Duckdb Docs

What Is This?

Overview

DuckDB Docs is a command-line skill that enables developers to search DuckDB and DuckLake documentation directly from their terminal or AI-assisted workflow. It uses full-text search against a locally cached index to return relevant documentation chunks based on a question or keyword. This eliminates the need to open a browser, navigate documentation sites, or scroll through lengthy reference pages during active development sessions.

The skill integrates with the Happycapy Skills platform and relies on a local DuckDB installation to power its search index. When invoked, it checks for a valid DuckDB binary, prepares the local documentation cache if needed, and then queries that cache to surface the most relevant content for the given input. Results are returned directly in the terminal, keeping developers in their flow state.

DuckLake documentation is also included in the search scope, making this skill useful for teams working with lakehouse architectures that combine DuckDB with object storage or catalog layers. Whether you are debugging a SQL function, exploring DuckLake table formats, or looking up configuration options, this skill provides fast, offline-capable access to the content you need.

Who Should Use This

• Backend developers who use DuckDB for analytical queries and need quick reference access without leaving the terminal
• Data engineers building pipelines with DuckLake who frequently consult documentation on table formats and catalog behavior
• AI-assisted workflow users who want their coding assistant to pull accurate DuckDB documentation during code generation sessions
• Database administrators evaluating DuckDB features and needing to compare configuration options or extension capabilities

Why Use It?

Problems It Solves

• Developers lose time switching between the terminal and a browser to look up DuckDB syntax, slowing down iteration cycles during active coding
• Documentation searches on public websites return broad results that require additional filtering to find the specific function or configuration detail needed
• Offline or restricted-network environments make it difficult to access online documentation, blocking progress during travel or in secure development environments
• AI coding assistants sometimes generate incorrect DuckDB SQL because they lack access to current, authoritative documentation at query time
• Teams working with DuckLake have no single unified search tool that covers both DuckDB core docs and DuckLake-specific content simultaneously

Core Highlights

• Full-text search across DuckDB and DuckLake documentation and blog posts
• Locally cached index for fast, offline-capable queries
• Returns focused documentation chunks rather than entire pages
• Accepts natural language questions or specific keywords as input
• Integrates with AI-assisted development workflows via the Happycapy Skills platform
• Automatically checks for and delegates DuckDB installation if not present
• Minimal setup required beyond a working DuckDB binary
• Covers both reference documentation and blog posts for broader context

How to Use It?

Basic Usage

Invoke the skill with a question or keyword as the argument:

happycapy run duckdb-docs "how to read a parquet file"

happycapy run duckdb-docs "window functions"

happycapy run duckdb-docs "DuckLake catalog configuration"

The skill returns the most relevant documentation chunks for the given input, formatted for terminal readability.

Specific Scenarios

Scenario 1: Looking up a SQL function during development You are writing an analytical query and cannot remember the exact syntax for list_aggregate. Run the following to retrieve the relevant reference section immediately:

happycapy run duckdb-docs "list_aggregate syntax"

Scenario 2: Researching DuckLake table registration You are configuring a new DuckLake environment and need to understand how tables are registered in the catalog:

happycapy run duckdb-docs "DuckLake register table"

Real-World Examples

A data engineer building an ETL pipeline uses the skill to quickly verify the correct syntax for COPY TO with Parquet options, avoiding a context switch to the browser mid-session. A backend developer integrating DuckDB into a Python application queries the skill for extension loading documentation to confirm the correct LOAD statement format.

When to Use It?

Use Cases

• Verifying SQL function signatures and return types during query development
• Looking up DuckDB extension names and installation commands
• Checking DuckLake catalog and storage configuration options
• Reviewing performance tuning settings and memory configuration parameters
• Finding blog post examples that demonstrate real-world DuckDB usage patterns
• Confirming correct file format options for COPY, READ_CSV, or READ_PARQUET
• Exploring new DuckDB features introduced in recent releases

Important Notes

Requirements

• DuckDB must be installed and accessible via the system PATH
• The Happycapy Skills platform must be configured with the duckdb-skills source
• Bash must be available in the execution environment
• An initial documentation cache build is required on first use, which needs a network connection