Warehouse UI

# 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:

```bash # 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

```bash warehouse-ui status ```

Explore Schema

```bash # 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

```bash # 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:

```bash 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):

```bash # 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

```bash warehouse-ui connections ```

Query History

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

Disconnect

```bash 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