DevOps & Infra

holobiomicslab Skill

Use when you have multiple independent peak-picking algorithms available and need to allow end-users to select among them for the same analytical task (peak detection in untargeted LC-MS data) without coupling the rest of your pipeline to each algorithm's API.

holobiomicslab Skill

Use when transitioning feature intensity data between pipeline stages (e.

holobiomicslab Skill

Use when after applying mspcompiler pipeline transformation steps (e.g., reorganize_mona, assign_smiles, assign_ri, read_multilibs, separate_polarity, complete_mgf) to confirm the operation succeeded without data loss or structural corruption.

holobiomicslab Skill

Use when when you have obtained a DrugBank release file (requiring access credentials) and need to integrate drug chemical structure, name, and identifier information into a metadata cleanup or chemical enrichment pipeline.

holobiomicslab Skill

Use when you have raw LC-MS/MS spectral data in vendor formats or unvalidated .mgf files before feeding them into the specXplore importing pipeline.

holobiomicslab Skill

Use when you have raw LC-HRMS metabolomics data in mzML or ABF format that needs to be processed through a reproducible pipeline. Use this skill when: (1) you have public or proprietary .mzML LC-MS datasets (e.g. from MetaboLights, MassIVE, or PRIDE);

holobiomicslab Skill

Use when a practitioner has pre-computed features from an external feature-finding procedure (e.g., vendor software, alternative open-source tools) and wishes to incorporate them into PFΔScreen's PFAS prioritization pipeline without re-detecting features from raw mzML data.

holobiomicslab Skill

Use when you have raw LCMS data in mzML format and a feature table (CSV) from a peak detection pipeline (e.g., MZmine) and need to prepare these inputs for NeatMS preprocessing, batch creation, or peak classification. This skill is the mandatory entry point for any NeatMS workflow.

holobiomicslab Skill

Use when after applying a configuration fix (e.g., adding an instrument type to an allowlist, updating filtering thresholds) to a dataset preprocessing pipeline, you need to confirm that the change produces the documented training/test split counts.

holobiomicslab Skill

Use when after implementing or modifying the scoring module that computes average InChIKey scores and neighbourhood scores for candidate matches, or when integrating new scoring logic into an existing MS2Query pipeline.

holobiomicslab Skill

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.

holobiomicslab Skill

Use when you need to validate that a software project's release branch is stable and ready for distribution. Specifically, use it when: (1) a release tag has been created or code pushed to a release branch; (2) you need to confirm that CI/CD pipelines execute without failures;

holobiomicslab Skill

Use when you need to validate that a scientific software project's continuous integration pipeline is functional and producing reproducible builds—particularly before releasing new versions, after merging changes to release branches, or when troubleshooting build failures that block distribution of.

holobiomicslab Skill

Use when you need to automate testing and quality checks on code changes—specifically when pull requests or commits are made to a repository and you want to verify that builds succeed, test suites pass, and code quality metrics meet project standards before merging.

holobiomicslab Skill

Use when when you have a list of chemically known compounds and need to validate that an MS processing pipeline (e.g., mzExacto) correctly retrieves their characteristic m/z, retention time, match factor, and area values from GC-MS data.

holobiomicslab Skill

Use when after executing a Nextflow-based MS-DIAL workflow on .mzML LC-HRMS metabolomics data using Docker or Singularity container backends.

holobiomicslab Skill

Use when you need to deploy CloMet for the first time on a new system, or when you want to ensure reproducible execution of metabolomics data harmonization tasks without manual dependency management.

holobiomicslab Skill

Use when after building multiple Docker image variants (e.g., cli, dev, linux, windows) using multi-stage builds with --target flags, and you need to verify that each variant's size falls within documented ranges (e.g., cli 6–7 GB, dev 9–11 GB, linux 8–10 GB, windows 4–5 GB).

holobiomicslab Skill

Use when you need to containerize a C#-based Windows application (like AirdPro CLI) for Linux deployment, require Wine and .NET Framework 4.

holobiomicslab Skill

Use when you need to verify that a GitHub Actions workflow (such as a development build release pipeline) has completed successfully, capture its build artifacts (installers, portable binaries, or packages), and document the workflow run metadata.

holobiomicslab Skill

Use when when deploying a new Python package in a reproducible analysis environment or continuous integration pipeline, and you need to confirm that all required core modules (e.g., pyteomics.mass, pyteomics.pepxml, pyteomics.mzid, pyteomics.tandem, pyteomics.

holobiomicslab Skill

Use when when you have a fragment peak list (m/z values and intensities) from MS/MS data and need to assign candidate chemical subformulae to each peak as part of a de novo chemical formula inference pipeline.

holobiomicslab Skill

Use when you have vendor-format LC-MS acquisition files (.raw, .d, .ms) from instrument runs and need to set up an end-to-end data quality control system that converts proprietary formats into open mzML, processes spectral data, and surfaces QC failures in real time.

holobiomicslab Skill

Use when when building a mass-spectrometry analysis pipeline that must support multiple vendor and open formats (mzML, mzXML, Thermo, Agilent, Bruker, etc.) and you need to shield downstream tools from format-specific parsing logic.

holobiomicslab Skill

Use when your analysis requires msconvert or another ProteoWizard tool on macOS, but native installation is infeasible or licensing-restricted. You need to convert vendor raw mass spectrometry files (.raw) to the open mzML format without installing ProteoWizard directly on your system.

holobiomicslab Skill

Use when when you need to verify that a GitHub Actions workflow (such as a development build or release pipeline) executes without fatal errors and produces expected artifacts. Use this skill when the workflow is already configured in a repository (e.g., a .yml file in .

holobiomicslab Skill

Use when after converting or downloading a pre-trained Keras model to HDF5 TensorFlow 2.3.0 format, particularly when integrating the model into a fixed-interface pipeline (e.g., NP Classifier) that expects specific named input/output layers.

holobiomicslab Skill

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);

holobiomicslab Skill

Use when when you need to verify that a Java project's automated build pipeline (GitHub Actions workflow) executes without errors and generates distributable artifacts (e.g., .deb installers, portable binaries, or .jar files).

holobiomicslab Skill

Use when you have raw LC-HRMS metabolomics data in .mzML or .abf format and need to perform peak detection, feature alignment, and metabolite annotation in a reproducible, containerized environment.

holobiomicslab Skill

Use when your LC-HRMS metabolomics analysis must run on a high-performance computing cluster (e.g., HiPerGator, SLURM-managed systems) that lacks Docker support or prefers Singularity for security and portability. You have .mzML or .

holobiomicslab Skill

Use when when you have Thermo Fisher Scientific .raw files from an Orbitrap instrument and need to identify the m/z value and corresponding intensity of the most abundant ion in each MS1 scan for quality control, method optimization, or feature extraction in a modular R-based proteomics pipeline.

holobiomicslab Skill

Use when you have a mature R analysis pipeline (e.g., using Spectra objects for MS data) but need access to specialized algorithms or calculations available only in Python libraries (e.g., CosineGreedy similarity from matchms).

holobiomicslab Skill

Use when you have generated or received mzPeak files from a Rust, Python, R, or other implementation and need to verify they comply with the published HUPO-PSI specification before integration into a production workflow, data repository, or downstream analysis pipeline.

holobiomicslab Skill

Use when when designing or integrating a file parser for mass spectrometry formats (.raw Thermo RAW format, .mzml XML-based format) in a metabolomics processing pipeline, or when reading legacy or heterogeneous instrument output where file integrity cannot be guaranteed.

holobiomicslab Skill

Use when when a deep learning pipeline processes mass spectrometry spectra through multiple independent scripts (e.g., train_rescore.py, run_fiddle.py, test_caffeine.py) and a specific feature must be removed or masked to prevent the model from learning directly from a protected input (e.