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duckdb

Execute SQL analytical queries on local files and DataFrames.

3|Updated Apr 2, 2026
Search Tags:#analytics#sql#data exploration#parquet#duckdb#polars
One-click install
npx skills add https://github.com/legout/pi-config --skill duckdb-legout
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: duckdb
Source: https://github.com/legout/pi-config/tree/main/installed-skills/duckdb
Command: npx skills add https://github.com/legout/pi-config --skill duckdb-legout

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires duckdb, polars, pyarrow, and includes references (resource) components.

What problem does it solve?

This skill solves the challenge of performing complex analytical queries on large datasets, CSVs, Parquet files, or DataFrames without the overhead of setting up a traditional database server or loading entire datasets into memory.

Core Features & Use Cases

  • Direct File Querying: Run SQL directly on CSV, Parquet, and JSON files using high-performance analytical engines.
  • DataFrame Integration: Seamlessly join and aggregate pandas or Polars DataFrames using standard SQL syntax.
  • Complex Analytics: Execute window functions, CTEs, and multi-table joins for advanced data exploration and ETL pipelines.

Quick Start

Use the duckdb skill to perform a SQL query on the file data.parquet to calculate the average value grouped by category.

Frequently Asked Questions about duckdb

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

FAQPage Schema
How do I run SQL queries directly on Parquet and CSV files without loading them into memory?▼

Yes, you can seamlessly query and join pandas or Polars DataFrames using standard SQL syntax, leveraging pyarrow integration for efficient zero-copy data processing across multiple sources.

Does this approach support advanced analytical SQL like window functions and CTEs?▼

Yes, the skill executes complex analytics including window functions, CTEs, and multi-table joins, enabling advanced data exploration and ETL transformations for analytical workloads.

What dependencies do I need to run SQL analytics on local data files?▼

For complex analytical queries on large datasets, this approach avoids the overhead of setting up a traditional database server, making it ideal for direct file querying and ETL pipelines.

What's the best way to perform ETL transformations on multiple data sources?▼

For complex analytical queries on large datasets, this approach avoids the overhead of setting up a traditional database server, making it ideal for direct file querying and ETL pipelines.

Can I join in-memory DataFrames with local file data using SQL?▼

Yes, you can seamlessly query and join pandas or Polars DataFrames using standard SQL syntax, leveraging pyarrow integration for efficient zero-copy data processing across multiple sources.

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