AltimateAI/altimate-code for Claude Code | Awesome Skills

🛠 Agent ToolsClaude CodeCodexCursorMCP

altimate-code

Open-source agentic data engineering harness for dbt, SQL, and cloud warehouses. 100+ tools, 10 warehouses, AI-powered.

AltimateAITypeScriptnpm

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What the docs cover

Install

Quick demo

Overview

Review altimate-code through its repository signals, setup path, and structured documentation.

Key Features

Documentation available at https://docs.altimate.sh
Built around typescript tooling

Best For

Agent Tool research

Use Cases

Agentic Data Engineering

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Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.

Aanthropicsabout 2 months ago

Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows -- all through natural language commands. Use it in your terminal, IDE, or tag @claude on Github.

**Learn more in the [official documentation](https://code.claude.com/docs/en/overview)**.

<img src="./demo.gif" />

## Get started
> [!NOTE]
> Installation via npm is deprecated. Use one of the recommended methods below.

For more installation options, uninstall steps, and troubleshooting, see the [setup documentation](https://code.claude.com/docs/en/setup).

1. Install Claude Code:

    **MacOS/Linux (Recommended):**

Install

npm install -g altimate-code

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npm install -g altimate-code

Or via curl on macOS/Linux (installs the altimate binary to ~/.altimate/bin):

curl -fsSL https://www.altimate.sh/install | bash

On Windows, install the same self-contained binary (to %USERPROFILE%\.altimate\bin) from PowerShell — no Node required:

powershell -c "irm https://www.altimate.sh/install.ps1 | iex"

The standalone install drops a single self-contained binary named altimate. The npm install exposes both altimate and altimate-code on PATH; the standalone install only exposes altimate. Alpine Linux (musl) and Windows on ARM64 are not currently supported by the standalone binary — use apk add gcompat on Alpine, or use WSL on Windows-on-ARM.

For GitHub, install the Altimate Code App to select repositories for interactive agent tasks. Automatic dbt pull-request reviews use the deterministic GitHub Action documented below; see the public demo PRs before installing it in your own repository.

Then — in order:

Step 1: Configure your LLM provider (required before anything works):

altimate        # Launch the TUI
/connect        # Interactive setup — choose your provider and enter your API key

Or set an environment variable directly:

export ANTHROPIC_API_KEY=your_key   # Anthropic Claude
export OPENAI_API_KEY=your_key      # OpenAI

Step 2 (optional): Auto-detect your data stack (read-only, safe for production connections):

altimate /discover

/discover auto-detects dbt projects, warehouse connections (from profiles.yml — checks DBT_PROFILES_DIR, project directory, then <home>/.dbt/; plus Docker and environment variables), and installed tools (dbt, sqlfluff, airflow, dagster, and more). Skip this and start building — you can always run it later.

Headless / scripted usage: altimate --yolo auto-approves all permission prompts. Not recommended with live warehouse connections.

Zero additional setup. One command install.

# Auto-detect your data stack (dbt projects, warehouse connections, installed tools)
> /discover

# Analyze a query for anti-patterns and optimization opportunities
> Analyze this query for issues: SELECT * FROM orders JOIN customers ON orders.id = customers.order_id

# Translate SQL across dialects
> /sql-translate this Snowflake query to BigQuery: SELECT DATEADD(day, 7, current_date())

# Generate dbt tests for a model
> /generate-tests for models/staging/stg_orders.sql

# Get a cost report for your Snowflake account
> /cost-report

# Compare a Snowflake table to a BigQuery copy, row-by-row, without moving data
> /data-parity prod.orders (Snowflake) vs. analytics.orders (BigQuery), id key

# Generate dbt 1.8 unit tests for a model with CASE/WHEN and JOINs
> /dbt-unit-tests for models/marts/fct_revenue.sql