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Agent skill

Hybrid-Engine Data Analysis

by code-yeongyu in code-yeongyu/oh-my-openagent

Analyzes CSV, Parquet and JSON data with DuckDB, Polars, numpy and matplotlib, preferring a persistent kernel over repeated one-shot processes.

Custom licenceAuto-check passedData & Analytics

Install Hybrid-Engine Data Analysis

skills CLI
$ npx skills add code-yeongyu/oh-my-openagent --skill data-scientist -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install code-yeongyu/oh-my-openagent data-scientist --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/code-yeongyu/oh-my-openagent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/shared-skills/skills/data-scientist .claude/skills/data-scientist && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
data-scientist
GitHub stars
70k
Token cost
~1.4k tokens
SKILL.md length
755 words
Files
11 (incl. scripts, references)
Skills in repo
45
Repo updated
First seen
Licence
Custom licence

At a glance

Analyzes CSV, Parquet and JSON data with DuckDB, Polars, numpy and matplotlib, preferring a persistent kernel over repeated one-shot processes.

  • Works in 4 steps: JavaScript kernel (Bun): run… → Python kernel: the default surface for… → uv lane (uv run --with ...): isolation… → …
  • Exploring a large CSV or Parquet file with SQL-style group-bys and joins
  • SKILL.md covers Execution surfaces: resident…, Engine selection, Placement: decide where the… and Hard rules, plus 3 more sections
  • Runs Shell, Python and PowerShell scripts from its folder; calls uv and bun

What it does

The skill chooses where computation runs before touching data. A resident kernel is the default because each one-shot process pays about a second of spawn and import cost and rescans the file, while a persistent connection answers repeat queries in milliseconds. It describes a JavaScript kernel on Bun using DuckDB's Node API after `scripts/ensure-js-deps.sh`, a Python kernel where Polars and pyarrow come from `scripts/ensure-py-deps.sh`, a `uv run --with` lane for heavy or crash-prone jobs, and batched one-shots when no kernel exists.

For engines, DuckDB handles SQL-shaped work and queries files in place, Polars handles DataFrame pipelines and data larger than memory, numpy covers statistical tests, linear algebra, FFT and sampling, and matplotlib draws every chart with a required visual check. References cover execution surfaces, Polars 1.x syntax, uv setup, placement and visualization, and a quick-query script is bundled. The excerpt ends in the engine comparison.

When your agent uses it

  • Exploring a large CSV or Parquet file with SQL-style group-bys and joins
  • Cleaning a dataset and plotting distributions or time series
  • Choosing between DuckDB and Polars for a data pipeline

Example prompts

  • “Load data/sales.parquet and show monthly revenue by region, then plot the trend.”
  • “Join orders.csv with customers.json and report the top customers by total spend.”
  • “Profile this dataset's distributions and flag columns with missing values.”

Requirements

  • Python with DuckDB, or Bun with `@duckdb/node-api`
  • `uv` for the isolated lane and Polars installs

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. JavaScript kernel (Bun): run scripts/ensure-js-deps.sh once; it prints the absolute
  2. Python kernel: the default surface for Python work. duckdb/numpy/matplotlib are
  3. uv lane (uv run --with ...): isolation for a heavy or crash-prone one-shot that
  4. No kernel (plain-shell harness): the same engines as one-shots — bun -e for

What it can do on your machine

Read from SKILL.md and the folder at commit ab81169. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 5 files in scripts/ (Shell, Python and PowerShell), which the agent can run.

    Shell commands in SKILL.md call:

    • uv
    • bun

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hybrid-Engine Data Analysis loads about 1.4k tokens when it runs, and up to ~5.7k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 755 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 755 words (~1,443 tokens).

“Answer data questions through the cheapest engine and surface that can prove the answer, and decide where the computation should live before touching the data.”

— opening of SKILL.md by code-yeongyu, Custom licence
name
data-scientist

Read the full SKILL.md on GitHub

Files

SKILL.md and 10 other files (scripts, references) in packages/shared-skills/skills/data-scientist of code-yeongyu/oh-my-openagent.

  • SKILL.md
  • references/execution-surfaces.md
  • references/placement.md
  • references/polars-lane.md
  • references/uv-setup.md
  • references/visualization.md
  • scripts/ensure-js-deps.sh
  • scripts/ensure-py-deps.sh
  • scripts/quick-query.py
  • scripts/setup-uv.ps1
  • scripts/setup-uv.sh

Open the folder on GitHubat commit ab81169

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in code-yeongyu/oh-my-openagent, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Hybrid-Engine Data Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Hybrid-Engine Data Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Hybrid-Engine Data Analysis this skillcode-yeongyu/oh-my-openagent70k—~1.4kAutomated safety check: PassCustom licence
Python Executorcortega26/chile-hub1132 repos~1.5kAutomated safety check: PassMIT
Vaex Out-of-Core DataFramesdavila7/claude-code-templates32k12 repos~1.6kAutomated safety check: PassMIT
Analytics Data AnalysisMindrally/skills268—~1.6kAutomated safety check: PassApache-2.0
Plot ML Figureprobabl-ai/skills137—~785Automated safety check: PassBSD-3-Clause
Excel and CSV Data Analysisbytedance/deer-flow83k4 repos~2.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Hybrid-Engine Data Analysis

What does Hybrid-Engine Data Analysis do?

Analyzes CSV, Parquet and JSON data with DuckDB, Polars, numpy and matplotlib, preferring a persistent kernel over repeated one-shot processes. The skill chooses where computation runs before touching data. A resident kernel is the default because each one-shot process pays about a second of spawn and import cost and rescans the file, while a persistent connection answers repeat queries in milliseconds.

When should I use Hybrid-Engine Data Analysis?

Hybrid-Engine Data Analysis fits situations like: exploring a large CSV or Parquet file with SQL-style group-bys and joins; cleaning a dataset and plotting distributions or time series; choosing between DuckDB and Polars for a data pipeline.

How do I install Hybrid-Engine Data Analysis in Claude Code?

Run `npx skills add code-yeongyu/oh-my-openagent --skill data-scientist -a claude-code`. Or copy the skill folder (packages/shared-skills/skills/data-scientist in code-yeongyu/oh-my-openagent) into .claude/skills/data-scientist in your project. Claude Code loads it when a task matches its description.

How do I install Hybrid-Engine Data Analysis in Codex?

Run `npx skills add code-yeongyu/oh-my-openagent --skill data-scientist -a codex`. Or copy the skill folder (packages/shared-skills/skills/data-scientist in code-yeongyu/oh-my-openagent) into .agents/skills/data-scientist in your project. Codex loads it when a task matches its description.

Can I use Hybrid-Engine Data Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add code-yeongyu/oh-my-openagent --skill data-scientist -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-scientist, .gemini/skills/data-scientist, .github/skills/data-scientist and .opencode/skills/data-scientist in your project.

What does Hybrid-Engine Data Analysis need to run?

Going by SKILL.md and its folder, Hybrid-Engine Data Analysis needs a shell, Python and PowerShell for the scripts in its folder and the command-line tools its instructions call (uv and bun). Our summary lists: Python with DuckDB, or Bun with `@duckdb/node-api`; `uv` for the isolated lane and Polars installs.

Does Hybrid-Engine Data Analysis access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Hybrid-Engine Data Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Hybrid-Engine Data Analysis use?

Hybrid-Engine Data Analysis has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.

How many tokens does Hybrid-Engine Data Analysis use?

About 1.4k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.2k tokens, read only when the agent opens those files.

What are the alternatives to Hybrid-Engine Data Analysis?

Skills that share tags, products or a category with Hybrid-Engine Data Analysis: Python Executor (cortega26/chile-hub, 113 stars), Vaex Out-of-Core DataFrames (davila7/claude-code-templates, 32k stars), Analytics Data Analysis (Mindrally/skills, 268 stars) and Plot ML Figure (probabl-ai/skills, 137 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hybrid-Engine Data Analysis?

code-yeongyu (a GitHub user) maintains it in code-yeongyu/oh-my-openagent, which has 69,882 GitHub stars. The repository holds 45 skills in this directory. The repository was last updated on October 8, 2026.

Source: code-yeongyu/oh-my-openagent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.