Polars (Claude Skill)

Fast in-memory DataFrame library for datasets that fit in RAM.

   
Type Claude Skill
Supplier K-Dense Inc. (community OSS)
Availability GA — part of the actively maintained K-Dense scientific-agent-skills collection
Pricing Free / OSS (see upstream LICENSE)
Capabilities Read/Write — Claude runs the skill’s Python locally (Bash), not as an MCP tool

How to install

  • Also packaged in the SciAgent-Skills collection (jaechang-hits (community OSS, CC BY 4.0)): clone jaechang-hits/SciAgent-Skills and run /plugin install sciagent-skills in Claude Code (or copy skills/scientific-computing/polars-dataframes into ~/.claude/skills/).
  • Claude Code / Claude.ai — Skills CLI (recommended): 
    npx skills add K-Dense-AI/scientific-agent-skills

    Installs the K-Dense collection; enable the polars skill when prompted. Works across Claude Code, Cursor, and Codex via the Agent Skills spec (requires Node ≥ 18).

  • Claude Code / Claude Desktop — manual clone: 
    git clone https://github.com/K-Dense-AI/scientific-agent-skills
cp -r scientific-agent-skills/skills/polars ~/.claude/skills/

    Project-scoped alternative: copy into .claude/skills/ instead of ~/.claude/skills/. The skill declares its own Python dependencies in its SKILL.md; install them (the K-Dense skills generally use uv / pip) when prompted on first use.

What it does

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.

Primary use cases: pandas is too slow but data still fits in memory.

Notes

Distributed as a SKILL.md (plus code examples) in the K-Dense collection — Claude executes it locally via Bash/Python rather than as an MCP server. Upstream license: see upstream LICENSE. The skill name to enable after install is polars.

Sources

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