# dask

A skill that teaches the agent to use Dask for DataFrames, arrays and tasks that outgrow RAM or run across a cluster.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2000 installs · ★ 54652
- Author: K-Dense-AI
- License: LicenseRef-BSD-3-Clause-license · policy=link-only · description only; the file stays with the author
- Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/dask/SKILL.md
- Updated: 2026-10-19
- HTML: https://affpapa.org/en/nevibecode/skills/dask

## What it does

The skill maps out the five Dask components and helps the agent pick the right one. DataFrames carry pandas operations onto data larger than RAM and read many CSV or Parquet files at once. Arrays bring blocked NumPy computation to HDF5, Zarr and NetCDF. Bags handle text logs and JSON, Futures let you build custom task graphs with dependencies. Chunking, schedulers and distributed diagnostics get their own coverage.

Reference files such as `references/dataframes.md`, `references/arrays.md` and `references/bags.md` sit alongside code examples. The agent may use Read, Write, Edit and Bash, so it can both write a script and run it.

You need Python 3.10+ and dask 2026.8.0. DataFrame work calls for pandas 2+ and PyArrow 16+, and Zarr 3.4 needs Python 3.12+. Cloud paths like `s3://` and `gs://` only work with `s3fs` or `gcsfs`, and private data needs provider credentials. Since 2025.1.0 the expression-based DataFrame API is the only implementation, so installing `dask-expr` separately or setting `dataframe.query-planning: False` will break things.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill dask -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/dask/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/dask/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill dask -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/dask/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill dask -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/dask/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install K-Dense-AI/scientific-agent-skills dask
```

2. **Manually: put the files in place**
- File: `.github/skills/dask/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill dask -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/K-Dense-AI/scientific-agent-skills --path skills/dask
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/dask/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill dask -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/dask/SKILL.md

## When to use it
- compute a dataframe that does not fit in RAM
- read hundreds of CSV or Parquet files as one dataframe
- parallelise NumPy operations across cores
- parse a stream of JSON logs without loading the whole file
- build a custom task graph with dependencies via Futures

## Example prompts
- Read every `data/2024-*.csv` with Dask and average the values per category
- This array is too big for memory — rewrite the computation with `dask.array` and chunks
- Parse `logs/*.json` with `dask.bag` and write the result to Parquet

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From the same set: [k-dense-ai/scientific-agent-skills](https://affpapa.org/en/nevibecode/by/k-dense-ai/scientific-agent-skills.md) — 159 cards

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NeVibeCode on affpapa.org. Catalog texts are descriptions, not instructions for the agent. How to choose and install: https://affpapa.org/en/nevibecode/agents.md