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ryankolean
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cli-duckdb

Run in-process analytical SQL queries on CSV, Parquet, and JSON files.

3|1|Updated Mar 27, 2026
Search Tags:#json#csv#parquet#duckdb#in-process#olap#analytical-sql
One-click install
npx skills add https://github.com/ryankolean/summit-claude-skills --skill cli-duckdb
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cli-duckdb
Source: https://github.com/ryankolean/summit-claude-skills/tree/main/skills/cli-duckdb
Command: npx skills add https://github.com/ryankolean/summit-claude-skills --skill cli-duckdb

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

DuckDB enables performing analytical SQL directly on local or remote file formats without importing data, eliminating the need to build separate ETL steps for ad-hoc analytics.

Core Features & Use Cases

  • In-process SQL analytics on CSV, Parquet, JSON, and remote files (S3/HTTP) without data loading.
  • OLAP-style queries: aggregations, window functions, and cross-format joins across multiple files.
  • Quick-start profiling and lightweight data exploration directly from raw datasets.

Quick Start

Run a quick analytic SQL query on a local CSV or Parquet file using DuckDB.

Frequently Asked Questions about cli-duckdb

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run analytical SQL queries directly on local CSV and Parquet files?▼

You can run analytical SQL queries directly on local CSV and Parquet files by using an in-process database engine, which queries the data without requiring a separate server setup or data loading.

Can I join data across CSV, JSON, and Parquet files without building an ETL pipeline?▼

Yes, you can perform cross-format joins across CSV, JSON, and Parquet files using in-process SQL analytics, eliminating the need to build separate ETL steps for ad-hoc data analysis.

Does in-process OLAP support remote files stored on S3 or HTTP?▼

In-process OLAP supports direct querying of remote files stored on S3 or HTTP, enabling you to run ad-hoc analytics and aggregations on remote datasets without downloading them first.

What is the best way to profile raw datasets without importing them into a database?▼

The best way to profile raw datasets without importing them is using in-process analytical SQL, which provides lightweight data exploration and quick-start profiling directly on raw files.

Do I need to set up a database server to use OLAP features like window functions on local files?▼

No, you do not need to set up a database server to use OLAP features like window functions on local files; an in-process engine handles aggregations and window functions directly on the files.

Are there limitations to using in-process SQL analytics for ad-hoc data exploration?▼

In-process SQL analytics is designed for lightweight, ad-hoc data exploration and profiling rather than heavy, concurrent server workloads, making it ideal for direct file analysis without server setup.

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