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db

Design, build, and query DuckDB schemas within Python workflows.

Updated Mar 20, 2026
Search Tags:#analytics#python#sql#pandas#duckdb#schema-design
One-click install
npx skills add https://github.com/jaydeland/Tony --skill db-jaydeland
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: db
Source: https://github.com/jaydeland/Tony/tree/main/.claude/skills/db
Command: npx skills add https://github.com/jaydeland/Tony --skill db-jaydeland

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps data professionals design efficient DuckDB schemas, write SQL queries, and perform end-to-end analytics using the DuckDB Python interface.

Core Features & Use Cases

  • Schema design and modeling for analytical workloads using DuckDB
  • SQL development and execution from Python with duckdb, including parameterized queries
  • Pandas integration for seamless data analysis and pipeline prototyping
  • Quick-start patterns for common analytics tasks such as time-series, star schemas, and denormalized tables

Quick Start

Create an in-memory DuckDB database, define a simple users table, and run a query to fetch all users.

Frequently Asked Questions about db

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

FAQPage Schema
How do I design DuckDB schemas and run analytical queries in Python?▼

You can design DuckDB schemas and run analytical queries in Python by using the DuckDB Python API to create tables and execute parameterized queries. This Skill provides patterns for building efficient analytical models directly within your Python workflows.

Can I integrate DuckDB with pandas for ad-hoc data exploration?▼

Yes, you can integrate DuckDB with pandas for ad-hoc data exploration. The DuckDB Python API allows seamless querying of in-memory pandas DataFrames, enabling rapid prototyping for data pipelines and analytics tasks without requiring data duplication.

What is the best way to query CSV and Parquet files using DuckDB?▼

The best way to query CSV and Parquet files using DuckDB is by leveraging its native support for these formats within Python. You can run SQL queries directly against external files to accelerate OLAP analytics and build denormalized tables without a separate loading step.

Does this DuckDB Skill support star schema design and time-series modeling?▼

Yes, this DuckDB Skill supports star schema design and time-series modeling. It offers quick-start patterns for common analytical tasks, allowing you to efficiently structure denormalized tables optimized for OLAP workloads and data pipelines.

How do I execute parameterized SQL queries with DuckDB in a Python workflow?▼

To execute parameterized SQL queries with DuckDB in a Python workflow, use the DuckDB Python API to safely pass variables into your SQL statements. This prevents SQL injection and streamlines dynamic query construction in data pipelines.

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