Data Skills

Focuses on pandas-specific rules for data manipulation, including method chaining, data selection using loc/iloc, and groupby operations.

[DevOps & Infra] Use when optimizing database queries, indexes, N+1 problems, slow queries, or analyzing query performance. Triggers on keywords like "slow query", "N+1", "index", "query optimization", "database performance", "eager loading".

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

Best practices for robust Excel data processing with Pandas and OpenPyXL

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientif

This skill should be used when the user asks to "query Chicago data", "find Chicago datasets", "get Chicago crime data", "download Chicago permits", "write a SODA query for Chicago", "search data.cityofchicago.org", or mentions Chicago city data (311, permits, licenses, inspections, crimes, etc.).

This skill should be used when the user asks to "query Cook County data", "find Cook County datasets", "get property assessments", "download parcel data", "search datacatalog.cookcountyil.gov", "get medical examiner data", "find court cases", "query State's Attorney data", or mentions Cook County government data (assessor, treasurer, courts, payroll, medical examiner, etc.).

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

This skill should be used when the user asks about ".tmuo files", "Tabular Editor user options", "TE3 preferences", "Preferences.json", "UiPreferences.json", "Layouts.json", "workspace database settings", "deployment preferences", "data source overrides", "keyboard shortcuts", "DAX editor settings", "TMDL options", or "per-model TE3 configuration". Provides guidance for understanding and configuring Tabular Editor 3 configuration files including application preferences and model-level user optio

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Single-cell analysis patterns with AnnData and Scanpy. Use when working with single-cell RNA-seq data, creating or manipulating AnnData objects, performing quality control, normalization, dimensionality reduction, clustering, or visualization of single-cell datasets.

Common patterns and workflows for Azure DevOps and database queries. Use to avoid inefficient searches and tangents.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Comprehensive data quality patterns using Great Expectations, DLT expectations, and custom validators for ensuring data reliability and trust.

Systematic exploratory data analysis process - discover patterns in unfamiliar data, identify meaningful insights, formulate specific questions for deeper investigation

Component skill for creating effective visualizations (terminal-based and image-based) in DataPeeker analysis sessions

Component skill for systematic data profiling and exploration in DataPeeker analysis sessions

SQL for data analysis with exploratory analysis, advanced aggregations, statistical functions, outlier detection, and business insights. 50+ real-world analytics queries.

BI fundamentals with metric definition, KPI calculation, dimensional modeling, dashboard optimization, and data storytelling. 40+ metric examples and calculation patterns.

Master advanced declarative visualization with HoloViews. Use this skill when creating complex multi-dimensional visualizations, composing overlays and layouts, implementing interactive streams and selection, building network or hierarchical visualizations, or exploring data with dynamic maps and faceted displays.

Master geographic and mapping visualizations with GeoViews. Use this skill when creating interactive maps, visualizing point/polygon/line geographic data, building choropleth maps, performing spatial analysis (joins, buffers, proximity), working with coordinate reference systems, or integrating tile providers and basemaps.

Master high-performance rendering for large datasets with Datashader. Use this skill when working with datasets exceeding 100M+ points, optimizing visualization performance, or implementing efficient rendering strategies with rasterization and colormapping techniques.

Use SAP-RPT-1-OSS open source tabular foundation model for predictive analytics on SAP business data. Handles classification and regression tasks including customer churn prediction, delivery delay forecasting, payment default risk, demand planning, and financial anomaly detection. Use when asked to predict, forecast, classify, or analyze patterns in SAP tabular data exports (CSV/DataFrame). Runs locally via Hugging Face model.

Transform data sources to Nixtla schema (unique_id, ds, y) with column inference. Use when preparing data for forecasting. Trigger with 'map to Nixtla schema' or 'transform data'.

Excel spreadsheet import and export for schedules, coverage matrices, and compliance reports. Use when importing schedule data from Excel or generating Excel files for faculty/admin.

Detects anomalies in time series data using TimeGPT. Identifies outliers, level shifts, and trend breaks without model training. Use when identifying anomalies, outliers, or unusual patterns in time series. Trigger with "detect anomalies", "find outliers", "anomaly detection".

Generate benchmarking pipelines to compare forecasting models and summarize accuracy/speed trade-offs. Use when evaluating TimeGPT vs StatsForecast/MLForecast/NeuralForecast on a dataset. Trigger with \"benchmark models\", \"compare TimeGPT vs StatsForecast\", or \"model selection\".

Comprehensive data profiling and quality assessment using Teradata ClearScape Analytics descriptive statistics functions

K-means clustering for customer segmentation and data grouping

Perform comprehensive exploratory data analysis on research data. Automatically analyze data structure, quality, distributions, and generate insights. Use when the user provides a dataset, asks to "explore data", "analyze this file", or needs to understand their data before formal analysis.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Factory needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas | Sử dụng khi: tạo Excel, bảng tính, công thức, phân

This skill calculates key financial ratios and metrics from financial statement data for investment analysis

Manage Google Sheets with comprehensive spreadsheet operations including reading/writing cell values, formulas, formatting, sheet management, and batch operations. Use for spreadsheet data operations, cell ranges, formulas, formatting, batch updates, and data analysis workflows. Shares OAuth token with email, calendar, contacts, drive, and docs skills.

Execute SQL queries on Danmarks Statistik data stored in DuckDB. Use when user needs specific data analysis, filtering, aggregation, or joins. Also includes table summary functionality.

Get detailed metadata for a specific Danmarks Statistik table including variables, dimensions, and structure. Use before fetching data to understand table schema.

Browse Danmarks Statistik subject hierarchy to explore available data topics and categories. Use when user wants to discover what data is available or explore DST's organizational structure.

List all Danmarks Statistik tables currently stored in DuckDB with metadata. Use when user wants to know what data is available locally or explore stored tables.