-shift labels) and need to resccore peptide identifications with MS²Rescore.

Use when when designing a library that needs to support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and you want to avoid reimplementing parameter validation, storage, and dispatch logic for each backend.

Use when when you have generated or edited a LipoCLEAN `options.txt` file using `--print MSD4` or `--print MSD5` and need to verify it is well-formed before running the analysis. Use this skill as a pre-flight check before invoking `--options options.

Use when after constructing individual mass tracks from mzTree data bins and before alignment across samples.

Use when you need to deploy a containerized web application (such as MetFrag webapp on Tomcat) and make it accessible at a specific HTTP endpoint on the host machine. Use this skill when you have a Docker image with an internal service listening on a known port (e.

Use when executing containerized conversion tools (e.g., AirdPro CLI) that must read vendor-format mass spectrometry raw files from the host filesystem and write converted output (e.g., .aird files) back to a persistent host directory. This is essential when first-run Wine initialization and .

Use when when you have a .msp mass spectra file with incomplete or missing chemical metadata fields (SMILES, InChI, CAS number, formula, InChIKey, IUPAC name) and need to populate those fields by querying multiple external chemical identifier services and local cheminformatics tools in parallel.

Use when when implementing a custom MsBackend class for the Spectra package, you must define these two methods to satisfy the MsBackend virtual class API contract.

Use when after applying one or more intensity drift correction strategies (Internal Standard correction, statistical drift correction, custom or weighted bracketing) within QuantyFey and before exporting the corrected intensity table for final quantification.

Use when after XCMS feature detection and retention time correction, when you need to group features derived from the same compound. Features from the same compound show correlated quantitative trends across samples;

Use when you have implemented both CPU and GPU versions of a spectral search algorithm (e.g., approximate nearest neighbor indexing with cascade filtering) and need to validate whether GPU acceleration is justified.

Use when you have received MSBERT-preprocessed spectral data from GNPS, MoNA, or MTBLS1572 and need to ensure data integrity before training a spectral embedding model.

Use when you have two or more feature tables in HDF5 format with detected features characterized by m/z, drift time, retention time, and intensity, and you need to match corresponding features across samples to account for systematic shifts caused by instrument variation or tuning differences.

Use when when building a unified sequence model (e.

Use when when a new file format specification has multiple language implementations and you need to validate that all implementations correctly interpret the specification.

Use when when you have domain-specific functionality (e.g., spectral similarity scoring, peak detection algorithms) implemented in one language (Python) but need to make it callable and composable within an R-based analytical pipeline (Spectra objects);