Agent skill · data analytics · k-dense-ai
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 on distributed computing patterns and integration with pandas/NumPy workflows that the AI wouldn't reliably generate on its own.
What it needs
Requires dask, gcsfs, pyarrow, s3fs installed locally. About 9k tokens when loaded. Last updated 2026-08-03. 32,849 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. Current upstream: dask 2026.3.0 (PyPI, March 2026). Docs: docs.dask.org. Since 2025.1.0, the expression-based DataFrame API with query planning is the only implementation — do not install dask-expr separately or set dataframe.query-planning: False. Quick Start Installation For a typical pandas/NumPy workflow with the distributed scheduler and dashboard: Remote object storage (S3, GCS, Azure): Requires Python 3.10+ (3.9 support dropped in 2024.12). DataFrame I/O requires PyArrow 16+ (as of dask 2026.1.2). 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/dask