Claude Skills by HolobiomicsLab

Use when after retention-time-based and abundance-correlation-based feature grouping have produced composite feature groups, and you need to identify which features within a group actually represent different compounds.

Use when running targeted peak detection on LC-MS data acquired with multiple overlapping m/z scan windows and observing distorted or periodically discontinuous peak profiles in EIC plots.

Use when you have GC-MS data with multiple replicate injections or samples, need to identify a predefined set of query chemicals by name, and want to consolidate all instances of those chemicals (which may appear with varying match factors or peak areas across different samples or injection.

Use when you have a large mass spectrometry dataset stored across multiple mzML, mzXML, or CDF files and need to perform operations (e.g., normalization, filtering, feature extraction) on the full dataset but memory constraints prevent loading all peak data simultaneously.

Use when you need to verify that a GitHub Actions workflow (such as a Build and Publish pipeline) executes successfully on a specific branch (e.g., release branch) and produces a passing build status.

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.

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

Use when when a Python package is being relocated to a new GitHub organization (e.

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.

Use when you need to verify that a GitHub Actions workflow (such as dev_build_release.yml) successfully completes end-to-end, especially after code changes or to confirm that automated build infrastructure is functioning correctly.

Use when you have configured a GitHub Actions workflow that executes build, test, and quality checks, and you want to embed a machine-readable, auto-updating badge in your repository README to signal pipeline status at a glance.

Use when you have a GitHub repository with published CI workflow badges (e.g., unit test or package test badges in the README) and need to independently verify that the workflows execute successfully, reproduce the pass/fail status, and collect structured test results.

Use when after generating cross-spectrum negative examples via precursor m/z windowing and before training a rescore model (e.g., Siamese architecture in FIDDLE v2.0.0).

Use when preparing training batches for a neural network classifier on LCMS peak data where class counts are unequal (e.g., more high-quality peaks than low-quality peaks).

Use when when you have multi-omic TWIM-MS data (raw or processed arrival-time records) and have already assigned features or detected ion features to biomolecular classes (e.

Use when after identifying putative BGC-encoded precursor peptides from a genome assembly via MetaMiner's BGC identifier, when preparing a RiPP structure database for downstream spectral matching via Dereplicator.

Use when you have a feature table with assigned biomolecular class labels (e.g., from preceding class assignment step) and raw ion mobility arrival time measurements from TWIM-MS data, and you need to compute class-appropriate CCS values for downstream multi-omic analysis.

Use when after fitting and optimizing a MB-PLS model on training data, apply this skill to the held-out test set (typically 10% of the original sample) to obtain unbiased performance estimates.

Use when you have a CSV or Excel file containing chemical structure descriptors for one or more molecules, and you want to obtain binary bitter/not-bitter predictions for each molecule using the BitterPredict classifier.

Use when when you have vendor mass spectrometry raw files (e.g., .raw format) that must be converted to an open format (Aird) using a Windows .

Use when you have applied two different clustering or dendrogram-flattening methods (e.g., constant-threshold vs. silhouette-score optimization) to the same feature set and need to assess whether the two solutions assign features to clusters consistently.

Use when after a GitHub Actions CI workflow has executed static analysis (e.g., via Sonarcloud) and generated a quality report.

Use when you have a published research article describing a new FT-ICR MS analysis tool and need to verify which analytical and visualization features are actually implemented (not just claimed).

Use when analyzing complex GC-MS mixtures where two or more chemical compounds elute at similar or identical retention times, producing overlapping or merged peaks in the raw chromatogram.

Use when when you have two independent predictions of categorical outcomes (up/down/no-change variation signs) across multiple sample pairs and need to measure agreement beyond what would be expected by chance.

Use when when you have preprocessed mass spectrometry imaging (MSI) ion images and need to generate augmented image pairs for contrastive learning in co-localized ion discovery tasks.

Use when you have ion mobility-mass spectrometry lipidomics data from samples spiked with U¹³C-labeled lipid internal standards (fully labeled yeast extract) and want to assess whether measured CCS values systematically deviate from a validated DT CCS N₂ reference library, indicating bias that may.

Use when you have raw or processed arrival-time data from a traveling-wave ion mobility mass spectrometry (TWIM-MS) platform and need to convert it into standardized collision cross section (CCS) values for comparative analysis across samples or datasets.

Use when when you have acquired ion mobility–mass spectrometry data (drift time and m/z dimensions) and need to convert observed drift times into calibrated CCS values.

Use when you have IM-MS lipidomics data from samples spiked with U13C-labeled internal standards (fully labeled yeast extract) and you need to quantify whether measured CCS values deviate systematically from theoretical values, or when you want to correct CCS measurements before downstream lipid.

Use when you have a pre-trained GNN CCS prediction model and need to assess its predictive performance and cross-dataset generalizability. Use it specifically when evaluating whether models trained on one CCS database (e.

Use when when rendering a treemap of qc_summary() output showing ion counts and percentages by filter status (passed/failed), and you need a perceptually uniform, colorblind-friendly palette that clearly distinguishes filter categories while using sequential intensity to reinforce the magnitude of.

Use when you have two or more independent, standardised scoring functions that rank the same set of candidate pairs (e.

Use when when you have peak-abundance .csv files and assigned molecular formula data from FT-ICR MS preprocessing, and you need to verify that a published pipeline's runtime claims (e.g., '<1 min for 40 samples', '~2 min for 120 samples') hold true on your own datasets or reference datasets.

Use when you have tandem MS/MS spectrum data in standard peak file formats (mzML, mzXML, or MGF) and need to cluster spectra based on precursor mass and fragment ion similarity.

Use when you have an existing mass spectrometry data file in a vendor or standard format (mzML, NetCDF, etc.) and need to convert it to mzPeak format for downstream analysis, visualization, or archival.

Use when you have consensus metabolic reconstructions for multiple community members (e.g., plant-associated microbes or plant-microbial consortia) and those individual models contain incomplete or disconnected metabolic pathways.

Use when you have draft metabolic reconstructions (in SBML or equivalent format) for multiple organisms sampled from a single ecological community (e.

Use when you are selecting a pathway enrichment method for metabolomics peak data and need to assess which method will remain stable when your data contains noise, dropout, or missing identifications.

Use when you have developed a new machine learning model for predicting metabolomic profiles from microbiome data and need to quantify its performance improvement over existing methods.

Use when after mass tracks have been aligned across all samples into a MassGrid (via sample-wise or centroid-based alignment), you have a unified set of m/z features tracked across the entire study.

Use when when processing a multi-sample LC-MS metabolomics project after mass-track extraction and retention-time calibration have been applied to all individual samples, and you need to detect peaks across the entire cohort.

Use when apply CLR transformation when you have microbiome or metabolomic count data that sums to a constant across samples (relative abundance or compositional data) and intend to train supervised or unsupervised machine learning models (especially neural networks) that assume unbounded, linear.

Use when after submitting a fingerprint or spectrum query to the CANOPUS web service and receiving a structured response.

Use when you have a GC-MS dataset with a Match.Factor column (or equivalent quality metric) and need to evaluate how many unique compounds are retained at different confidence thresholds, or when you must subset the compound list to a user-defined quality level before downstream cheminformatics.

Use when you have raw GC-MS or LC-MS data in vendor format (NetCDF, .raw, .d) or generic mass lists, and you need to assign chemical identities to detected peaks.

Use when apply this filter after batch correction of compound/internal-standard ratios when you have pooled study quality control (SQC) samples and need to determine which compounds are sufficiently reproducible for downstream reporting. Use it specifically when the nonReportableRSD threshold (e.

Use when you have raw peak area or intensity measurements for both compounds and their corresponding internal standards across all study samples (including QC and calibration samples), and you need to normalize for instrument variability and injection efficiency before batch correction or quality.

Use when you have a set of query chemicals and a reference library (organized by type or group), and you need to determine which reference compounds most closely resemble each query chemical based on structural features.

Use when you have a collection of preprocessed MS/MS spectra with structural annotations (InChIKey, SMILES, or InChI) and need to identify pairs of compounds that are structurally related above a specified similarity threshold.