duckdb
Use when writing DuckDB SQL, using .duckdb files, duckdb imports, analytical queries, CSV/Parquet/JSON ingestion, ETL pipelines, extensions, client APIs, configuration, or performance tuning.
Best use case
duckdb is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when writing DuckDB SQL, using .duckdb files, duckdb imports, analytical queries, CSV/Parquet/JSON ingestion, ETL pipelines, extensions, client APIs, configuration, or performance tuning.
Teams using duckdb should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/duckdb/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How duckdb Compares
| Feature / Agent | duckdb | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when writing DuckDB SQL, using .duckdb files, duckdb imports, analytical queries, CSV/Parquet/JSON ingestion, ETL pipelines, extensions, client APIs, configuration, or performance tuning.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# DuckDB
## Overview
DuckDB is an in-process analytical database with rich SQL dialect, first-class support for Parquet/CSV/JSON, and client APIs for Python, Node.js, Rust, Java, R, Go, WASM, and more. It excels at OLAP workloads, local data exploration, embedded analytics, and data engineering pipelines across local and cloud data sources.
## References Index
For detailed guides and patterns, refer to the following documents in `references/`:
- **[Core DuckDB](references/core.md)**
- SQL dialect highlights, data import/export, configuration, and key SQL patterns.
- **[Advanced SQL Patterns](references/sql_patterns.md)**
- QUALIFY, COLUMNS(*), EXCLUDE/REPLACE/RENAME, list comprehensions, structs, maps, PIVOT/UNPIVOT, ASOF joins, UNION BY NAME, recursive CTEs, GROUP BY ALL, SAMPLE, string slicing, lambda functions.
- **[Performance Tuning](references/performance.md)**
- EXPLAIN ANALYZE, storage inspection, pushdown optimizations, parallel execution, memory management, Parquet performance, partition pruning, bulk loading, indexing.
- **[Data Engineering](references/data_engineering.md)**
- Multi-source reads (CSV, Parquet, JSON, Excel, SQLite, PostgreSQL, MySQL), httpfs/S3/GCS/Azure, glob patterns, Delta Lake, Iceberg, partitioned output, ETL patterns, cross-database queries, secrets management.
- **[Python Client](references/python_client.md)**
- Connection management, DataFrame integration, relational API, and parameter binding.
- **[Client Connections](references/connections.md)**
- Node.js, Rust, Java/JDBC, R/dbplyr, Go, WASM, ADBC (Arrow), ODBC driver setup and usage.
- **[Key Function Reference](references/functions.md)**
- Aggregates, date/time, string, list, struct, map, spatial, and full-text search functions.
- **[Extension Development](references/extensions.md)**
- Building, testing, and distributing DuckDB C++ extensions.
- **[CLI](references/cli.md)**
- Interactive shell usage, dot-commands, and scripting patterns.
- **[Configuration & Administration](references/configuration.md)**
- Pragmas, SET statements, database files/WAL/checkpointing, catalog inspection, extension management, cloud credentials, .duckdbrc startup config.
---
<workflow>
## Key SQL Dialect Features
- `SELECT * EXCLUDE (col)` -- select all columns except specific ones
- `SELECT COLUMNS('pattern')` -- select columns matching a regex
- `PIVOT` / `UNPIVOT` -- built-in pivot support
- `LIST`, `STRUCT`, `MAP` nested types with full query support
- `GROUP BY ALL`, `ORDER BY ALL` -- automatic grouping/ordering
- Friendly SQL: `FROM tbl SELECT col` syntax, implicit `SELECT *`
</workflow>
---
## Quick Start
<example>
```python
import duckdb
con = duckdb.connect() # in-memory
result = con.sql("SELECT 42 AS answer").fetchall()
```
```bash
# CLI
duckdb mydb.duckdb "SELECT * FROM read_parquet('data/*.parquet')"
```
</example>
---
## Official References
- DuckDB documentation: <https://duckdb.org/docs/>
- Python API: <https://duckdb.org/docs/api/python/overview>
- Extensions: <https://duckdb.org/docs/extensions/overview>
- CLI: <https://duckdb.org/docs/api/cli/overview>
- Extension template: <https://github.com/duckdb/extension-template>
## Shared Styleguide Baseline
- Use shared styleguides for generic language/framework rules to reduce duplication in this skill.
- [General Principles](https://github.com/cofin/flow/blob/main/templates/styleguides/general.md)
- [PostgreSQL](https://github.com/cofin/flow/blob/main/templates/styleguides/databases/postgres_psql.md)
- Keep this skill focused on tool-specific workflows, edge cases, and integration details.
<guardrails>
## Guardrails
Add guardrails instructions here.
</guardrails>
<validation>
## Validation
Add validation instructions here.
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