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daskby K-DenseDataGitHub stars: 47.3k

Dask skill: what it does and how to install it

Scales pandas, NumPy, text and custom Python workloads beyond memory with Dask's parallel collections, schedulers and clusters.

Summary generated from the skill's documentation.

Install

$ npx skills add K-Dense-AI/scientific-agent-skills --skill dask

Run it in a terminal. If your agent is already running, start a new session so it picks the skill up.

About this skill

What it does. Guides parallel and distributed Python workflows with Dask DataFrames, Arrays, Bags and Futures. It covers lazy task graphs, chunking, scheduler selection, cluster execution, file processing, ETL, debugging and performance practices for larger-than-memory data.

When to use it. Use it to scale pandas or NumPy workloads, process many files, or distribute custom tasks across cores or machines. For single-machine out-of-core analytics, use Vaex; for in-memory speed, use Polars.

History

Repo stars

47.3kAbout +18.6k since 9 Jul 2026

Before 1 Oct 2026 the curve is estimated from public event data.

Stars are counted for the whole repository, which holds 50 skills.

Show as a table
Repo stars by day
DateRepo stars
1 Oct 202647,271
24 Sept 2026 (estimated)46,976
17 Sept 2026 (estimated)46,257
10 Sept 2026 (estimated)41,836
3 Sept 2026 (estimated)30,545
27 Aug 2026 (estimated)29,347
20 Aug 2026 (estimated)29,160
13 Aug 2026 (estimated)29,027
6 Aug 2026 (estimated)28,947
30 Jul 2026 (estimated)28,894
23 Jul 2026 (estimated)28,894
16 Jul 2026 (estimated)28,841
9 Jul 2026 (estimated)28,708

Installs

1.7k

Tracking since . A chart appears once there are 7 days of data.

Installs via skills.sh

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