Claude Skills by HolobiomicsLab

Use when when you have a mass spectrometry data file (such as mzPeak) that has been read by two or more independent implementations (e.g., Rust, Python/pyarrow, R/arrow) and need to verify that all implementations produce identical spectrum metadata, data types, row counts, and numerical values.

Use when when you have thousands to millions of high-resolution tandem MS/MS spectra (in mzML, mzXML, or MGF format) that need to be clustered or compared, and exhaustive pairwise distance computation is computationally prohibitive.

Use when when you have a large MsBackend object and need to (1) select a contiguous or non-contiguous range of spectra for focused analysis, or (2) combine spectra from multiple independently-loaded backends (e.

Use when after successfully constructing a nearest neighbor index from hashed spectrum feature vectors and before performing density-based clustering or similarity searches.

Use when you have millions of MS/MS spectra represented as low-dimensional vectors (via feature hashing) and need to compute pairwise distances only between similar spectra rather than comparing every spectrum to every other spectrum.

Use when when you have a processed or annotated MsmsSpectrum object (from USI loading or direct instantiation) and need to generate a figure showing observed peaks, their intensities, and assigned fragment ions (e.g., b/y ions) for publication or presentation.

Use when you have mass spectrometry data stored in a SQLite database indexed by spectrum ID and need to retrieve specific spectra by ID (random access via __getitem__) or iterate through all spectra sequentially (via read method).

Use when after training a multi-component neural network architecture (e.

Use when when you have prediction scores (softmax probabilities, uncertainties) from a trained deep learning model evaluated on a heterogeneous dataset and you need to determine whether prediction confidence or accuracy varies systematically across structurally distinct or novel compound.

Use when after computing aggregate statistics (mean, median, standard deviation, frequency distributions, similarity coefficients) over a large dataset of molecular fingerprints or feature vectors, when you need to verify that computed metrics exhibit expected distributional shapes and to identify.

Use when you have computed raw or standardised correlation scores (or other link-ranking metrics) for all possible GCF-MF pairs in a dataset and want to verify that validated links (those with known strain co-occurrence or experimental confirmation) are significantly enriched at higher score values.

Use when when you have run the same mass spectrum through molecular formula assignment under different parameter settings (e.

Use when you have sampled flux distributions from two or more constraint-based metabolic models representing different biological conditions (e.

Use when when you have a table of execution times or performance metrics indexed by two or more categorical dimensions (e.g., plot_type × backend, or sample × condition), and you need to compare central tendency and spread across groups to rank or benchmark alternatives.

Use when when a project README or documentation embeds badge endpoints that report real-time status (e.g., Travis CI build, Landscape.

Use when training a deeply regularized deep neural network on a large molecular feature dataset (e.

Use when you have genomic clusters (GCFs) and metabolomic features (MFs) from paired microbial datasets, each with strain membership information, and you need to score potential links between them.

Use when you have computed raw strain correlation scores (based on shared strain membership) between genomic and metabolomic objects of heterogeneous sizes, and you need to compare link quality fairly across pairs with different numbers of strains.

Use when when you need to aggregate values from multiple records in a JSON input document into a single concatenated string field (e.

Use when after you have identified statistically significant LC-MS features and run MamsiStructSearch to generate structural clusters (isotopologue groups, adduct groups, cross-assay links) and computed correlation cluster assignments.

Use when you have trained or loaded a deep learning model that produces high-dimensional spectral embeddings (e.g., 200-dimensional vectors from MS2DeepScore base network) and need to validate that these embeddings capture chemical structure information rather than learning spurious patterns.

Use when you have a set of compounds (e.g., novel NPS analogues in an evaluation dataset) and need to classify them as structurally similar to or divergent from a reference set (e.g., training compounds).

Use when you have a spectral library with structural ground truth (InChIKey or SMILES annotations for ≥50% of spectra) and want to benchmark whether a new or existing spectral similarity scorer ranks structurally related compounds higher than unrelated ones.

Use when when you have a set of query chemical compounds (by name or SMILES) and a reference library organized into named groups (e.

Use when when you have loaded a structure-organism pairs table from a natural products database (e.g., LOTUS) and need to answer questions about the distribution of chemical diversity—specifically, how many unique 2D structures appear in exactly 1 organism versus many organisms.

Use when when you have downloaded a curated structure-organism dataset (such as LOTUS) in TSV or CSV format with separate 2D and 3D structure-organism pair tables, and need to produce authoritative headline counts of unique referenced pairs, unique curated structures, unique organisms, and source.

Use when when a scientific software repository documents multiple standalone tools, web applications, or resources with associated metadata (URLs, publications, taxonomic coverage) in its README, and you need to create a machine-readable inventory for downstream indexing, validation, or reuse.

Use when you have generated or received a mass spectrometry data file in a structured format (e.g., mzPeak, Parquet-based archive) and need to verify it conforms to the published specification before use in analysis pipelines, sharing with collaborators, or publishing.

Use when when you have deposited a collection of JSON project documents in a platform or repository and need to verify that all conform to a published JSON Schema specification before publication, distribution, or integration with downstream systems.

Use when when you need to understand the modular composition of a multi-component research software project, particularly before onboarding, refactoring, or deploying it.

Use when when executing a multi-converter annotation workflow on mass spectra metadata (.

Use when after retrieving a JSON or tabular response from a web service endpoint (such as CANOPUS), validate the result before parsing or integrating it into your analysis pipeline.

Use when when you have MS/MS spectra with assigned precursor formulas and need to annotate fragment peaks with their chemical subformulas, but want to avoid the computational overhead of generating full SIRIUS fragmentation trees or do not have access to spectrum databases.

Use when you have created a GNPS molecular network (classical or feature-based workflow) and separately run an MS2LDA experiment on the corresponding MGF file, and you want to associate each network node with its constituent substructural motifs and visualize which motifs are shared between.

Use when you have created a GNPS molecular network (either classical or feature-based) and have computed MS2LDA motif assignments (probability and overlap scores) for the same spectra.

Use when when you have imported a tab-delimited metabolomics file (via readData or similar) containing columns for compound identifiers, sample/aliquot names, peak areas (primary assay), internal standard areas (secondary assay), and sample type classifications, and you need to organize these into.

Use when when you need to quantify and compare the effect of multiple filtering thresholds (e.g., Match.Factor ≥65, ≥80, ≥90) on the size of a retained compound set.

Use when you have completed batch spectral searches against multiple domain-specific MASST tools (via Fast Search API or individual domain searches) and need to combine and visualize the aggregated match results in a format compatible with metadataMASST web interface or downstream analysis.

Use when you have a domain-specific language (DSL) grammar specification and raw query strings that must be converted into structured intermediate representations for validation, transformation, or execution.

Use when a Shiny application or similar cross-platform tool is restricted to a single operating system (e.

Use when when you have raw metabolomics measurements in tab-delimited text format (e.g., from Sciex OS exports) and need to load them into R for quality control analysis.

Use when you have multiple independent implementations of the same data format reader (e.g., Rust, Python, R versions) and need to verify they produce identical or equivalent output.

Use when you have raw tabular experimental data (CSV or Excel) with column headers annotated using MESSES tag syntax (#<table_name>.id for record identifiers, #.

Use when you have extracted tabular data (e.g., protocol descriptions, sample preparation steps) into an intermediate JSON representation and need to subset records by type or property (e.g., test='type=sample_prep'), then order them consistently (e.

Use when you have an unknown MS/MS spectrum (tandem mass spectrum) with a measured precursor m/z and fragment peaks, and you need to assign the most likely molecular formula and ionization adduct (e.g., [M+H]+, [M+Na]+, [M+K]+).

Use when you have centroided LC-MS/MS spectra (in MGF, mzXML, mzML, or mzData format) and wish to identify peptidic natural products or ribosomally synthesized and post-translationally modified peptides (RiPPs) against a known structure database or custom RiPP structure database.

Use when you have a new fragmentation acquisition strategy (e.g., a weighted exclusion variant, alternative TopN ranking, or dynamic isolation window rule) that you wish to evaluate without access to real mass spectrometry hardware.

Use when you have a large collection of tandem mass spectra (mzML, mzXML, or MGF format) and want to group similar spectra into clusters to identify redundancy, discover novel peptides or metabolites, or prepare data for downstream annotation.

Use when you have aligned features characterized across multiple dimensions (m/z, drift time, retention time) and need to: (1) resolve MS/MS spectra that may contain fragments from multiple co-eluting or co-mobilizing precursors; (2) identify and validate isotopic signatures (e.

Use when you have acquired raw MS/MS spectra (in MGF or mzML format) from a mass spectrometry instrument or public repository (e.g., MassIVE, MetaboLights, GNPS) that will be used for de novo chemical formula ranking or adduct assignment.