Agent skill · data analytics · k-dense-ai
vaex
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.
Why this skill is useful
Provides domain-specific knowledge for processing and analyzing large datasets efficiently with Vaex, which the AI wouldn't reliably generate on its own.
What it needs
Requires vaex installed locally. About 5k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
Vaex Overview Vaex is a high-performance Python library designed for lazy, out-of-core DataFrames to process and visualize tabular datasets that are too large to fit into RAM. Vaex can process over a billion rows per second, enabling interactive data exploration and analysis on datasets with billions of rows. Installation Install the full meta-package (recommended): Minimal install (pick only what you need): The vaex package is a meta-package that pulls in vaex-core, vaex-viz, vaex-hdf5, vaex-ml, and other sub-packages. Arrow support is built into vaex-core (the separate vaex-arrow package is deprecated). vaex-distributed is deprecated in favor of vaex-enterprise. Version notes (vaex 4.19.0+): Python 3.12 and NumPy v2 require vaex >= 4.19.0. On Windows, you may need Python dev headers to build the annoy dependency. When to Use This Skill Use Vaex when: Processing tabular datasets larger than available RAM (gigabytes to terabytes) Performing fast statistical aggregations on massive datasets Creating visualizations and heatmaps of large datasets Building machine learning pipelines on big data Converting between data formats (CSV, HDF5, Arrow, Parquet) Needing lazy evaluation and virtual columns to avoid memory overhead Working with astronomical data, financial time series, or other large-scale scientific datasets Vaex vs alternatives: Use polars when data fits in RAM and you need maximum in-memory speed. Use dask when you need distributed pandas/NumPy across a cluster. Use vaex for single-machine, out-of-core analytics on tabular data that exceeds RAM via memory-mapped HDF5/Arrow files. Core Capabilities Vaex provides six primary capability areas, each documented in detail in the references directory: 1. DataFrames and Data Loading Load and create Vaex DataFrames from various sources including files (HDF5, CSV, Arrow, Parquet), pandas DataFrames, NumPy arrays, and dictionaries. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/vaex