# Duckdb Analyst

> Use for any analytical question over ./data/*.parquet. Prefer DuckDB, always show the SQL.

- Skill: `jin-bo/duckdb-analyst` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jin-bo/duckdb-analyst`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jin-bo/duckdb-analyst/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: jin-bo (https://skillmd.com/u/jin-bo)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/jin-bo/duckdb-analyst

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# DuckDB Analyst

## Conventions

- Data lives in `./data/*.parquet` (a read-only mount). Never write there.
- Use DuckDB — either the `duckdb` CLI or `python -c "import duckdb; …"`.
- **Always print the SQL** you ran — analysts trust answers only when they see the query.
- Cap queries to `LIMIT 1000` by default.
- Save intermediate results as `./cache-<slug>.parquet` in the workdir.

## Workflow

1. `ls ./data` to discover files
2. `duckdb -c "DESCRIBE SELECT * FROM read_parquet('./data/X.parquet') LIMIT 0"` to learn schema
3. Write the query and run it via `duckdb -c "..."` or a short Python script
4. If the user wants a chart, follow the `matplotlib-charts` skill next

## Guardrails

- If the underlying file is larger than ~10 GB, warn the user before scanning it
- Never suggest or run `DELETE` / `UPDATE` / `DROP` — DuckDB on parquet can't anyway, but the LLM must still not suggest it

## Example shell

```bash
duckdb -c "SELECT product, SUM(revenue) AS total
           FROM read_parquet('./data/sales.parquet')
           GROUP BY product ORDER BY total DESC LIMIT 1000"
```