Data & Analytics

johnalbertini14-glitch Bundle

Query Odoo data including salesperson performance, customer analytics, orders, invoices, CRM, accounting, VAT, inventory, and AR/AP. Generates WhatsApp cards, PDFs, Excel. Use when user explicitly mentions Odoo or asks for Odoo data.

holobiomicslab Skill

Use when you have a .msp format MS/MS spectrum library (e.g., from MassBank or similar public databases) and need to convert it into individual CSV entries indexed by positive or negative ionisation mode for use as a custom fragment library in MetaboAnnotatoR annotation pipelines.

holobiomicslab Skill

Use when when you have received raw MRM lipidomics export files in vendor-specific formats (TSV, CSV) with inconsistent column naming, unparsed lipid identifiers (e.g., 'PC(36:1)' as a single string), and unknown data quality issues.

holobiomicslab Skill

Use when raw MRM lipidomics data arrives in vendor-specific export formats (e.g., Sciex, Waters, Thermo TSV/CSV) with inconsistent or proprietary column naming, numeric encoding, and lipid nomenclature.

holobiomicslab Skill

Use when you have raw mzML files and a feature table (CSV) from LCMS data processed by tools like mzMine, and you need to create train/test/validation batches with specific matrix dimensions (120 × 2) and verified margin/peak signal separation before training or evaluating a neural network.

holobiomicslab Skill

Use when you have raw LC-MS data files (mzML, netCDF, or raw vendor formats) from multiple samples and need to extract, align, and quantify chromatographic features across the cohort.

holobiomicslab Skill

Use when you have raw or processed HRMS/MS data from Q-Exactive, Agilent Q-TOF, Bruker Q-TOF, or SCIEX Q-TOF instruments in formats such as mzML, CSV peaklists, or vendor-specific formats, and you need to extract experimental fragment m/z values and their intensities for comparison against.

holobiomicslab Skill

Use when immediately after loading a raw GC-MS CSV file and before executing the spreadOut() function. Use it when you have received peak table data from an instrument vendor (e.

holobiomicslab Skill

Use when you have an mzPeak file (uncompressed ZIP archive containing Parquet files) and need to extract and work with spectrum metadata (scan descriptions, precursors, selected ions), spectrum signal data (profile or centroid m/z and intensity arrays), or chromatogram data.

holobiomicslab Skill

Use when you have raw mzML files and corresponding feature tables (CSV format, mzmine-formatted) from untargeted LCMS experiments, and you need to convert them into uniformly-shaped peak matrices (2 × 120 per peak: margin + signal regions) as input for neural network classification of MS1 peak.

holobiomicslab Skill

Use when when you have raw mzML files and a corresponding feature table (CSV format, e.g., from mzmine) and need to generate peak matrices with fixed dimensions (e.g., 2 × 120) that encode margin vs. peak signal regions for training a neural network classifier to filter false positive LCMS peaks.

holobiomicslab Skill

Use when you have raw .msp spectral library files (e.g., from MassBank or custom sources) and need to convert them into a structured CSV library format for use in metabolite annotation.

holobiomicslab Skill

Use when when processing untargeted LC-MS data with SLAW and observing incomplete feature detection across the sample cohort—i.e., features present in some samples but with missing values (zeros or NAs) in others due to signal dropout, retention time drift, or mass calibration drift.

holobiomicslab Skill

Use when when you have Parquet-encoded tabular data (e.

holobiomicslab Skill

Use when you have generated one or more MemoMatrix objects (MS2 fingerprint matrices from separate sample sets) and need to combine them for cross-cohort alignment, validate structural consistency after merging, or prepare merged matrices for downstream filtering and visualization.

holobiomicslab Skill

Use when you have mass-spectrometry data in a Pandas DataFrame and need to expose plot kinds (spectrum, chromatogram, mobilogram, peakmap) as a `.plot(kind='...

holobiomicslab Skill

Use when you have loaded mzPeak spectrum or chromatogram metadata and signal data into PyArrow Table structures (via the Python mzPeak reader or equivalent) and need to persist them to disk in Parquet format for downstream analysis, interoperability with other languages (R, .

holobiomicslab Skill

Use when when you have mass spectrometry data organized in a Pandas DataFrame with m/z values, retention time (RT), and intensity measurements, and you want to visualize the joint distribution and correlation of these three dimensions to identify peaks, assess separation, and detect patterns across.

holobiomicslab Skill

Use when you have an experimental MS/MS spectrum (e.g., from MassBank or local data) and need to identify significant fragment ions above noise, assign occurrence scores to peaks, and generate a CSV-formatted library entry for use in metabolite feature annotation pipelines.

holobiomicslab Skill

Use when you have MSI intensity data exported from commercial software (MSiReader, SCiLS) or Cardinal processing pipelines as plain-text CSV files or Cardinal MSProcessedImagingExperiment/MSContinuousImagingExperiment objects, and you need to prepare it for mass-difference tabulation and adduct.

holobiomicslab Skill

Use when after compound database dereplication with SIRIUS or MetFrag has produced candidate annotations (CSV or JSON format), and you need to filter implausible structures, compute standardized molecular descriptors, and rank candidates by confidence before reporting final metabolite.

holobiomicslab Skill

Use when you have a mass-spectrometry query string written in MassQL (or similar domain-specific SQL-inspired syntax) that must be converted into structured form for execution. The input is raw, unparsed text containing SQL keywords, MS-specific operators (e.

holobiomicslab Skill

Use when when reading mzPeak files or other Parquet-backed mass spectrometry archives where spectral m/z and intensity arrays are stored in columnar layouts (point or chunked format) and you need to reconstruct them into Python NumPy arrays, pandas DataFrames, or equivalent in-memory structures for.

holobiomicslab Skill

Use when when you have annotated representative LCMS samples (raw mzML files + labeled feature tables in mzmine CSV format) and need to convert them into balanced or unbalanced peak matrix batches with fixed dimensions for neural network training.

holobiomicslab Skill

Use when you have multiple centroided .mzML LC-MS files that need to be loaded into a unified object for targeted peak integration, and you need to distinguish QC runs from sample runs to compute per-group quality metrics (e.g., average SNR, peak correlation, area under curve per QC cohort).

holobiomicslab Skill

Use when you have raw mzML files and feature tables (CSV from mzMine or XCMS) for LCMS data, have generated training/validation/test batches with known class imbalance, and need to train a CNN model from scratch to achieve AUC ROC > 0.9 for distinguishing true from false positive MS1 peaks.

holobiomicslab Skill

Use when you have experimental peaklist data (CSV or mzML-derived tables) from UHPLC-HRMS/MS instruments (Q-Exactive, Agilent/Bruker/SCIEX Q-TOF) with fragment m/z values and want to annotate them to known lipid identities.

holobiomicslab Skill

Use when you have a raw LC–MS compound metadata file (xlsx or csv) with heterogeneous column names and column order, and you need to prepare it for targeted peak detection in TARDIS.

holobiomicslab Skill

Use when when you have SQL-inspired query strings that encode domain-specific assumptions and need to validate, transform, or execute them against data repositories. Specifically: input is human-readable DSL text containing mass spectrometry-specific clauses (e.

holobiomicslab Skill

Use when you have a list of polypeptide sequences (one per line or CSV format) and need to compute average mass (weighted by natural isotope abundances) to compare against experimental LC-MS or MS/MS data where the full isotopic distribution—not just the most abundant peak—is relevant for peptide.

holobiomicslab Skill

Use when you have an m/z-resolved feature list from LC- or GC-HRMS analysis (either detected by pyOpenMS or provided as a custom Excel table) and need to prioritize potential PFAS compounds by identifying clusters of homologous structures.

holobiomicslab Skill

Use when you have access to a lipidomics library repository (e.g., LipidMatch .csv files) and need to audit or report the total number of distinct lipid species and lipid-type categories present.

holobiomicslab Skill

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.

holobiomicslab Skill

Use when when you have an existing tool or library with file-based storage (JSON, CSV, binary formats) that needs to support efficient querying across large datasets, and you want to migrate to a relational database without breaking existing functionality or performance expectations.

holobiomicslab Skill

Use when you have a raw feature table (TSV/CSV) derived from LC-MS peak detection (e.

holobiomicslab Skill

Use when you have pre-trained MSGO model weights (PFAS or lipid variant) and a set of experimental mass spectra (either from reference evaluation sets or real wastewater/environmental samples) in CSV format with m/z and intensity pairs, and you need to obtain candidate molecular structures ranked.