# Dask

Parallel/distributed computing.

- Repository: https://github.com/foryourhealth111-pixel/Vibe-Skills (3789 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill dask -g -a claude-code -y`
Codex: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill dask -g -a codex -y`
Cursor: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill dask -g -a cursor -y`
Gemini CLI: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill dask -g -a gemini-cli -y`

## Description

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 `map_partitions`
- Performance optimization tips
- Common patterns (ETL, time series, multi-file processing)

**Quick Example**:
```python
import dask.dataframe as dd

# Read multiple files as single DataFrame
ddf = dd.read_csv('data/2024-*.csv')

The rest: https://github.com/foryourhealth111-pixel/Vibe-Skills/blob/HEAD/bundled/skills/dask/SKILL.md