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dask

85 / 100 · Low risk

Fine to try.

By lamm-mit/scienceclaw (GitHub) · data · 251 stars · scanned at commit 894598c

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.

Works with any agent that supports SKILL.md skills.

What this skill can do

Install

Paste this into your terminal. It downloads exactly the version we scanned, not whatever is newest.

if [ -e ~/.agents/skills/'dask' ] || [ -L ~/.agents/skills/'dask' ]; then echo 'A skill folder named dask already exists — not replacing it' >&2; false; else d=$(mktemp -d) && git clone --quiet --filter=blob:none --no-checkout 'https://github.com/lamm-mit/scienceclaw' "$d" && git -C "$d" checkout --quiet '894598cd3a90891a077c254566276bd379038bad' -- 'skills/dask' && mkdir -p ~/.agents/skills && cp -R "$d"/'skills/dask' ~/.agents/skills/'dask' && rm -rf "$d"; fi

Codex reads ~/.agents/skills directly. For Claude Code, Cursor, Copilot, Gemini CLI, Antigravity or OpenCode, also link it. For Claude Code: ln -s ~/.agents/skills/'dask' ~/.claude/skills/'dask' (other agents: ~/.cursor/skills, ~/.copilot/skills, ~/.gemini/skills, ~/.gemini/config/skills, ~/.config/opencode/skills). Or install through the finder skill, which does both.

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