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polars

85 / 100 · Low risk

Fine to try.

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

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

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/'polars' ] || [ -L ~/.agents/skills/'polars' ]; then echo 'A skill folder named polars 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 "$d" checkout --quiet '894598cd3a90891a077c254566276bd379038bad' -- 'skills/polars' && mkdir -p ~/.agents/skills && cp -R "$d"/'skills/polars' ~/.agents/skills/'polars' && 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/'polars' ~/.claude/skills/'polars' (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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