What is polars-expertise?

A guide for using Polars, a DataFrame library for processing tabular data in Python or Rust. It covers expressions, immediate and deferred execution, Parquet files, grouping, and time-based operations.

It is for filtering and aggregating tables, reading Parquet files, converting workflows from other data tools, and using lazy or streaming-style processing in Python or Rust.

It helps developers choose and write Polars code instead of guessing how its data-processing API works, especially for larger datasets.

polars-expertise — Skill for Codex by DeevsDeevs

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add DeevsDeevs/agent-system --skill polars-expertise

Clone the repo

git clone --depth 1 https://github.com/DeevsDeevs/agent-system

Made for: Codex.

Its marketplace also offers this one on its own, as the plugin polars-expertise — /plugin install polars-expertise after adding the marketplace above.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/deevsdeevs/agent-system/polars-expertise.svg)](https://agentmods.dev/skills/deevsdeevs/agent-system/polars-expertise)

Your own site

<a href="https://agentmods.dev/skills/deevsdeevs/agent-system/polars-expertise"><img src="https://agentmods.dev/badge/skills/deevsdeevs/agent-system/polars-expertise.svg" alt="Measured on agentmods" height="20"></a>

Per session 136 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,044 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00136	$0.02044
Opus 5	$0.00068	$0.01022
Sonnet 5	$0.00027	$0.00409
Haiku 4.5	$0.00014	$0.00204

Measured 7d ago against content hash 16cf49e414cd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

polars-expertise scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (examples/financial_ohlcv.py, examples/pandas_migration.py, examples/streaming_large_file.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

polars-expertise/SKILL.md · 224 lines

How it starts

The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Polars

High-performance DataFrame library built on Apache Arrow. Supports Python and Rust with expression-based API, lazy evaluation, and automatic parallelization.

Quick Start

Python

uv pip install polars
# GPU support: uv pip install polars[gpu]

import polars as pl

# Eager: immediate execution
df = pl.DataFrame({"symbol": ["AAPL", "GOOG"], "price": [150.0, 140.0]})
df.filter(pl.col("price") > 145).select("symbol", "price")

# Lazy: optimized execution (preferred for large data)
lf = pl.scan_parquet("trades.parquet")
result = lf.filter(pl.col("volume") > 1000).group_by("symbol").agg(
    pl.col("price").mean().alias("avg_price")
).collect()

Rust

# Cargo.toml - select features you need
[dependencies]
polars = { version = "0.46", features = ["lazy", "parquet", "temporal"] }

use polars::prelude::*;

fn main() -> PolarsResult<()> {
    // Eager
    let df = df![
        "symbol" => ["AAPL", "GOOG"],
        "price" => [150.0, 140.0]
    ]?;

    // Lazy (preferred)
    let lf = LazyFrame::scan_parquet("trades.parquet", Default::default())?;
    let result = lf
        .filter(col("volume").gt(lit(1000)))
        .group_by([col("symbol")])
        .agg([col("price").mean().alias("avg_price")])
        .collect()?;
    Ok(())
}

Core Pattern: Expressions

Everything in Polars is an expression. Expressions are composable, lazy, and parallelized.

# Expression building blocks
pl.col("price")                      # column reference
pl.col("price") * pl.col("volume")   # arithmetic
pl.col("price").mean().over("symbol") # window function
pl.when(cond).then(a).otherwise(b)   # conditional

Expressions execute in contexts: select(), with_columns(), filter(), group_by().agg()

When to Use Lazy

Use Lazy (`scan_*`, `.lazy()`)	Use Eager (`read_*`)
Large files (> RAM)	Small data, exploration
Complex pipelines	Simple one-off ops
Need query optimization	Interactive notebooks
Streaming required	Immediate feedback

Read the full file on GitHub · 224 lines

Files

What ships with it

21 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago First seen · 224 lines · 136 tokens per session scan A 16cf49e414cd

Subscribe to this mod's changes

polars-expertise is a skill published in the GitHub repository DeevsDeevs/agent-system (40 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 136 tokens to every session and 2,044 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Analytics and metrics Claude Code ai-agents opencode

ralplan

Consensus planning entrypoint that auto-gates vague ralph/autopilot/team requests before execution.

Yeachan-Heo/oh-my-claudecode · 21 tokens

deep-interview

Socratic deep interview with mathematical ambiguity gating before explicit execution approval.

Yeachan-Heo/oh-my-claudecode · 16 tokens

remember

Review reusable project knowledge and decide what belongs in project memory, notepad, or durable docs.

Yeachan-Heo/oh-my-claudecode · 20 tokens

plannotator-annotate

Open Plannotator's annotation UI for a file, folder, or URL, then address the returned annotations.

backnotprop/plannotator · 29 tokens

southwest

Search Southwest Airlines fares and points pricing via Patchright browser automation. SW is not in any GDS or API. Covers all fare classes, Companion Pass value, and fare drop monitoring.

borski/travel-hacking-toolkit · 40 tokens

review-work

Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.

code-yeongyu/oh-my-openagent · 63 tokens