Productivity

holobiomicslab Skill

Use when you have raw LC-MS data (mzML, NetCDF) from multiple runs that require sequential feature detection, alignment, quantification, and optional filtering (e.g., blank exclusion, QC reproducibility, retention-time outlier removal) before spatial mapping or annotation.

holobiomicslab Skill

Use when before launching a multi-tool computational workflow (e.g., QCxMS2 mass spectra calculations) that depends on external programs with version-sensitive APIs or features. Apply this skill when: (1) the workflow has explicit minimum version requirements for one or more dependencies;

holobiomicslab Skill

Use when after completing MS2 annotation in the JPA metabolomics workflow, when you have aligned feature data (feature matrix with m/z, retention time, intensity, and sample assignments) and need to extract and export EIC traces for individual features or feature subsets for external validation.

holobiomicslab Skill

Use when you have access to a GitHub repository with a Maven-based CI workflow (e.g., defined in .

holobiomicslab Skill

Use when use when the workflow requires code_artifact_inspection.

holobiomicslab Skill

Use when after loading an MS-DIAL feature table when you need to separate features into two disjoint groups: one meeting a quantitative threshold (e.g., m/z decimal values outside [4, 8], coefficient of variation below a cutoff, or mass defects within acceptable bounds) and one not meeting it.

holobiomicslab Skill

Use when use this skill at the start of any MSI analysis workflow when you have raw imzML files or vendor-specific MSI data that need to be loaded into napari-MSI-Explorer for visualization, mean intensity calculation, ROI analysis, or annotation.

holobiomicslab Skill

Use when you have peak/feature tables from one or more peak picking tools (MZmine, XCMS, MS-DIAL, Compound Discoverer) and need to ingest them into LipidMatch or combine results from multiple tools in a single lipidomics workflow.

holobiomicslab Skill

Use when after completing Part 4 (Identification of ISF Features) in the ISFrag workflow, when you have an analysis results object containing identified ISF features and need to generate a shareable, tabular export that documents feature annotations, hierarchical parent–fragment relationships, and.

holobiomicslab Skill

Use when starting from raw LC-MS spectral files (mzML or mzXML format) in a global metabolomics study and you need to produce a complete, validated feature table with m/z, retention time, and intensity values across all samples.

holobiomicslab Skill

Use when when you have a pretrained encoder that captures domain knowledge (e.g., spectral feature extraction) and need to train task-specific decoder or scoring modules on top of it without degrading the encoder's learned representations.

holobiomicslab Skill

Use when you have .mzML or .abf LC-HRMS metabolomics raw data files and need to perform peak detection, feature identification, and chromatogram alignment reproducibly across different compute environments (local workstations, HPC clusters).

holobiomicslab Skill

Use when when a user uploads a JSON project document to the Pairing Omics Data Platform, before accepting it into the repository or indexing it for search. Use this skill to catch missing required fields, incorrect field types, and constraint violations early in the submission workflow.

holobiomicslab Skill

Use when you need to verify that a GitHub Actions workflow (such as 'dev_build_release.

holobiomicslab Skill

Use when you have a working R package (e.g., IonFlow for ionomics analysis) with documented functions and parameters, and you need to wrap it as a Galaxy tool so end users can invoke the R workflow through Galaxy's web interface without command-line expertise.

holobiomicslab Skill

Use when you have an OpenMS workflow ready to execute in online_deployment:true mode with Redis available, and you need to submit it as a queued job artifact rather than blocking the Streamlit thread. Trigger conditions: (1) online_deployment flag is true in settings;

holobiomicslab Skill

Use when you need to verify claims about algorithm performance, data processing correctness, or workflow outcomes in a scientific article or software repository. Use it when source documents contain figures, tables, or visualization badges (e.

holobiomicslab Skill

Use when you have located a workflow definition file (YAML or JSON) from a versioned release and need to confirm that all mandatory workflow metadata fields (name, version, inputs, outputs, steps) are declared, properly formatted, and cross-references are resolved before validation or execution.

holobiomicslab Skill

Use when you have a machine learning training workflow (e.g., k-fold cross-validation) where each fold is independent, GPU-accelerated, and can run in parallel.

holobiomicslab Skill

Use when after implementing code changes to MS2Query (e.g., modifying the workflow branching logic for true library matches vs. analog search results), before committing or pushing to the repository.

holobiomicslab Skill

Use when when you have a Nextflow workflow repository with a Makefile, and you need to execute the workflow for local testing or validation. Triggers include: (1) first-time setup of a downloaded workflow; (2) regression testing after code changes;

holobiomicslab Skill

Use when you have multiple pre-trained neural network models (e.g., MLP and GNN) that produce overlapping predictions on the same set of candidates, and your evaluation metric is rank-based (average rank, Rank@K) rather than point-wise accuracy or RMSE.

holobiomicslab Skill

Use when when you have raw mass spectrometry data files from multiple acquisition modalities (LC-MS, GC-MS, ion mobility, or imaging) and need to automatically route each to the correct downstream analysis module without manual intervention.

holobiomicslab Skill

Use when you need to execute a Nextflow metabolomics workflow (e.

holobiomicslab Skill

Use when you have a metabolomic SummarizedExperiment object with replicate QC (quality control) samples and need to remove non-reproducible metabolic features before phenotype association modeling. Use it specifically when your workflow requires FDA-compliant reproducibility thresholds (CV < 0.

holobiomicslab Skill

Use when your workflow fetches data from multiple external web services (e.g., CIR, CTS, PubChem, IDSM, BridgeDb) asynchronously and you need to track which services are available, how often they fail, and their response latencies during a long-running annotation job.

holobiomicslab Skill

Use when when extending MSMetaEnhancer with a new local chemical transformation (e.g., SMILES to InChI) that should execute non-blockingly within an asynchronous annotation workflow.

holobiomicslab Skill

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.

holobiomicslab Skill

Use when after completing pretraining and fine-tuning stages when you have a checkpoint with task-specific performance but need to improve cross-modal consistency.

holobiomicslab Skill

Use when when you have a processed Bruker Solarix FT-ICR mass spectrum object (from ReadBrukerSolarix) and a reference peak list file (SRFA.

holobiomicslab Skill

Use when when preparing to create a release branch in a Maven-based multi-module project (e.g., NMRFx), use this skill to verify that all pom.xml files in the repository tree declare identical version strings. This is a prerequisite for safe release-branch naming (e.g., release/X.Y.

holobiomicslab Bundle

Use when you have untargeted LC-MS/MS MS2 data and want to spread a handful of confident annotations across whole molecular families — build a feature-based molecular network, seed it with spectral-library and SIRIUS/CANOPUS annotations, then propagate compound classes and analogue annotations across network components (MolNetEnhancer / network annotation propagation) so unannotated nodes inherit chemically-plausible identities.

holobiomicslab Bundle

Use when you have an LC-MS metabolomics feature list (m/z, optionally p-values/fold changes) and want biological interpretation without prior identification — feature preparation, mummichog functional analysis from m/z, pathway/enrichment analysis, and pathway-level interpretation.

holobiomicslab Skill

Use when you have downloaded a GNPS archive from either GNPS1 (https://gnps.ucsd.edu) or GNPS2 (https://gnps2.org) and need to parse spectra (spectra.mgf), molecular family networks (molecular_families.tsv), spectral library annotations (annotations.

holobiomicslab Skill

Use when when you have imzML mass spectrometry imaging data files and need to convert raw ion image intensities into quantitative lipid abundance (pmol/mm²) using known internal standards.

holobiomicslab Skill

Use when you have created a GNPS molecular network (classical or feature-based workflow) and separately run an MS2LDA experiment on the corresponding MGF file, and you want to associate each network node with its constituent substructural motifs and visualize which motifs are shared between.