DevOps & Infra

holobiomicslab Skill

Use when when deploying a Shiny application with mixed R and Python dependencies (e.g., pmartR backend with Kaleido for plot export) to production or CI/CD pipelines, and when some dependencies are under active development alongside the application.

holobiomicslab Skill

Use when you have raw mass spectrometry data files from a mass spectrometer instrument and need to feed them into a peptide feature detection pipeline (e.g., IsoFusion) that accepts only MS1 format input.

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 after imputing missing values and before assigning Cluster_IDs in the notame preprocessing pipeline.

holobiomicslab Skill

Use when at the start of an untargeted LC-MS annotation pipeline when you have a KEGG database with exact masses and need to prepare a mass-matching reference.

holobiomicslab Skill

Use when after theoretical spectra have been generated for lipid–adduct combinations with enumerated fragment masses and intensities, and you need to deploy them for downstream MS/MS analysis on Orbitrap (DDA via Excalibur) or SkyLine (targeted/PRM) workflows.

holobiomicslab Skill

Use when you have prepared metabolomics input files (feature quantification table, MS/MS spectra in MGF format, sample metadata) and are about to execute the TIMA taxonomically informed annotation pipeline, or after pipeline execution to verify all outputs were generated correctly.

holobiomicslab Skill

Use when use this skill during pipeline installation or initialization when you need to locate required external binaries (e.g., bowtie2, samtools, R, Python) but the user has not explicitly provided their installation paths in a configuration file.

holobiomicslab Skill

Use when when a software tool is distributed as a Docker image and you need to confirm it is available on a registry (e.g., Docker Hub), that the image pulls without corruption, and that the application's entry point (help command, version output, or interactive shell) is accessible and responsive.

holobiomicslab Skill

Use when when a research software project is decomposed into distinct subproject components (e.

holobiomicslab Skill

Use when when you have deployed NP Classifier using Docker Compose and need to confirm that both the server and TensorFlow Serving containers are running and healthy before sending SMILES strings to the /classify endpoint.

holobiomicslab Skill

Use when when beginning an LC-MS/MS metabolomics analysis pipeline and you have preprocessed xcms result objects (XcmsExperiment or legacy xcmsSet) that need to be loaded into memory, validated for integrity, and prepared for feature grouping (e.

holobiomicslab Skill

Use when a scientific Python package is being moved to a new GitHub organization with different structural conventions (e.g., from a personal lab account to a community-led organization like metabolomics-cloud or scverse).

holobiomicslab Skill

Use when when enriching mass spectra metadata by querying multiple external web services (CIR, CTS, PubChem, IDSM, BridgeDb) asynchronously, and you need to avoid API rate-limit violations, gracefully skip missing or malformed responses, and maintain pipeline stability without losing partial.

holobiomicslab Skill

Use when you are building a Qt5-based desktop application with cross-platform (macOS/Windows/Linux) distribution requirements and need to automate Windows executable generation as part of a CI/CD pipeline.

holobiomicslab Skill

Use when when you need to audit whether Python source files in a repository conform to black formatting standards before accepting contributions, merging code, or running CI/CD pipelines.

holobiomicslab Skill

Use when you have a rule-based reaction prediction system (like Pickaxe) with a large rule set (e.g., ~1221 MetaCyc generalized rules), and you need to understand the scaling relationship between rule count and reaction coverage to decide how many rules to deploy for a specific application.

holobiomicslab Skill

Use when you have a curated dataset of ≤10,000 molecular structures with known collision cross section values for training, a target set of ≤10,000 molecules requiring CCS predictions, a compatible browser, and either lack local Python installation or prefer cloud-based execution to avoid.

holobiomicslab Skill

Use when you need to deploy a containerized web application (such as MetFrag webapp on Tomcat) and make it accessible at a specific HTTP endpoint on the host machine. Use this skill when you have a Docker image with an internal service listening on a known port (e.

holobiomicslab Skill

Use when when you have raw MS/MS mass spectrometry data and need to submit it to the Mass2SMILES Docker inference container for structure and functional group prediction.

holobiomicslab Skill

Use when when implementing or auditing a deep learning pipeline for MS/MS-based molecular formula prediction, verify that precursor m/z values in the input feature array are zeroed before they reach the spectrum encoder (e.g., TCN).

holobiomicslab Skill

Use when building a multi-source metadata annotation pipeline where converters are organized as dynamically discoverable subclasses in separate packages (e.g., MSMetaEnhancer.libs.converters.web and MSMetaEnhancer.libs.converters.

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.

holobiomicslab Skill

Use when you need to validate that Docker image builds for multiple deployment variants (e.g., cli, dev, linux, windows) meet documented compressed size ranges, or when you must audit storage footprint across a multi-target build pipeline without pre-computed metrics.

holobiomicslab Skill

Use when after generating a dashboard_data.json file from the msFeaST Jupyter pipeline, use this skill to verify that the JSON file is correctly formatted and completely loaded into the interactive dashboard before conducting visual exploration or sharing the dashboard with collaborators.

holobiomicslab Skill

Use when when setting up imzML Writer for the first time on a new machine, or when raw vendor mass spectrometry file conversion fails with 'msconvert not found' or Docker image unavailable errors. Specifically: on Windows/PC systems before invoking RAW_to_mzML conversion;

holobiomicslab Skill

Use when when you have LC-MS raw data and need to process it through a feature detection and quantification pipeline in KNIME, but lack a structured mapping between sample identifiers, experimental conditions, and the raw LC-MS runs.

holobiomicslab Skill

Use when use this skill at the start of any mass spectrometry analysis pipeline when you have raw spectral data in mzML, mzXML, msp, MGF, JSON, or metabolomics-USI format and need to load it into a Python environment for preprocessing, cleaning, filtering, or similarity comparison.

holobiomicslab Skill

Use when you have raw MS/MS spectra in MGF or other standard formats that need to be ingested into a machine learning pipeline for cross-modal matching against molecular structures, or when spectra from different collision energy levels or instruments require standardization before comparative.

holobiomicslab Skill

Use when when standard conversion directives (headers, collate, fields_to_headers, exclusion_headers, values_to_str, sort_by, test) cannot express the required transformation logic, or when domain-specific aggregation, conditional logic, or value derivation must be applied to records after field.

holobiomicslab Skill

Use when when you have a Streamlit workflow application that must support both offline (local machine, Windows installer) and online (Docker, cloud) deployment modes, and you need to decide at runtime whether to queue jobs remotely via Redis or spawn local processes.

holobiomicslab Skill

Use when after running k-fold repeated cross-validation on a development set (via runCrossValidation with parameters k, repNum, and a subset of algorithms) and you need to select which trained classifier and hyperparameter combination to deploy.

tomevault-io Bundle

Create secure CI/CD workflows with GitHub Actions for Java 26/Maven/PostgreSQL builds, security scans, and deployments Use when this capability is needed.

holobiomicslab Skill

Use when you have one or more vendor mass spectrometry raw files (Thermo .raw, Agilent .d, Sciex .wiff2, or other MSConvert-supported formats) that must be converted to Aird format for batch processing, cloud deployment, or integration with downstream analysis tools.

holobiomicslab Skill

Use when when deploying a complex bioinformatics pipeline (like HiC-Pro) across heterogeneous computing environments where required tools (bowtie2, samtools, R, Python) may be installed in non-standard locations, differ in version, or require scheduler-specific configuration (TORQUE, SGE, SLURM.

holobiomicslab Skill

Use when when a repository displays a GitHub Actions workflow badge (e.