data-scientist
Expert data processing specialist with intelligent DuckDB/Polars selection for maximum performance. Always includes numpy, never uses pandas, runs everything through uv. Triggers: 'analyze the data', 'analyze this file', 'what is in this CSV/parquet/json', 'summarize this', 'group by', 'filter rows', 'sort by', 'join these files', 'merge datasets', 'time series trend', 'last 30 days data', 'compare yesterday and today', 'distribution/histogram', 'correlation', 'clean duplicates', 'handle missing values', 'dataset larger than RAM', 'SQL query on files', 'DataFrame operations', 'chart/plot this
Profile →npx skills add code-yeongyu/oh-my-openagent --skill data-scientist --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 bash scripts/setup-uv.sh # macOS / Linux / WSL / Git Bash — auto-detects OS + arch, installs or updates uv to latest ``` ```powershell 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](references/uv-setup.md). Verify: `uv --version`. ## Core Principles ### ABSOLUTE
- Role & Expertise
- Environment Setup
- Core Principles
- ABSOLUTE RULES
- Standard Package Pattern
- Tool Selection Logic
- Decision Tree (Apply in Order)
- Quick Reference
- Essential Patterns
- DuckDB Direct File Query
- Polars Lazy Evaluation
- Zero-Copy DuckDB → Polars
- Hybrid Approach
- Quick Query CLI
bash scripts/setup-uv.sh # macOS / Linux / WSL / Git Bash — auto-detects OS + arch, installs or updates uv to latest
Default for data tasks (numpy + pyarrow are mandatory parts of the set)
uv run --with numpy --with duckdb --with polars --with pyarrow python -c "{code}"
With visualization (RECOMMENDED for most analysis requests)
uv run --with numpy --with duckdb --with polars --with pyarrow --with matplotlib python -c "{code}"
Pure Polars
uv run --with numpy --with polars python -c "{code}"
Pure DuckDB (with the Arrow handoff available)
uv run --with numpy --with duckdb --with pyarrow python -c "{code}"
SQL query (uses DuckDB)What does the data-scientist skill do?
Expert data processing specialist with intelligent DuckDB/Polars selection for maximum performance. Always includes numpy, never uses pandas, runs everything through uv. Triggers: 'analyze the data', 'analyze this file', 'what is in this CSV/parquet/json', 'summarize this', 'group by', 'filter rows', 'sort by', 'join these files', 'merge datasets', 'time series trend', 'last 30 days data', 'compare yesterday and today', 'distribution/histogram', 'correlation', 'clean duplicates', 'handle missing values', 'dataset larger than RAM', 'SQL query on files', 'DataFrame operations', 'chart/plot this
How do I install it?
Run `npx skills add code-yeongyu/oh-my-openagent --skill data-scientist --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From code-yeongyu/oh-my-openagent, a repository with 67,209 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.