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Setup

You need: npm 8+. An API key for any supported LLM provider.

Step 1: Install

npm install -g altimate-code

Zero additional setup. One command install.

Step 2: Configure Your LLM

altimate        # Launch the TUI
/connect        # Choose your provider and enter your API key

Or set an environment variable:

export ANTHROPIC_API_KEY=your-key-here   # Anthropic Claude (recommended)
export OPENAI_API_KEY=your-key-here      # OpenAI

Minimal config file option (altimate-code.json in your project root):

{
  "provider": {
    "anthropic": {
      "apiKey": "{env:ANTHROPIC_API_KEY}"
    }
  },
  "model": "anthropic/claude-sonnet-4-6"
}

Changing your LLM provider

Switch providers at any time by updating the provider and model fields in altimate-code.json:

{
  "provider": {
    "anthropic": {
      "apiKey": "{env:ANTHROPIC_API_KEY}"
    }
  },
  "model": "anthropic/claude-sonnet-4-6"
}

{
  "provider": {
    "openai": {
      "apiKey": "{env:OPENAI_API_KEY}"
    }
  },
  "model": "openai/gpt-4o"
}

{
  "provider": {
    "bedrock": {
      "region": "us-east-1",
      "accessKeyId": "{env:AWS_ACCESS_KEY_ID}",
      "secretAccessKey": "{env:AWS_SECRET_ACCESS_KEY}"
    }
  },
  "model": "bedrock/anthropic.claude-sonnet-4-6-v1"
}

{
  "provider": {
    "azure": {
      "apiKey": "{env:AZURE_OPENAI_API_KEY}",
      "baseURL": "https://your-resource.openai.azure.com/openai/deployments/your-deployment"
    }
  },
  "model": "azure/gpt-4o"
}

{
  "provider": {
    "google": {
      "apiKey": "{env:GOOGLE_API_KEY}"
    }
  },
  "model": "google/gemini-2.5-pro"
}

{
  "provider": {
    "ollama": {
      "baseURL": "http://localhost:11434"
    }
  },
  "model": "ollama/llama3.1"
}

{
  "provider": {
    "openrouter": {
      "apiKey": "{env:OPENROUTER_API_KEY}"
    }
  },
  "model": "openrouter/anthropic/claude-sonnet-4-6"
}

You can also set a smaller model for lightweight tasks like summarization:

{
  "model": "anthropic/claude-sonnet-4-6",
  "small_model": "anthropic/claude-haiku-4-5-20251001"
}

Step 3: Connect Your Warehouse

Auto-discover with /discover

Skip this step if you want to work locally. You can always run /discover later.

altimate /discover

Auto-detects your dbt projects, warehouse credentials from ~/.dbt/profiles.yml, running Docker containers, and environment variables (SNOWFLAKE_ACCOUNT, PGHOST, DATABASE_URL, etc.).

Manual configuration

Add a warehouse connection to .altimate-code/connections.json:

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

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

{
  "databricks": {
    "type": "databricks",
    "server_hostname": "dbc-abc123.cloud.databricks.com",
    "http_path": "/sql/1.0/warehouses/abcdef",
    "access_token": "{env:DATABRICKS_TOKEN}",
    "catalog": "main",
    "schema": "default"
  }
}

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

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

{
  "redshift": {
    "type": "redshift",
    "host": "my-cluster.abc123.us-east-1.redshift.amazonaws.com",
    "port": 5439,
    "database": "analytics",
    "user": "admin",
    "password": "{env:REDSHIFT_PASSWORD}"
  }
}

All warehouse types support SSH tunneling for bastion hosts. See the Warehouses reference for full options including key-pair auth, IAM roles, and ADC.

Verify your connection:

> warehouse_test snowflake
✓ Connected successfully

Step 4: Choose an Agent Mode

altimate ships with specialized agent modes, each with its own tool permissions:

Mode Access Use when you want to...
Builder Read/Write Create and modify SQL, dbt models, pipelines. SQL writes prompt for approval.
Analyst Read-only Explore production data safely, run cost analysis. SQL writes denied entirely.
Plan Minimal Plan an approach before switching to builder to execute it

Switch modes in the TUI:

/agent analyst

Or from the CLI:

altimate --agent analyst

The Analyst mode is production-safe — it blocks INSERT, UPDATE, DELETE, and DROP statements at the harness level. The Builder mode has full read/write access for creating and editing SQL and dbt files.

Step 5: Select Skills

Skills are reusable prompt templates for common workflows. Type / in the TUI to browse all available skills:

Skill Purpose
/query-optimize Optimize slow queries with anti-pattern detection
/sql-review SQL quality gate with grading
/sql-translate Cross-dialect SQL translation
/cost-report Snowflake/Databricks cost analysis
/pii-audit Scan for PII exposure
/dbt-develop Scaffold new dbt models
/dbt-test Generate dbt tests
/dbt-docs Generate dbt documentation
/dbt-analyze Column-level lineage and impact analysis
/dbt-troubleshoot Debug dbt errors
/data-viz Interactive dashboards and visualizations
/teach Teach patterns from example files
/train Load standards from documents

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

Custom skills

Add your own skills as Markdown files in .altimate-code/skill/:

---
name: cost-review
description: Review SQL queries for cost optimization
---

Analyze the SQL query for cost optimization opportunities.
Focus on: $ARGUMENTS

Skills are loaded from these paths (highest priority first):

  1. .altimate-code/skill/ (project)
  2. ~/.altimate-code/skills/ (global)
  3. Custom paths via config:
{
  "skills": {
    "paths": ["./my-skills", "~/shared-skills"]
  }
}

Step 6: Configure Permissions

Governance is enforced at the harness level, not via prompts. Every tool has a permission level: allow, ask, or deny.

Per-agent permissions

Set tool permissions for each agent mode in altimate-code.json:

{
  "agent": {
    "analyst": {
      "permission": {
        "write": "deny",
        "edit": "deny",
        "bash": {
          "dbt docs generate": "allow",
          "*": "deny"
        }
      }
    },
    "builder": {
      "permission": {
        "write": "allow",
        "edit": "allow",
        "bash": {
          "dbt *": "allow",
          "rm -rf *": "deny"
        }
      }
    }
  }
}

Project rules with AGENTS.md

Define project-wide conventions in an AGENTS.md file at your project root. These rules are automatically loaded into every agent's system prompt:

# Project Rules

- All staging models must be prefixed with `stg_`
- Never run queries without a WHERE clause on production tables
- Use `ref()` instead of hardcoded table names in dbt models
- All new models require at least one unique test and one not_null test

Default permissions by agent mode

Agent File writes SQL writes Bash Training
Builder allow ask (prompts for approval) ask allow
Analyst deny deny (blocked entirely) deny (safe commands auto-allowed) allow
Plan deny deny deny deny

Step 7: Build Your First Artifact

In the TUI, paste this prompt:

Build a NYC taxi analytics dashboard using BigQuery public data and dbt
for transformations. Include geographic demand analysis with
pickup/dropoff hotspots, top routes, airport traffic, and borough
comparisons. Add revenue analytics with fare breakdowns, fare
distribution, tip analysis, payment trends, and revenue-per-mile
by route.

What's Next