Agent skill · data analytics · code-yeongyu
data-scientist
Processes and analyzes data with resident-kernel engines (DuckDB, Polars) and one-shot tools. Use for CSV/parquet/JSON analysis, group-by/join/aggregation, time series, distributions, cleaning, or plotting a dataset.
Why this skill is useful
Adds executable scripts for data processing with DuckDB and Polars, optimizing performance and memory usage.
What it needs
Requires duckdb, matplotlib, numpy, polars, pyarrow, uv installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 67,387 stars on the source repository.
What this skill does
Data Scientist: Hybrid-Engine Data Processing 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. Execution surfaces: resident kernel first A persistent REPL/eval kernel (many harnesses expose one for JavaScript and Python) is the default surface. Reason: each one-shot process pays roughly a second of spawn-plus-import overhead and re-scans the input file, while a resident connection amortizes both — after a one-time load, repeat queries return in milliseconds. Exploration is repeat queries, so this difference dominates the session. 1. JavaScript kernel (Bun): run scripts/ensure-js-deps.sh once; it prints the absolute import path for @duckdb/node-api. Dynamic-import it, connect once, query across cells. 2. Python kernel: the default surface for Python work. duckdb/numpy/matplotlib are typically resident; Polars and pyarrow come from scripts/ensure-py-deps.sh, which installs them once into a user cache keyed to the kernel's interpreter — sys.path.insert the printed directory and import. The interpreter itself is never mutated. 3. uv lane (uv run --with ...): isolation for a heavy or crash-prone one-shot that should not take the kernel down. 4. No kernel (plain-shell harness): the same engines as one-shots — bun -e for DuckDB-js, uv run python -c for the Python stack — batching several questions per process. Per-surface patterns and pitfalls: read references/execution-surfaces.md before first use. Engine selection DuckDB for SQL-shaped work: direct file queries, joins, aggregation, subqueries, window functions. It queries CSV/Parquet/JSON in place without loading, spills to disk past its memory limit, and reads remote files with the same syntax. Polars when the pipeline is DataFrame-shaped: expression-chain transforms, reshapes, streaming datasets past RAM — resident in the Python kernel via ensure-py-deps.sh. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills code-yeongyu/data-scientist