DevOps & Infra

holobiomicslab Skill

Use when after installing R or modifying an R environment via conda, package managers, or container images; before running any pipeline step that depends on R packages for statistical analysis, visualization, or data normalization.

holobiomicslab Skill

Use when you have raw molecular structures in SMILES or SDF format and need to prepare molecular descriptors as input to a descriptor-based classifier (e.g., BitterPredict.m).

holobiomicslab Skill

Use when designing a contrastive learning pipeline for ion images or other data modalities where you need to process multiple augmented versions of the same input through an encoder and enforce similarity between the resulting representations.

holobiomicslab Skill

Use when after implementing a lossless compression–decompression cycle on mzML or mzXML mass spectrometry files, or when validating that a lossy compression pipeline meets acceptable error thresholds.

holobiomicslab Skill

Use when when implementing a new feature or bug fix in a shared repository (such as Maven GUI metabolomics analysis software) where multiple contributors work in parallel and code quality is enforced via CI/CD pipelines (Travis, Appveyor).

holobiomicslab Skill

Use when when implementing a metadata annotation pipeline for mass spectra that fetches values from multiple external services (e.

holobiomicslab Skill

Use when after building and starting a Dockerized server via docker-compose orchestration, particularly when deploying machine learning inference services that depend on pre-trained models behind an API gateway.

holobiomicslab Skill

Use when when you have metabolomics datasets (loaded as SummarizedExperiment objects) and need to apply a sequence of data-processing and analytical steps (e.

holobiomicslab Skill

Use when when you have SMILES strings as input to a molecular machine learning pipeline (e.g., retention time prediction, spectral prediction) and need to convert them into structured molecular representations before calculating descriptors or constructing molecular graphs.

holobiomicslab Skill

Use when executing a multi-stage deep learning pipeline (pretraining → fine-tuning → alignment) where each stage loads a checkpoint from the previous stage and you need to confirm that loss is decreasing monotonically within each stage, that checkpoints are being saved, and that no training.

holobiomicslab Skill

Use when you have converted mass spectrometry data in mzXML or mzML format and need to extract metabolic features via region-of-interest (ROI) search followed by preprocessing and augmentation.

holobiomicslab Skill

Use when you have vendor raw mass spectrometry data files (e.g., .raw, .d, .wiff2 formats) that must be converted to mzML or Aird format, and you need to automate the conversion in a batch workflow, Docker container, or non-interactive environment.

holobiomicslab Skill

Use when at the start of any DaDIA pipeline execution, or whenever you are preparing to run a complex multi-package R workflow on a new system or after updating package managers.

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 when converting raw metabolomics data from external formats (tab-delimited text, Sciex OS exports) into a unified R analysis environment, or when you have an existing SummarizedExperiment from another pipeline (e.

holobiomicslab Skill

Use when when setting up a bioinformatics pipeline (particularly Hi-C data processing) that depends on multiple external binaries with version constraints, and you need to configure the environment in a way that is both portable across systems and reproducible across runs.

holobiomicslab Skill

Use when when setting up a new conda environment for a Python-based bioinformatics pipeline and you need to confirm that all declared dependencies (e.g., pysam >=0.15.4, bx-python >=0.8.8, numpy >=1.18.1, scipy >=1.4.

holobiomicslab Skill

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.

holobiomicslab Skill

Use when when you have a Streamlit-based scientific application (e.g. OpenMS workflows) that must support both online Docker deployments with multiple worker processes and offline local deployments, and you need to prevent long-running analyses from blocking the web UI thread.

holobiomicslab Skill

Use when you have raw MS files (with associated metadata: filename, upload path, file format) staged in an uploader repository and need to route them to a proteomics processor for analysis while preserving audit trails and ensuring format compatibility before processing begins.

holobiomicslab Skill

Use when when deploying the ipbhalle/metfragweb Docker container and you need to confirm that custom JVM heap sizes (specified via JAVA_OPTS=-Xmx?g -Xms?g) have taken effect, or when troubleshooting memory-related runtime issues in the running container.

holobiomicslab Skill

Use when when setting up a multi-language data analysis pipeline that requires both R and Python components to coexist with exact version constraints, especially when deep learning (keras) and machine learning (caret, pROC) packages must share a common Python backend.

holobiomicslab Skill

Use when you have deployed a TensorFlow Serving instance (via docker-compose or equivalent) and need to verify that the model's input layer names ('input_2048' and 'input_4096') and output layer name ('output') match the expected schema before integrating the model into a classification pipeline or.

holobiomicslab Skill

Use when before executing a bioinformatics pipeline that depends on multiple R packages with strict version constraints (e.g., DaDIA, which requires R ≥4.0, XCMS ≥3.11.4, and metaMS =1.25.1).

holobiomicslab Skill

Use when you have downloaded Keras-format pre-trained models (e.g., via get_models.sh) and need to deploy them locally via TensorFlow Serving within a Dockerized classification API.

holobiomicslab Skill

Use when when configuring an OpenMS Streamlit application for online deployment with Redis and RQ worker processes, you need to determine how many worker processes can safely run in parallel within a Docker container's memory constraint.

holobiomicslab Skill

Use when when building a mass spectrometry data import pipeline that must ingest spectra from multiple file formats and produce a unified, queryable representation suitable for metadata validation, peak filtering, and similarity comparisons.

holobiomicslab Skill

Use when when you have acquired Keras models (via get_models.sh or similar download) and need to deploy them locally through TensorFlow Serving within a Docker/docker-compose stack.

holobiomicslab Skill

Use when when submitting OpenMS workflows to RQ (Redis Queue) in online Docker mode, you need to preserve the job ID and execution context for recovery after container restarts or worker failures. Apply this skill if: (1) you are using QueueManager.

holobiomicslab Skill

Use when you have a Nextflow workflow (e.g., Nextflow4MS-DIAL) that currently runs under Docker or bare metal, and you need to execute it on an HPC cluster that lacks Docker support or enforces Singularity as the container runtime.

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 when processing raw mass spectrometry data files of unknown or mixed provenance, and you need to automatically route IMS inputs to their corresponding analysis 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 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 your MSI data is stored in a Cardinal imaging experiment object (version 2.2+) that has already been peak-binned with peakBin(), and you want to run mass2adduct's massdiff() and adductMatch() pipeline without manually exporting to CSV;

holobiomicslab Skill

Use when when you have vendor raw mass spectrometry files on the host machine that need to be processed by a containerized tool (e.g., AirdPro), and the container must read input from and write output to specific host paths. Typical trigger: you have a .