Agent skill · data analytics · aiskillstore
dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Why this skill is useful
Provides domain-specific knowledge for parallel processing and distributed computing with Dask that the AI wouldn't reliably generate on its own.
What it needs
Requires dask installed locally. About 8k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Dask Overview Dask is a Python library for parallel and distributed computing that enables three critical capabilities: Larger-than-memory execution on single machines for data exceeding available RAM Parallel processing for improved computational speed across multiple cores Distributed computation supporting terabyte-scale datasets across multiple machines Dask scales from laptops (processing ~100 GiB) to clusters (processing ~100 TiB) while maintaining familiar Python APIs. When to Use This Skill This skill should be used when: Process datasets that exceed available RAM Scale pandas or NumPy operations to larger datasets Parallelize computations for performance improvements Process multiple files efficiently (CSVs, Parquet, JSON, text logs) Build custom parallel workflows with task dependencies Distribute workloads across multiple cores or machines Core Capabilities Dask provides five main components, each suited to different use cases: 1. DataFrames - Parallel Pandas Operations Purpose: Scale pandas operations to larger datasets through parallel processing. When to Use: Tabular data exceeds available RAM Need to process multiple CSV/Parquet files together Pandas operations are slow and need parallelization Scaling from pandas prototype to production Reference Documentation: For comprehensive guidance on Dask DataFrames, refer to references/dataframes.md which includes: Reading data (single files, multiple files, glob patterns) Common operations (filtering, groupby, joins, aggregations) Custom operations with mappartitions Performance optimization tips Common patterns (ETL, time series, multi-file processing) Quick Example: Key Points: Operations are lazy (build task graph) until .compute() called Use mappartitions for efficient custom operations Convert to DataFrame early when working with structured data from other sources 2. Arrays - Parallel NumPy Operations Purpose: Extend NumPy capabilities to datasets larger than memory using blocked algorithms. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/dask--fa040c