Productivity

holobiomicslab Skill

Use when you have multiple MSP (mass spectrum) library files to read and merge in R, and your computational task is time-consuming (e.g., structure extraction, SMILES assignment, or RI assignment) and you have a multi-core system available.

holobiomicslab Skill

Use when you have received a Bruker Solarix FT-ICR raw data directory (.d format, containing CompassXtract output or native ser/fid transients) and need to import it into a Python-based analysis workflow for FT-MS signal processing, calibration, or molecular formula search.

holobiomicslab Skill

Use when after running in silico annotation tools (SIRIUS, ISDB) or spectral library matching on your feature table, when you need to retain only annotations meeting a minimum confidence threshold.

holobiomicslab Skill

Use when when you need to convert vendor-specific raw mass spectrometry files (.raw) to the open mzML format using imzML Writer, and msconvert is not yet installed or its location is not recognized by the system.

holobiomicslab Skill

Use when when you have cloned a Python package repository and need to verify that the package installs correctly and its test suite passes locally. This is the prerequisite workflow before running pytest or code formatters like black on the package source.

holobiomicslab Bundle

Use when you have a spectrum or feature of interest and want to know where else it occurs across all public metabolomics data — query preparation, repository-scale fastMASST search, specialized microbe/plant/food MASST for ecological context, and co-occurrence analysis.

holobiomicslab Skill

Use when when deploying a Nextflow workflow across multiple execution environments (local machines, HPC clusters) where tool versions, dependencies, or OS configurations may differ. Choose this skill specifically when you need to process LC-HRMS .mzML or .

holobiomicslab Skill

Use when when integrating MSConvert into an automated LC-MS QC workflow and you need to confirm that vendor acquisition files are properly converted to mzML format with intact spectral metadata. Apply this skill after each MSConvert invocation or when QC results appear incomplete or anomalous (e.

holobiomicslab Skill

Use when you have raw or unprocessed MS/MS spectral data in standard metabolomics formats (MGF, mzML, mzXML, msp, or JSON) and need to import them into a Python-based workflow for MS2 fingerprint generation, peak counting, or spectral similarity scoring.

holobiomicslab Skill

Use when when you have a new or updated R package available via a non-CRAN repository (such as r-universe) and need to verify it installs cleanly, passes R-CMD-check compliance, and is ready for downstream workflow execution.

holobiomicslab Skill

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

holobiomicslab Skill

Use when applied immediately after loading raw SMILES strings from external databases or user input during the 2_curating workflow stage, before attempting canonicalization or 2D/3D coordinate generation.

holobiomicslab Skill

Use when you have LC-MS/MS DDA metabolomics data (positive and/or negative ionization modes) and sample metadata (originating taxon) for one or more samples, and you need to generate a Wikidata-connected RDF knowledge graph for integrated natural products analysis, taxonomy-aware compound.

holobiomicslab Skill

Use when you have UPLC-HRMS data from ThermoFisher, Agilent, or other vendor instruments (converted via MSConvert if needed), organized as batch-processed files ready for MSThunder input, and you need to identify unknown organic pollutants with deep learning-assisted structure prediction and.

holobiomicslab Skill

Use when when you have aligned peak-alignment data from a preceding molecular networking task (structured as a table with peak intensity, m/z, retention time, and alignment quality metrics) and need to visualize and interactively filter peaks across multiple spectra to support comparative mass.

holobiomicslab Skill

Use when when you have a Python package repository on GitHub and need to automatically verify that pull requests and commits pass unit tests and meet code quality standards before merge.

holobiomicslab Skill

Use when you have located a workflow definition file (YAML or JSON) in a versioned release or commit and need to verify that it conforms to the schema specification for that release version (e.g., v1.0.0).

holobiomicslab Skill

Use when you have multi-language code implementations (R and MATLAB scripts) for a single scientific workflow, documented example scripts for a reference sub-sample scenario, and need to verify that outputs from one language can serve as inputs to the next, or that both implementations produce.

holobiomicslab Skill

Use when when you have unlabeled molecular structure data (SMILES or molecular graphs) from natural products and need to learn task-agnostic representations that capture both evolutionary (scaffold-level) and structural (side-chain) information before finetuning on downstream classification or.

holobiomicslab Skill

Use when when you need to benchmark multiple encoder types (e.g., FFN vs. GNN) on the same predictive task and require evidence that performance differences reflect genuine architectural trade-offs rather than suboptimal tuning.

holobiomicslab Skill

Use when when you need to reproduce published ORA simulation results, validate pathway enrichment findings from a metabolomics study, or examine pitfalls and practices in Over-representation Analysis methodology.

holobiomicslab Skill

Use when you receive mass spectrometry data through heterogeneous identifier formats—specifically when the input could be a GNPS Task ID, a Universal Spectrum Identifier (USI), or a Feature-Based Molecular Networking (FBMN) identifier—and you need to programmatically determine which format was.

holobiomicslab Skill

Use when after cloning the ENPKG repository and installing dependencies using uv sync or conda, before executing the workflow on metabolomics datasets.

holobiomicslab Skill

Use when when setting up a new LC-MS QC workflow or modifying existing QC rules: you have access to internal standards and target analytes, know their expected retention times and m/z values, and need to establish pass/fail boundaries for sample acceptance before or concurrent with instrument runs.

holobiomicslab Skill

Use when you have a collection of mzML or mzML.gz files from LC-IMS-MS/MS experiments and need to apply a consistent, reproducible sequence of feature detection, alignment, CCS calibration, isotope detection, and MS/MS deconvolution operations across multiple samples.

holobiomicslab Skill

Use when when you have deposited or archived biosynfoni fingerprint vectors (such as from Zenodo 10.5281/zenodo.14822624) and need to ingest them into a Python workflow to compute distributional statistics, bit-frequency profiles, sparsity metrics, or pairwise similarity coefficients.

holobiomicslab Skill

Use when you have multi-class or time-course metabolomic peak tables (with or without quality control samples and/or internal standards) and need to select the preprocessing workflow from hundreds of candidate combinations.

holobiomicslab Skill

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.

holobiomicslab Skill

Use when your R-based Spectra analysis workflow requires a specific mass spectrometry algorithm (e.g., CosineGreedy similarity scoring, spectral normalization, or advanced filtering) that is available only in a Python MS package (matchms, spectrum_utils) and not yet in native R.

holobiomicslab Skill

Use when you have search results from DIA mass spectrometry data containing feature Q-value scores and need to restrict the analyte choices available to users in a GUI to only those meeting a 1% feature Q-value threshold.

holobiomicslab Skill

Use when when you have a collection of mzML.gz files from a multidimensional MS instrument (e.

holobiomicslab Skill

Use when a GitHub repository displays a CI workflow badge (e.g., passing/failing status in README) and you need to verify that the reported status is accurate, reproduce the CI environment locally, or debug workflow failures.

holobiomicslab Skill

Use when when you have multi-branch deep learning architecture predicting related but distinct peptide properties (charge state, isotope count, retention time) from raw mass spectrum, and you want to leverage auxiliary task gradients to improve primary task learning.

holobiomicslab Skill

Use when you have raw quantification data (abundance or intensity values across samples and features) from mass spectrometry or similar high-dimensional assays and need to prepare it for msFeaST's feature selection workflow, which requires standardized internal data structures compatible with.

holobiomicslab Skill

Use when when beginning a LipoCLEAN analysis workflow and needing to configure MS-DIAL export file locations, filtering parameters, and other tool options. Specifically when you have MS-DIAL 4 or MS-DIAL 5 exported data and need to prepare an options.txt file before running lipid quality filtering.

holobiomicslab Bundle

Use when you have mass-spectrometry imaging data (imzML, e.g. MALDI/DESI) and want spatially-resolved metabolite annotations — pixel preprocessing and m/z alignment, FDR-controlled spatial annotation, spatial segmentation, and region-wise comparison.