Skill · em Dados, IA e pesquisa

dask

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

Procedência

Antes de instalar

7 arquivos · 74,3 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/dask/.

d=".claude/skills/dask"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/dask"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/arrays.md" "$u/references/arrays.md" \
  -o "$d/references/bags.md" "$u/references/bags.md" \
  -o "$d/references/best-practices.md" "$u/references/best-practices.md" \
  -o "$d/references/dataframes.md" "$u/references/dataframes.md" \
  -o "$d/references/futures.md" "$u/references/futures.md" \
  -o "$d/references/schedulers.md" "$u/references/schedulers.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/skills/dask/.

d="$HOME/.claude/skills/dask"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/dask"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/arrays.md" "$u/references/arrays.md" \
  -o "$d/references/bags.md" "$u/references/bags.md" \
  -o "$d/references/best-practices.md" "$u/references/best-practices.md" \
  -o "$d/references/dataframes.md" "$u/references/dataframes.md" \
  -o "$d/references/futures.md" "$u/references/futures.md" \
  -o "$d/references/schedulers.md" "$u/references/schedulers.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: instala em .agents/skills/dask/.

d=".agents/skills/dask"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/dask"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/arrays.md" "$u/references/arrays.md" \
  -o "$d/references/bags.md" "$u/references/bags.md" \
  -o "$d/references/best-practices.md" "$u/references/best-practices.md" \
  -o "$d/references/dataframes.md" "$u/references/dataframes.md" \
  -o "$d/references/futures.md" "$u/references/futures.md" \
  -o "$d/references/schedulers.md" "$u/references/schedulers.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.agents/skills/dask/.

d="$HOME/.agents/skills/dask"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/dask"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/arrays.md" "$u/references/arrays.md" \
  -o "$d/references/bags.md" "$u/references/bags.md" \
  -o "$d/references/best-practices.md" "$u/references/best-practices.md" \
  -o "$d/references/dataframes.md" "$u/references/dataframes.md" \
  -o "$d/references/futures.md" "$u/references/futures.md" \
  -o "$d/references/schedulers.md" "$u/references/schedulers.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

Neste projeto: instala em .agents/skills/dask/.

d=".agents/skills/dask"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/dask"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/arrays.md" "$u/references/arrays.md" \
  -o "$d/references/bags.md" "$u/references/bags.md" \
  -o "$d/references/best-practices.md" "$u/references/best-practices.md" \
  -o "$d/references/dataframes.md" "$u/references/dataframes.md" \
  -o "$d/references/futures.md" "$u/references/futures.md" \
  -o "$d/references/schedulers.md" "$u/references/schedulers.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.gemini/antigravity-cli/skills/dask/.

d="$HOME/.gemini/antigravity-cli/skills/dask"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/dask"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/arrays.md" "$u/references/arrays.md" \
  -o "$d/references/bags.md" "$u/references/bags.md" \
  -o "$d/references/best-practices.md" "$u/references/best-practices.md" \
  -o "$d/references/dataframes.md" "$u/references/dataframes.md" \
  -o "$d/references/futures.md" "$u/references/futures.md" \
  -o "$d/references/schedulers.md" "$u/references/schedulers.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.dask

Prévia do SKILL.md

---
name: dask
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."
---

# 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
…

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