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Quickstart

Step 1: Install

npm install -g altimate-code

Step 2: Connect Your LLM

altimate        # Launch the TUI
/connect        # Interactive setup

Or set an environment variable and skip the prompt:

export ANTHROPIC_API_KEY=sk-ant-...
altimate

Step 3: Connect Your Warehouse

Option A: Auto-detect from dbt profiles

If you have ~/.dbt/profiles.yml configured:

/discover

Altimate reads your dbt profiles and creates warehouse connections automatically. You'll see output like:

Found dbt project: jaffle_shop (dbt-snowflake)
Found profile: snowflake_prod → Added connection 'snowflake_prod'
Indexing schema... 142 tables, 1,847 columns indexed

Option B: Manual configuration

Add to .altimate-code/connections.json in your project root:

{
  "snowflake": {
    "type": "snowflake",
    "account": "xy12345.us-east-1",
    "user": "dbt_user",
    "password": "${SNOWFLAKE_PASSWORD}",
    "warehouse": "TRANSFORM_WH",
    "database": "ANALYTICS",
    "schema": "PUBLIC",
    "role": "TRANSFORMER"
  }
}

{
  "bigquery": {
    "type": "bigquery",
    "project": "my-project-id",
    "keyfile": "~/.config/gcloud/application_default_credentials.json"
  }
}

{
  "postgres": {
    "type": "postgres",
    "host": "localhost",
    "port": 5432,
    "database": "analytics",
    "user": "postgres",
    "password": "${POSTGRES_PASSWORD}"
  }
}

{
  "local": {
    "type": "duckdb",
    "database": "./data/analytics.duckdb"
  }
}

Then index the schema for autocomplete and analysis:

/schema-index snowflake

Step 4: Your First Workflow — NYC Taxi Cab Analytics

Try this end-to-end example. Paste this prompt into the TUI:

Take the New York City taxi cab public dataset, bring up a DuckDB instance,
and build a dashboard showing areas of maximum coverage and lowest coverage.
Set up a complete dbt project with staging, intermediate, and mart layers,
and create an Airflow DAG to orchestrate the pipeline.

What altimate does:

  1. Downloads the NYC TLC trip data into a local DuckDB instance
  2. Scaffolds a full dbt project with proper directory structure: 
    nyc_taxi/
  models/
    staging/
      stg_yellow_trips.sql
      stg_taxi_zones.sql
    intermediate/
      int_trips_by_zone.sql
      int_zone_coverage_stats.sql
    marts/
      fct_zone_coverage.sql
      dim_zones.sql
  seeds/
    taxi_zone_lookup.csv
  dbt_project.yml
  profiles.yml              # points to DuckDB
  3. Generates mart models that aggregate pickup/dropoff counts per zone, rank zones by trip volume, and classify them as high-coverage or low-coverage
  4. Creates an Airflow DAG (dags/nyc_taxi_pipeline.py) with tasks for data ingestion, dbt run, dbt test, and dashboard generation
  5. Builds an interactive dashboard visualizing zone coverage across NYC — top zones, bottom zones, and geographic distribution

This single prompt exercises warehouse connections, dbt scaffolding, SQL generation, orchestration wiring, and visualization — the full altimate toolkit.

Skill Discovery: What Can I Do?

Type / in the TUI to see all available skills. Here's a quick reference for common tasks:

I want to... Skill Example
Optimize a slow query /query-optimize /query-optimize SELECT * FROM big_table
Review SQL before merging /sql-review /sql-review models/staging/stg_orders.sql
Check Snowflake costs /cost-report /cost-report (last 30 days)
Scan for PII exposure /pii-audit /pii-audit (full schema) or /pii-audit models/marts/
Debug a dbt error /dbt-troubleshoot Paste the error message
Add tests to a model /dbt-test /dbt-test models/staging/stg_orders.sql
Document a model /dbt-docs /dbt-docs models/marts/fct_revenue.sql
Analyze downstream impact /dbt-analyze /dbt-analyze stg_orders (before refactoring)
Create a new dbt model /dbt-develop Create a staging model for the raw_orders source
Translate SQL dialects /sql-translate /sql-translate snowflake bigquery SELECT DATEADD(...)
Check migration safety /schema-migration /schema-migration migrations/V003__alter_orders.sql
Teach a pattern /teach /teach @models/staging/stg_orders.sql

Pro tip: You don't need to memorize these. Just describe what you want in plain English — the agent routes to the right skill automatically.

What's Next

  • Setup — Warehouses, LLM providers, agent modes, skills, and permissions
  • Examples — End-to-end walkthroughs for common data engineering tasks
  • Interfaces — TUI, CLI, CI, IDE, and GitHub/GitLab integrations