Agent skill · data analytics · davila7
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 don't fit in memory.
Why this skill is useful
Provides domain-specific knowledge on processing and analyzing large datasets with Vaex, which the AI wouldn't reliably generate on its own.
What it needs
Requires vaex installed locally. About 3k tokens when loaded. Last updated 2026-08-06. 30,138 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. 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 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. Reference references/coredataframes.md for: Opening large files efficiently Converting from pandas/NumPy/Arrow Working with example datasets Understanding DataFrame structure 2. Data Processing and Manipulation Perform filtering, create virtual columns, use expressions, and aggregate data without loading everything into memory. Reference references/dataprocessing.md for: Filtering and selections Virtual columns and expressions Groupby operations and aggregations String operations and datetime handling Working with missing data 3. Performance and Optimization Leverage Vaex's lazy evaluation, caching strategies, and memory-efficient operations. Reference references/performance.md for: Understanding lazy evaluation Using delay=True for batching operations Materializing columns when needed Caching strategies Asynchronous operations 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/vaex