Individual skill
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.
plugins/omo/skills/data-scientist/SKILL.md
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- Last checked
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- Repository last updated
- Nov 6, 2026
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SKILL.mdcore · 5,797 bytesagents/openai.yamlasset · 49 bytesreferences/execution-surfaces.mddoc · 4,088 bytesreferences/placement.mddoc · 3,424 bytesreferences/polars-lane.mddoc · 3,620 bytesreferences/uv-setup.mddoc · 2,797 bytesreferences/visualization.mddoc · 3,008 bytesscripts/ensure-js-deps.shruns · 983 bytesscripts/ensure-py-deps.shruns · 1,358 bytesscripts/quick-query.pyruns · 3,329 bytesscripts/setup-uv.ps1runs · 2,339 bytesscripts/setup-uv.shruns · 1,833 bytes
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# 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