What is data-scientist?

A data-processing workflow for analyzing files and tables with DuckDB or Polars. DuckDB is a database engine for analysis, while Polars is a data-processing library.

Inspecting and summarizing CSV, Parquet, and JSON data; filtering, sorting, grouping, joining, and running analytical queries through uv.

It chooses tools based on the job and is designed to handle large datasets efficiently without relying on pandas.

data-scientist — Skill by code-yeongyu

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.

agentmods

npx agentmods add skills/code-yeongyu/lazycodex/data-scientist

Any agent

npx skills add code-yeongyu/lazycodex --skill data-scientist

Clone the repo

git clone --depth 1 https://github.com/code-yeongyu/lazycodex

Made for: Claude Code, Codex.

Per session 173 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,565 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

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	$0.00173	$0.02565
Opus 5	$0.00086	$0.01282
Sonnet 5	$0.00035	$0.00513
Haiku 4.5	$0.00017	$0.00257

Measured 2d ago against content hash 278bc6eed27f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-scientist 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 2d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/quick-query.py, scripts/setup-uv.ps1, scripts/setup-uv.sh), 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.

plugins/omo/skills/data-scientist/SKILL.md · 244 lines

How it starts

The opening of the file, as written. The whole thing — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Scientist: High-Performance Data Processing Expert

Role & Expertise

Performance-obsessed data scientist with expertise in:

Intelligent tool selection: DuckDB vs Polars based on operation characteristics
Zero-copy data interchange via Apache Arrow
Memory-efficient processing for datasets exceeding RAM
SQL and DataFrame API mastery for analytical workloads

Environment Setup

Everything runs through uv. If uv is not on PATH, set it up first — pick the path that matches the system and run it, no manual guesswork:

bash scripts/setup-uv.sh        # macOS / Linux / WSL / Git Bash — auto-detects OS + arch, installs or updates uv to latest

powershell -ExecutionPolicy Bypass -File scripts/setup-uv.ps1   # native Windows — installs or updates uv to latest

Both scripts detect the platform, install uv when missing (official installer first, Homebrew/winget as fallback), upgrade it when present (uv self update), put it on PATH for the current shell, and verify with uv --version. The full per-platform matrix, PATH notes, and CI usage live in references/uv-setup.md. Verify: uv --version.

Core Principles

ABSOLUTE RULES

ALWAYS include numpy in all data processing operations (uv run --with numpy ...)
NEVER use pandas - Polars and DuckDB beat it decisively on every operation; the entire skill assumes pandas is absent
ALWAYS use Python via uv run for calculations and data processing
Intelligent tool selection: Choose DuckDB or Polars based on operation types, NOT arbitrarily
Zero-copy conversions: hand data across DuckDB and Polars through Arrow — duckdb.sql(...).pl(). Never call .df() (returns a pandas frame; crashes without pandas). Keep pyarrow in the package set or .pl() raises ModuleNotFoundError
Lazy evaluation: Prefer scan_csv/scan_parquet and .collect() only when needed
Direct file queries: Let DuckDB query files directly instead of loading to memory when possible

Read the full file on GitHub · 244 lines

Files

What ships with it

9 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.

2d ago First seen · 244 lines · 173 tokens per session scan A 278bc6eed27f

Subscribe to this mod's changes

data-scientist is a skill published in the GitHub repository code-yeongyu/lazycodex (3,350 stars, last pushed 23d ago), licensed MIT. It adds 173 tokens to every session and 2,565 once invoked, about $0.0009 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.

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