---

name: duckdb-sql description: Generate DuckDB SQL queries. Use when user asks about DuckDB queries, data analysis, exploring .ddb database files, CSV files, Parquet files, wants help editing/improving SQL, asks to use the duckdb skill, references duckdb assets, or wants to initialize/setup duckdb analysis. allowed-tools: Read, Grep, Glob, Bash

DuckDB SQL Query Assistant

Generate SQL queries for DuckDB databases, CSV files, and Parquet files.

Reference Files

• Query patterns: Multi-file queries, common SQL patterns, CSV handling
• DuckDB syntax: Data types, functions, CLI commands
• Setup guide: First-time setup, enum detection, schema updates
• Data dictionary template: Template for generating data_dictionary.md

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Core Workflow

Step 1: Check for Existing Assets

Your FIRST action must be to check if duckdb_sql_assets/ exists.

Even if the user provides direct file paths (e.g., @file.ddb), check for existing assets first.

If duckdb_sql_assets/ exists with tables_inventory.json, schema_*.sql, and data_dictionary.md:

NEVER:

• Run duckdb bash commands (no .schema, no DESCRIBE, no direct queries)
• Open or access .ddb files directly
• Regenerate asset files
• Check for schema changes (unless explicitly requested)

ALWAYS:

• Read tables_inventory.json for available tables and file paths
• Read data_dictionary.md for business context
• Read relevant schema_*.sql files to validate column names
• Generate queries using documented schema only

The only exception: user explicitly requests schema refresh (see setup guide).

Step 2: Identify Question Type

• Discovery questions ("Do we have...?", "Where is...?") -> Search documentation, explain findings
• Query requests ("Show me...", "List all...") -> Use two-step query plan workflow
• SQL review requests ("Review this SQL", "Improve this query") -> Use SQL review workflow

Step 3: If No Assets Exist

See First-Time Setup for the complete workflow.

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Display-Only by Default

DO NOT execute queries unless explicitly requested. Your primary purpose is to display queries for the user to review and run themselves.

• Default: Generate and display SQL only
• Execute: Only when user says "run this", "execute it", "show me the results"

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Asset Directory

Terminology: "assets", "the assets", "duckdb assets" = duckdb_sql_assets/ directory.

Contents:

• tables_inventory.json - Manifest of source files and table metadata
• schema_<filename>.sql - Schema files (one per source file)
• data_dictionary.md - Semantic documentation of tables and fields

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Supported File Types

File Type	Extension	Tables per File	Schema Source
DuckDB Database	.ddb	Multiple	Native schema via .schema
CSV	.csv	Single	Auto-inferred by DuckDB
Parquet	.parquet	Single	Embedded in file

Table naming for CSV/Parquet: Convert filename to lowercase snake_case (e.g., My Transactions-2024.csv -> my_transactions_2024).

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Query Execution Model

All queries run in an in-memory DuckDB session (duckdb with no file argument).

File Type	How to Reference	Example
.ddb table	_db_alias.tablename after ATTACH	_db_sales.customers
CSV file	File path in quotes	'data/transactions.csv'
Parquet file	File path in quotes	'data/events.parquet'
Glob pattern	Glob in quotes	'logs/*.csv'

ATTACH Alias Convention

Use _db_ prefix + filename slug: sales.ddb -> AS _db_sales

Standard Query Preamble

-- Setup: Attach database files
ATTACH IF NOT EXISTS 'data/sales.ddb' AS _db_sales (READ_ONLY);

-- Query
SELECT c.name, o.total_amount
FROM _db_sales.customers c
JOIN _db_sales.orders o ON c.customer_id = o.customer_id;

Path convention: Use relative paths by default for portability.

See query-patterns.md for more examples.

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Schema Validation (CRITICAL)

Before generating ANY SQL, validate every table and column against asset files only:

1. Verify all table names exist in tables_inventory.json or schema files
2. Verify all column names exist in schema_<filename>.sql
3. Verify column types match usage
4. Verify JOIN columns exist on both sides

If you cannot find a table or column, DO NOT use it. Instead:

• Tell user the field doesn't exist
• Suggest similar fields that DO exist
• Ask for clarification

Common hallucination patterns to avoid:

• Assuming name exists (check for first_name, product_name, etc.)
• Assuming user_id when it might be profile_id or customer_id
• Inventing status values not in the data

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Query Generation - Two-Step Workflow

ALWAYS present a query plan first before writing SQL.

Step 1: Present Query Plan

**Query Plan:**
- **Attach statements needed:**
  - `ATTACH IF NOT EXISTS 'data/sales.ddb' AS _db_sales (READ_ONLY)`
- **Tables:**
  - _db_sales.customers (c) - Customer records [from sales.ddb]
  - _db_sales.orders (o) - Order transactions [from sales.ddb]
- **Joins:**
  - _db_sales.customers -> _db_sales.orders on customer_id
- **Filters:**
  - Optional: date range on order_date
- **Output:** Returns order details with customer names

Does this plan look correct?

If ambiguous, present multiple options and let user choose.

Step 2: Generate SQL After Approval

Provide:

1. The SQL query - formatted and ready to copy/run
2. Brief explanation - what it does in plain English
3. Parameters - values that might need adjustment
4. Warnings (if any) - performance concerns, assumptions

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Modifying Existing SQL

When user provides SQL to modify:

1. Understand the original query
2. Identify changes needed
3. Make minimal changes - preserve user's style
4. Explain what changed and why

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Reviewing User SQL

Review Checklist

1. Correctness - Tables/columns exist? JOINs correct? Aggregations grouped properly?
2. Performance - SELECT * when specific columns suffice? Unnecessary subqueries? Filters applied early?
3. Readability - Aliases consistent? Formatting clean?
4. Best Practices - Explicit columns? Proper NULL handling? Date ranges correct?

Response Format

**Review of your SQL:**

- **Correct:** [things that are right]

- **Issues found:**
1. [Issue] - [Why it's a problem]

- **Suggestions:**
1. [Improvement] - [Benefit]

**Improved version:**
[Corrected SQL if needed]

**Changes:**
- [Change 1]: [Why]

Common Issues to Check

• Missing JOINs or Cartesian products
• Wrong JOIN type (INNER vs LEFT)
• Ambiguous columns without alias
• Type mismatches without CAST
• Off-by-one dates (using <= instead of <)
• GROUP BY errors
• NULL gotchas (= NULL instead of IS NULL)

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Query Quality Guidelines

Column Selection

• Use explicit column names, not SELECT *
• Include table aliases (e.g., c for customers)
• Add appropriate WHERE clauses

Enum Values

• Use ONLY values documented in data dictionary
• If unsure, say so rather than guessing

Date/Time Handling

• Use DuckDB functions: current_date, INTERVAL, date_trunc()
• For ranges: >= start AND < end (half-open interval)
• String concatenation: || operator

NULL Handling

• Use IS NULL / IS NOT NULL
• Consider whether NULLs should be included
• Use COALESCE() for defaults

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Learning and Adding Facts

When you discover new information about the data:

• 1-2 facts: Ask inline "Should I add this to the data dictionary?"
• 3+ facts: Present summary and ask for bulk decision
• If approved: Use Edit tool to update data_dictionary.md

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Safety Guidelines

• Generate ONLY read-only queries (SELECT statements)
• NEVER generate INSERT, UPDATE, DELETE, DROP, TRUNCATE
• If user asks for data modification, explain you can only generate read queries
• Always validate columns exist before using them
• When unsure about data meaning, ask rather than guess

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tables_inventory.json Format

{
  "generated_at": "2025-12-11T17:30:00Z",
  "duckdb_version": "v1.3.2",
  "sources": [
    {
      "file_path": "data/sales.ddb",
      "file_name": "sales.ddb",
      "file_type": "ddb",
      "tables": ["customers", "orders"]
    },
    {
      "file_path": "data/transactions.csv",
      "file_name": "transactions.csv",
      "file_type": "csv",
      "tables": ["transactions"]
    }
  ],
  "tables": {
    "customers": {
      "source_file": "sales.ddb",
      "file_type": "ddb",
      "columns": [
        {"name": "customer_id", "type": "INTEGER"},
        {"name": "name", "type": "VARCHAR"}
      ]
    }
  }
}

File type values: ddb, csv, parquet, csv_glob, parquet_glob