Data & Analytics

holobiomicslab Skill

Use when when implementing a new MsBackend subclass that stores only a subset of core spectra variables (e.

holobiomicslab Skill

Use when when you have mass spectrometry data (mzML, Bruker .d, or CSV) loaded into a Pandas DataFrame with columns for m/z, retention time, ion mobility, or intensity values, and you need to render spectrum plots, chromatograms, mobilograms, or 2D peak maps.

holobiomicslab Skill

Use when when you have a mzPeak file (uncompressed ZIP archive containing Parquet tables) and need to access decoded spectral data arrays (m/z, intensity), spectrum metadata (scan descriptors, precursors), or chromatogram data in a form suitable for Python/R analysis pipelines.

holobiomicslab Skill

Use when after loading spatial metabolomics data (from CSV, imzML, or merged positive/negative ionization modes) into an AnnData object, and before filtering metabolites or performing cross-modal integration.

holobiomicslab Skill

Use when after loading spatial metabolomics data (from CSV, imzML, or merged positive/negative mode files) into an AnnData object and before filtering or integrating with spatial transcriptomics.

holobiomicslab Skill

Use when after retention-time correction and alignment of centroided mzML or mzXML LC-MS files across a sample cohort.

holobiomicslab Skill

Use when you have executed mzExacto() on a preprocessed GC-MS dataset and need to verify that the returned dataframe correctly matches query chemicals to their m/z peaks, retention times, and quantitative measurements (area values).

holobiomicslab Skill

Use when your input is a feature intensity table (CSV or R data frame) with features as columns and samples as rows, and you have accompanying sample metadata (batch identifiers, QC/study sample labels, run order, sample phenotypes, collection dates).

holobiomicslab Skill

Use when you are building a new data representation or storage strategy for MS spectra (e.g., on-disk HDF5, SQL database, remote file access) and need to integrate it seamlessly into workflows that use the Spectra package.

holobiomicslab Skill

Use when you are creating a new backend to expose MS data (m/z, intensity, retention time, and other spectral properties) from a specific storage format or data source (e.g., mzML files, SQL databases, in-memory matrices, or spectral libraries) to Spectra objects.

holobiomicslab Skill

Use when when you have mass spectrometry data in a Pandas DataFrame with columns for m/z, retention time (or ion mobility), and intensity, and you want to generate interactive (rather than static) visualizations for exploratory analysis, interactive drill-down, or deployment in web applications or.

holobiomicslab Skill

Use when after loading and parsing raw MRM export files (TSV, CSV, or vendor-specific formats) into a pandas DataFrame and decomposing lipid nomenclature, before performing lipid matching, statistical analysis, or visualization.

holobiomicslab Skill

Use when when you have mzPeak files (Parquet-based archives in uncompressed ZIP containers) or other PyArrow-compatible columnar formats containing mass spectrometry spectra, and you need to extract and decode spectral data arrays (m/z values, intensities) into Python memory for downstream.

holobiomicslab Skill

Use when you have raw mzML files and feature tables (CSV format from mzMine or XCMS) from untargeted LCMS experiments and need to distinguish true metabolite peaks from false positives introduced by the peak-picking algorithm.

holobiomicslab Skill

Use when after feature detection and alignment (XCMS or equivalent), when you have a CSV feature table with m/z and retention time columns and need to group features derived from the same compound.

holobiomicslab Skill

Use when when uploading a delimited CSV or similar tabular file to Punc'data containing high-resolution mass spectrometry results, and the column headers are present but their semantic roles (e.

holobiomicslab Skill

Use when you have mzPeak files stored as Parquet tables within a ZIP archive and need to load spectrum metadata, chromatogram metadata, signal data (profile or centroid), or peaks into memory for analysis in R, Python, or another Arrow-supported language.

holobiomicslab Skill

Use when you have mass spectrometry data loaded into a pandas DataFrame with m/z, retention time, and intensity columns, and need to confirm that pyOpenMS-Viz can produce visualizations (spectra, chromatograms, or peak maps) using a specific plotting backend (matplotlib, Bokeh, or Plotly) on real.

holobiomicslab Skill

Use when after computing InChIKey and neighbourhood scores for library match candidates, you need to write results to a persistent format (CSV, JSON, or database) for storage, sharing, and interpretation.

holobiomicslab Skill

Use when you have preprocessed MS/MS spectral data (converted to bag-of-fragments format with neutral losses extracted and noise filtered) and seek to identify recurring fragmentation patterns indicative of molecular substructures across a spectral cohort.

holobiomicslab Skill

Use when after raw mass spectrometry data has been converted to a peak feature table (CSV or tabular format) containing mass-to-charge ratios, retention times, and intensity values across samples, and you need to reduce noise and identify which peaks show meaningful differential patterns between.

holobiomicslab Skill

Use when you have MS/MS spectral library data currently stored in file-based formats (JSON, CSV, binary, MGF, MSP) and need to migrate to database-backed storage to support fast queries by metadata filters (precursor m/z, ion mode, retention time) and computed similarity scores against query.

holobiomicslab Skill

Use when immediately after chromatographic peak detection (findChromPeaks) when you have detected peaks across multiple samples and need to identify which peaks represent the same feature across the sample cohort.

pjt222 Skill

Add PUT workflow annotations to source files using the correct language-specific comment prefix. Covers annotation syntax, skeleton generation via put_generate(), multiline annotations, .internal variables, and validation. Supports 30+ languages with automatic comment prefix detection. Use after analyzing a codebase and having an annotation plan, when adding workflow documentation to new or existing source files, or when documenting data pipelines, ETL processes, or multi-step computations.

pjt222 Skill

正しい言語固有のコメントプレフィックスを使用して、ソースファイルにPUTワークフロー アノテーションを追加する。アノテーション構文、put_generate()によるスケルトン 生成、複数行アノテーション、.internal変数、バリデーションをカバーする。 30以上の言語をサポートし、自動コメントプレフィックス検出を備える。 コードベースを分析してアノテーション計画を持った後、新規または既存のソースファイルに ワークフロードキュメントを追加する時、データパイプライン、ETLプロセス、 複数ステップの計算をドキュメント化する時に使用する。

pjt222 Skill

使用正确的语言特定注释前缀向源文件添加 PUT 工作流注解。涵盖注解语法、 通过 put_generate() 生成骨架、多行注解、.internal 变量和验证。支持 30+ 种语言的自动注释前缀检测。适用于分析代码库并有注解计划后、向新的或现有 源文件添加工作流文档时，或记录数据管道、ETL 流程或多步骤计算时。

pjt222 Skill

Perform Computer Systems Validation (CSV) assessment following GAMP 5 methodology. Covers user requirements, risk assessment, IQ/OQ/PQ planning, traceability matrix creation, and validation summary reporting. Use when new computerized system being introduced in GxP environment, when existing validated system undergoing significant change, when periodic revalidation required, or when regulatory inspection demands validation gap analysis.

pjt222 Skill

Serialize and deserialize data across common formats including JSON, XML, YAML, Protocol Buffers, MessagePack, and Apache Arrow/Parquet. Covers format selection criteria, encoding/decoding patterns, performance trade-offs, and interoperability considerations. Use when choosing a wire format for API communication, persisting structured data to disk, exchanging data between systems written in different languages, optimizing transfer size or parsing speed, or migrating from one serialization format to another.

pjt222 Skill

Realizar una evaluación de Validación de Sistemas Informatizados (CSV) siguiendo la metodología GAMP 5. Cubre requisitos de usuario, evaluación de riesgo, planificación IQ/OQ/PQ, creación de matrices de trazabilidad e informes de resumen de validación. Usar cuando se introduce un nuevo sistema informatizado en un entorno GxP, cuando un sistema validado existente experimenta un cambio significativo, cuando se requiere revalidación periódica, o cuando una inspección regulatoria exige un análisis de brechas de validación.

pjt222 Skill

Serializar y deserializar datos en formatos comunes incluyendo JSON, XML, YAML, Protocol Buffers, MessagePack y Apache Arrow/Parquet. Cubre criterios de seleccion de formato, patrones de codificacion/decodificacion, compromisos de rendimiento y consideraciones de interoperabilidad. Usar al elegir un formato de transmision para comunicacion API, persistir datos estructurados en disco, intercambiar datos entre sistemas escritos en diferentes lenguajes, optimizar tamano de transferencia o velocidad de analisis, o migrar de un formato de serializacion a otro.

pjt222 Skill

GAMP 5方法論に従ったコンピューター化システムバリデーション（CSV）評価を実施します。 ユーザー要件、リスク評価、IQ/OQ/PQ計画、トレーサビリティマトリクス作成、 バリデーション概要報告書の作成を対象とします。新しいコンピューター化システムを GxP環境に導入する際、既存のバリデート済みシステムに重大な変更が加えられる際、 定期的な再バリデーションが必要な際、または規制査察でバリデーションのギャップ分析が 求められる際に使用します。

pjt222 Skill

JSON、XML、YAML、Protocol Buffers、MessagePack、Apache Arrow/Parquetを含む 一般的なフォーマット間でデータをシリアライズ・デシリアライズする。 フォーマット選択基準、エンコード/デコードパターン、パフォーマンスの トレードオフ、相互運用性の考慮事項をカバー。API通信のワイヤーフォーマット 選択、構造化データのディスク永続化、異なる言語で書かれたシステム間の データ交換、転送サイズや解析速度の最適化、シリアライゼーション フォーマット間の移行に使用する。

pjt222 Skill

Add PUT workflow annotations to source files using the correct language-specific comment prefix. Covers annotation syntax, skeleton generation via put_generate(), multiline annotations, .internal variables, and validation. Supports 30+ languages with automatic comment prefix detection. Use after analyzing a codebase and having an annotation plan, when adding workflow documentation to new or existing source files, or when documenting data pipelines, ETL processes, or multi-step computations.

pjt222 Skill

Organize, track, and value a trading card game collection. Covers inventory methods, storage best practices, grade-based valuation, want-list management, and collection analytics for Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when starting a new collection and setting up inventory tracking, cataloging an existing collection that has grown beyond casual knowledge, valuing a collection for insurance or sale, or deciding which cards to submit for professional grading based on value potential.

pjt222 Skill

按照 GAMP 5 方法执行计算机系统验证（CSV）评估。涵盖用户需求、 风险评估、IQ/OQ/PQ 规划、追溯矩阵创建和验证摘要报告。适用于 GxP 环境中引入新计算机化系统、现有已验证系统发生重大变更、 需要定期重新验证，或法规检查要求验证差距分析时使用。

pjt222 Skill

跨常见格式进行数据序列化和反序列化，包括 JSON、XML、YAML、Protocol Buffers、 MessagePack 和 Apache Arrow/Parquet。涵盖格式选择标准、编码/解码模式、性能权衡 和互操作性考虑。适用于选择 API 通信的线路格式、将结构化数据持久化到磁盘、在不同 语言编写的系统之间交换数据、优化传输大小或解析速度，以及从一种序列化格式迁移到另一种。