Claude Skills by HolobiomicsLab

Use when when you need to confirm that a documented web service endpoint is deployed and accessible before using it for analysis, or when troubleshooting tool availability in a bioinformatics pipeline. Apply this skill after obtaining a service URL (e.

Use when a webservice component (like MAGMa's joblauncher) lacks formal API documentation but the source code is accessible, and downstream consumers (web applications, external services) need to understand available HTTP endpoints, parameter schemas, and response formats without manual.

Use when you have a SMILES string or batch of SMILES strings representing chemical structures and need to obtain NP Classifier predictions programmatically.

Use when you have NMR peak data (1H and 13C chemical shift values) and need to obtain SMART 3 classification predictions from the DeepSAT service.

Use when after combining multiple EI or MS2 mass spectral libraries and you have access to NIST RI reference files (ri.dat and USER.DBU) and need to assign experimental retention indices to compounds.

Use when processing raw GC-MS data in NetCDF format where peaks have been detected but lack standardized retention indices.

Use when you have a compiled EI or MS2 library object (from read_lib or c() combination of multiple sources) and a local NIST library installation with accessible ri.dat and USER.DBU files in the mssearch/nist_ri directory.

Use when after applying AbundanceSimilarityParam (with threshold ≥0.7 and log2 transform) to retention-time-based feature groups from SimilarRtimeParam, when you need to examine the internal correlation structure of specific feature sub-groups (e.g., FG.

Use when you have MS2 fragmentation spectra from multiple samples (in .mgf, .mzML, or .mzXML format) and want to compare them despite poor feature overlap, strong RT shifts between acquisitions, or use of different LC-MS platforms (e.g., Orbitrap vs. Q-ToF).

Use when when processing multiple LC-MS samples with varying scan numbers or retention-time drift, before constructing composite mass tracks for peak detection.

Use when after sample alignment step in untargeted LC-MS workflows, particularly when processing multi-sample cohorts with QC samples interspersed throughout the sequence.

Use when you have raw GC-MS output from Agilent Unknowns Analysis (a .CSV with columns Component.RT, Base.Peak.MZ, Component.Area, Compound.Name, Match.Factor, File.

Use when after peak detection has been completed on individual LC-MS samples and you have a collection of detected peaks with m/z, retention time, and intensity values from each sample.

Use when after chromatographic peak detection on preprocessed LC-MS data when you have an xcms result object (XcmsExperiment or xcmsSet) with detected peaks and need to collapse redundant m/z signals into feature groups.

Use when after mass track extraction and alignment across samples, when preparing to detect elution peaks on composite mass tracks. Use this when inter-sample retention time variation exceeds acceptable alignment tolerance (e.

Use when you have multiple LC-MS runs with the same set of targets (compounds) and observe or expect retention time drift or jitter between runs.

Use when when you have Thermo Orbitrap .raw files containing known reference peptides (e.

Use when you have two independent LC-MS untargeted metabolomic feature datasets (each with retention time and m/z values) and need to identify which features in one dataset correspond to features in the other.

Use when after sample alignment and grouping of isotopologues and adducts have been completed, when the aligned feature table contains NA or zero entries (missing intensities) for features that are detected in some samples but fall below the detection threshold in others.

Use when you have a set of small-molecule structures (SMILES, InChI, or SDF format) and need to predict their chromatographic retention times for a specific method, either to support metabolite annotation, validate experimental assignments, or project RTs across different chromatographic platforms.

Use when when you have retention times measured on one chromatographic method and need to predict or map them to another method with minimal or no overlap in measured molecules.

Use when you have LC-MS/MS spectral data (in MGF, mzXML, mzML, or mzData format) and corresponding genomic sequence data (raw FASTA nucleotide sequences or genome mining tool outputs like antiSMASH .final.

Use when you have a normalized peak-abundance matrix from FT-ICR MS data (peaks as rows, samples as columns) and need to compare the number and diversity of detected molecular species across experimental conditions—for example, to test whether inoculation or environmental perturbation alters the.

Use when you have LC-MS/MS spectra (MGF, mzXML, mzML, or mzData format) and either raw genome nucleotide sequences or antiSMASH/BOA genome mining tool output, and you need to identify which RiPPs are present in your sample.

Use when when you have genomic sequences (assembled contigs or antiSMASH/BOA mining results) and want to match experimental tandem mass spectra against predicted RiPP structures.

Use when after identifying precursor peptides from genome assemblies via BGC mining, when you need to enumerate the chemical space of PTM variants (lantibiotic, lassopeptide, cyanobactin, or other RiPP classes) before matching tandem mass spectra against a constructed database using Dereplicator or.

Use when you have: (1) tandem MS/MS spectra in MGF, mzXML, mzML, or mzData format from LC-MS/MS analysis; (2) a set of predicted RiPP precursor peptides derived from genomic biosynthetic gene cluster mining (via antiSMASH, BOA, or raw FASTA);

Use when you have completed pathway analysis using multiple competing methods (e.g., PALS, ORA, GSEA) on a metabolomics peak intensity dataset and need to verify that ranking results remain stable when input data is intentionally corrupted.

Use when after training a NeatMS CNN model on labeled MS1 peaks and generating predictions on a held-out test set, compute ROC-AUC to assess whether the model achieves the target discrimination threshold (AUC ≥ 0.9) without evidence of overfitting.

Use when when evaluating a regression or similarity prediction model and you need to understand whether prediction error is uniform across the outcome space or concentrated in particular ranges (e.g., low structural similarity vs. high similarity scores).

Use when you have a trained regression model (e.g., a neural network or similar predictor) and a held-out test set with ground-truth continuous labels, and you need to measure whether the model's predictions match the true values.

Use when when you have implemented conditional routing logic in the GNPS_MASST codebase and need to verify that spectrum submissions with explicit domain-context selections (e.

Use when when you need to empirically validate that one mass spectrometry data processing library achieves higher throughput than competing alternatives. Specifically: you have multiple candidate libraries (e.

Use when you have obtained a Rust source repository (e.g., mzpeak_prototyping) and need to compile it into a working command-line converter tool or library. Use this skill when the source includes a Cargo.

Use when you are designing a new backend or data container that must integrate seamlessly with an existing Spectra-based workflow. You have identified a virtual parent class (e.

Use when you are extending the MsBackend virtual class to create a new backend for storing MS spectra data and need to define the internal data structure. Specifically: when you have multiple types of spectra variables (e.

Use when you are building a new data representation or storage strategy for MS spectra (e.g., on-disk HDF5, SQL database, remote file access) and need to integrate it seamlessly into workflows that use the Spectra package.

Use when you need to verify that an S4 replacement method (e.g., `mz<-`) in a bioinformatics backend class correctly validates input data using vectorized operations on NumericList or similar container objects, rather than inefficient loops or apply functions.

Use when when designing or optimizing S4-based data backends (such as MsBackend subclasses) and you need to decide whether to pre-populate all slots with complete data structures or defer initialization until data access.

Use when after MZmine feature detection and molecular networking on a single LC-MS/MS DDA sample, when you have a feature table (with retention time, m/z, fragmentation spectra) and sample-level taxonomical metadata, and you want to assign both spectral identity and predicted chemical structure to.

Use when when you have loaded centroided .mzML files into a Spectra object and plan to use TARDIS (tardisPeaks) with an MsExperiment object rather than file paths, and you need TARDIS to distinguish QC runs from sample runs for separate quality metric calculation, polarity filtering, and.

Use when after generating aligned MS2 fingerprints (sample-by-fingerprint matrices) from metabolomics data when you need to visually inspect sample clustering, identify sample similarities, or detect batch effects and RT shifts across different LC methods or mass spectrometer technologies.

Use when when beginning mass alignment in a multi-sample LC-MS metabolomics study, before constructing the MassGrid.

Use when after feature detection but before statistical analysis, when your study includes blank samples (e.g., solvent or extraction blanks) and you want to remove features that fail to show meaningful enrichment in actual study samples relative to blank contamination.

Use when you have unaligned MS2 spectra from multiple metabolomics samples (in mzML, mzXML, or MGF format) and need to compare them without relying on retention time or aligned m/z features.

Use when you have a new or modified LC-MS data processing tool and need to determine whether it can handle production-scale sample cohorts (50–100+ samples) on modest hardware (single-core CPU, ≤16 GB RAM).

Use when you have a Thermo Orbitrap .raw file and need to (1) verify that a targeted acquisition method (e.g., PRM) maintains consistent scan spacing across all cycles; (2) extract only scans matching a specific precursor ion and fragmentation method;

Use when when processing LC-MS data with multiple overlapping m/z scan windows and observing sawtooth-pattern distortions in EICs during tardisPeaks() execution.

Use when a user uploads a JSON project file to the platform and you need to verify it matches the required format defined in app/public/schema.json before accepting it into the database.

Use when you are starting a new mass spectrometry analysis task or feature request where the problem scope is unclear, the tool chain (e.g., OpenMS + Python + KNIME integration) is not yet selected, or acceptance criteria (code quality, test coverage, documentation) have not been established.