Claude Skills by HolobiomicsLab

Use when you have a large indexed gzip file (igz format) with metadata encoded in the gzip header comment field, and you need to retrieve specific blocks or spectra by integer index without decompressing the entire file.

Use when when processing aligned LC-MS data across multiple samples where the computational bottleneck is repeated peak-detection algorithm calls (one per sample per m/z value). Typical scenario: >10 samples with >1000 m/z values each, where N individual find_peaks invocations dominate runtime.

Use when when you have a set of gallery or benchmark scripts (e.

Use when when evaluating a new or updated version of a data processing tool (especially asari or similar LC-MS workflows) before production deployment, or when verifying claims about scalability, memory efficiency, or throughput on specific hardware classes (e.g., ≤16 GB RAM single-core systems).

Use when when you need to compare the computational efficiency of different mass spectrometry libraries on identical data and processing pipelines, or when you want to establish baseline throughput for a library version and validate claims of performance improvement.

Use when you need to add a new local chemical structure conversion capability to MSMetaEnhancer when existing web-service converters (CTS, CIR, PubChem) are unavailable, too slow, or unsuitable for your workflow, and you have a chemical transformation (e.

Use when when setting up a new computational workflow (e.g., ENPKG) that depends on pinned versions of Python packages and system libraries, or when collaborating across machines where package availability or versions may differ.

Use when a Python library exposes functionality that depends on external packages (like sqlalchemy, pandas, or lxml) that are not required for core operations.

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.

Use when you have a user-submitted spectrum with domain-context metadata (e.

Use when converting JSON metadata and you need to populate a target field by selecting or iterating over records only when they satisfy a logical test condition (e.g., 'include this record only if a specific field has a particular value').

Use when you need to understand how a data-processing software system discriminates among multiple input types (LC, GC, IMS, MALDI) and selectively instantiates processing pipelines.

Use when when you have a trained deep learning model (e.g., PS2MS) and an evaluation dataset of compounds, and you need to assess how prediction confidence varies across structural novelty classes (e.g., training-similar vs. structurally novel NPS analogues).

Use when after executing forward inference on preprocessed mass spectrometry spectra with a deep learning model (e.g., PS²MS), when you have per-spectrum predictions with associated confidence scores or per-class probabilities.

Use when you need to set up a LipoCLEAN analysis for MS-DIAL lipid identifications and do not yet have a configuration file, or you are switching between MS-DIAL versions 4 and 5 and need version-specific parameter defaults (e.g., column names and scaling factors that differ between versions).

Use when when designing a library that needs to support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and you want to avoid reimplementing parameter validation, storage, and dispatch logic for each backend.

Use when when you have computed hierarchical clustering dendrograms on your feature matrix (microbes or metabolites) using Euclidean distance and complete linkage, and need to determine how many clusters to cut the dendrogram into.

Use when you have a generic genome-scale metabolic model (SBML format) and cross-sectional omics data (RNA-seq, intracellular metabolomics, extracellular flux measurements from bioanalyzer or similar) from multiple biological samples (cell lines, conditions).

Use when when you have (1) transcriptomics data and a metabolic network model with GPR rules to compute RAS scores; (2) constraint-based model predictions (RPS from optGpSampler or similar) quantifying how gene expression differences translate to flux differences;

Use when you have constraint-based metabolic models with integrated multi-omics constraints (transcriptomics via Reaction Activity Scores, extracellular flux ratios via YSI bioanalyzer or LC-MS, nutrient availability bounds), and you need to determine whether differential enzyme expression.

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 .

Use when after completing a multi-stage Docker build targeting a compiled runtime environment (e.g., airdpro:cli produced from a Wine + .NET Framework 4.8 + Ubuntu 22.

Use when when deploying a multi-component research application (e.g., MAGMa's four subproject services) as containerized microservices that need to communicate internally—specifically when you have identified service interdependencies (e.

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.

Use when executing containerized conversion tools (e.g., AirdPro CLI) that must read vendor-format mass spectrometry raw files from the host filesystem and write converted output (e.g., .aird files) back to a persistent host directory. This is essential when first-run Wine initialization and .

Use when when deploying containerized versions of a multi-variant application (e.g., CLI, development, Linux, and Windows flavors) and you need to verify that each built image meets documented size constraints before registry push or production release.

Use when you have mass spectrometry imaging (MSI) data with ion images that need low-dimensional representation learning for downstream tasks like co-localized ion searching or isotope discovery.

Use when when you have paired augmented ion images processed through ResNet18 encoders producing 512-dimensional representation vectors, and you need to learn meaningful low-dimensional representations without labeled data by enforcing that augmentations of the same image remain similar while.

Use when when you need to understand how a multi-instrument mass spectrometry platform (such as mzmine) selectively routes data to different processing pipelines based on declared input type (LC, GC, IMS, or MS Imaging).

Use when you have validated intermediate JSON data (conforming to the Experiment Description Specification) and need to configure how it should be converted to a supported output format (e.g., mwTab for Metabolomics Workbench submission).

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.

Use when when you have a .msp mass spectra file with incomplete or missing chemical metadata fields (SMILES, InChI, CAS number, formula, InChIKey, IUPAC name) and need to populate those fields by querying multiple external chemical identifier services and local cheminformatics tools in parallel.

Use when adding a new converter class to MSMetaEnhancer or when modifying an existing converter's __init__ method to add/remove conversions.

Use when you have pairs of augmented ion images from mass spectrometry imaging data and need to generate low-dimensional representation vectors that maximize similarity between augmentations of the same image while avoiding representation collapse.

Use when when deploying a Word2Vec-based spectral similarity model (such as Spec2Vec) on a new mass spectrometry dataset and needing to assess whether the pre-trained model's learned peak embeddings sufficiently represent the peaks in your query spectra.

Use when after applying one or more intensity drift correction strategies (Internal Standard correction, statistical drift correction, custom or weighted bracketing) within QuantyFey and before exporting the corrected intensity table for final quantification.

Use when you have paired experimental and computational predictions for the same biological property (e.

Use when after identifying structural clusters (isotopologue groups, adduct groups, and cross-assay links) and assigning features to correlation clusters via hierarchical clustering.

Use when after computing a correlation matrix (e.g., Pearson correlation across samples) on statistically significant LC-MS features, particularly when you need to inspect hierarchical dendrogram structure, validate cluster assignments from different flattening methods (constant-threshold vs..

Use when when training a regularized deep neural network for molecular property regression (e.g., retention time prediction on the METLIN SMRT dataset with 80,038+ samples), use cosine annealing warm restarts to escape plateaus and improve convergence.

Use when when you have preprocessed mass spectra (peak-filtered, metadata-cleaned) in supported formats (mzML, mzXML, msp, MGF, JSON) and need to compare all or many pairs of spectra to identify similar compounds, search spectral libraries, or build a similarity network for spectral clustering or.

Use when you have a collection of deconvolved mass spectra (in MGF or mzTab format) from GC-MS analysis and need to group them into a molecular network to identify structural relationships and enable compound annotation.

Use when when you have access to a curated dataset (such as LOTUS) with published headline statistics in a peer-reviewed article or enriched index, and you need to validate data integrity, trace reporting accuracy, or establish a reproducible baseline before downstream analysis.

Use when you have grouped unique 2D chemical structures by organism prevalence and need to confirm that the counts in each frequency bin (singleton, low-diversity, medium-diversity, high-diversity) match published or curated reference values.

Use when after identifying statistically significant features within individual LC-MS assays (e.g., via MB-VIP and permutation testing), use this skill when you have multiple parallel assays acquired in complementary ionization modes (e.

Use when when you have antiSMASH-predicted BGCs and wish to link them to metabolomic data via structure prediction, but only BGCs with sufficient structural homology to characterized reference clusters will yield reliable predictions.

Use when you have two or more feature tables in HDF5 format with detected features characterized by m/z, drift time, retention time, and intensity, and you need to match corresponding features across samples to account for systematic shifts caused by instrument variation or tuning differences.

Use when you have trained a neural network or regression model on one paired microbiome-metabolome dataset and wish to test whether it can predict metabolite abundances in an independent, externally-sourced dataset collected from different patient cohorts or study populations.

Use when you have executed batch searches against one or more domain-specific MASST tools and received multiple output files (_microbe.html, _plant.html, _tissue.html, _microbiome.html, _food.html, _matches.tsv, _library.tsv, _datasets.tsv, _count_*.

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.