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motherduck-explore

Discover MotherDuck databases, tables, columns, and data shares via metadata functions.

53|3|Updated Mar 30, 2026
Search Tags:#data sharing#data catalog#data profiling#motherduck#database discovery#schema exploration#duckdb sql
One-click install
npx skills add https://github.com/motherduckdb/agent-skills --skill motherduck-explore
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: motherduck-explore
Source: https://github.com/motherduckdb/agent-skills/tree/main/plugins/motherduck-skills-claude/skills/motherduck-explore
Command: npx skills add https://github.com/motherduckdb/agent-skills --skill motherduck-explore

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It eliminates the guesswork of finding which databases, tables, columns, and shares exist in a MotherDuck environment so you can confidently write the right queries and models.

Core Features & Use Cases

  • Catalog discovery: Identify available databases, schemas, tables/views, and shared data sources.
  • Schema understanding: Inspect column types, nullability, and table/column comments to determine data meaning and constraints.
  • Data profiling & preview: Use SUMMARIZE and small row previews to establish grain, ranges, cardinality, and likely measures before moving to analysis.
  • Workflow fit: Supports the “explore first, then query/model” pattern for analytics, dashboarding, migrations, and data product work.

Quick Start

Use the motherduck-explore skill to list the databases and then summarize and sample the key tables in your target database to produce a concise schema map.

Frequently Asked Questions about motherduck-explore

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

FAQPage Schema
How do I discover databases, tables, and columns in a MotherDuck environment?▼

Schema discovery in MotherDuck is done by listing available databases, schemas, and tables, then inspecting column types, nullability, and comments to determine data meaning and constraints before querying.

Can I preview data and profile table cardinality before writing DuckDB SQL queries?▼

Yes, data profiling and previewing use SUMMARIZE and small row samples to establish grain, ranges, cardinality, and likely measures, ensuring you write the correct DuckDB SQL queries.

What's the best way to explore shared data sources and catalog schemas in MotherDuck?▼

Catalog discovery identifies available databases, schemas, tables, views, and shared data sources, removing uncertainty by producing a compact grain-and-join-ready schema summary for downstream work.

Does the schema exploration flow support read-only data profiling for dashboarding tasks?▼

Yes, the recommended exploration flow uses read-only MotherDuck metadata functions and optional structured tools to return concise schema summaries, supporting the explore-first pattern for dashboarding and modeling.

When do I need to inspect column nullability and table comments during data catalog search?▼

You need schema understanding when preparing for analytical work, as inspecting column types, nullability, and comments determines data meaning and constraints to remove guesswork before modeling.

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