Warehouse UI — Database Query Tool

Use this skill to connect to databases, explore schemas, run queries, estimate costs, and generate SQL from natural language.

Installation

Download from GitHub Releases: https://github.com/olegnazarov23/warehouse-ui/releases

• macOS: Download the DMG, drag to Applications, then add to PATH: ln -s /Applications/warehouse-ui.app/Contents/MacOS/warehouse-ui /usr/local/bin/warehouse-ui
• Windows: Run the installer EXE, it adds to PATH automatically

Supported Databases

• PostgreSQL
• MySQL
• SQLite
• BigQuery (with cost projection)
• MongoDB

Connect to a Database

Before running queries, establish a connection:

# From a connection URL
warehouse-ui connect --url "postgres://user:pass@localhost:5432/mydb"

# With explicit parameters
warehouse-ui connect --type postgres --host localhost:5432 --database mydb --user admin --password secret

# SQLite (local file)
warehouse-ui connect --type sqlite --database /path/to/data.db

# BigQuery (service account)
warehouse-ui connect --type bigquery --database my-gcp-project --option sa_json_path=/path/to/sa.json

# MySQL
warehouse-ui connect --url "mysql://user:pass@localhost:3306/mydb"

Check Connection Status

warehouse-ui status

Explore Schema

# List all databases
warehouse-ui schema list-databases

# List tables in a database
warehouse-ui schema list-tables --database mydb

# Describe a table (columns, types, nullability)
warehouse-ui schema describe users --database mydb

Run Queries

# SQL as argument
warehouse-ui query "SELECT * FROM users LIMIT 10"

# With explicit limit
warehouse-ui query --sql "SELECT count(*) FROM orders WHERE created_at > '2024-01-01'" --limit 1000

# From a SQL file
warehouse-ui query --file path/to/report.sql

Output is JSON with columns, rows, row count, duration, and (for BigQuery) bytes processed and cost.

Cost Estimation (Dry Run)

Check query cost before executing — especially useful for BigQuery:

warehouse-ui dry-run "SELECT * FROM big_dataset.events WHERE date > '2024-01-01'"

Returns: estimated bytes, estimated cost (USD), statement type, referenced tables, and warnings.

AI-Powered Queries

Generate SQL from natural language using a configured AI provider (set OPENAI_API_KEY or ANTHROPIC_API_KEY):

# Generate SQL only
warehouse-ui ai "show me the top 10 customers by total revenue"

# Generate and execute
warehouse-ui ai "find all orders from last week that were cancelled" --execute

List Saved Connections

warehouse-ui connections

Query History

warehouse-ui history --limit 10
warehouse-ui history --search "SELECT"

Disconnect

warehouse-ui disconnect

Output Format

All commands output JSON to stdout by default. Add --format table for human-readable output. Errors are JSON on stderr with exit code 1.

Environment Variables

• DATABASE_URL — Auto-connect without explicit connect step (supports postgres://, mysql://, sqlite://, mongodb://)
• OPENAI_API_KEY — Required for ai command with OpenAI
• ANTHROPIC_API_KEY — Required for ai command with Anthropic

Tips

• Set DATABASE_URL to skip the connect step entirely
• Use schema describe <table> to understand table structure before querying
• Use dry-run on BigQuery to check costs before executing expensive queries
• Use --limit to control result size for large tables
• Use connections to see databases already configured in the desktop app