Auto-detect your data stack (dbt projects, warehouse connections, installed tools)

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**The open-source data engineering harness.**

The intelligence layer for data engineering AI — 100+ deterministic tools for SQL analysis,
column-level lineage, dbt, FinOps, and warehouse connectivity across every major cloud platform.

Run standalone in your terminal, embed underneath Claude Code or Codex, or integrate
into CI pipelines and orchestration DAGs. Precision data tooling for any LLM.

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---

## Install

```bash
npm install -g altimate-code
```

Then — in order:

**Step 1: Configure your LLM provider** (required before anything works):
```bash
altimate        # Launch the TUI
/connect        # Interactive setup — choose your provider and enter your API key
```

Or set an environment variable directly:
```bash
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):
```bash
altimate /discover
```

`/discover` auto-detects dbt projects, warehouse connections (from `~/.dbt/profiles.yml`, Docker, 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.

## Why a specialized harness?

General AI coding agents can edit SQL files. They cannot *understand* your data stack.
altimate gives any LLM a deterministic data engineering intelligence layer —
no hallucinated SQL advice, no guessing at schema, no missed PII.

| Capability | General coding agents | altimate |
|---|---|---|
| SQL anti-pattern detection | None | 19 rules, confidence-scored |
| Column-level lineage | None | Automatic from SQL, any dialect |
| Schema-aware autocomplete | None | Live-indexed warehouse metadata |
| Cross-dialect SQL translation | None | Snowflake ↔ BigQuery ↔ Databricks ↔ Redshift |
| FinOps & cost analysis | None | Credits, expensive queries, right-sizing |
| PII detection | None | 30+ regex patterns, 15 categories |
| dbt integration | Basic file editing | Manifest parsing, test gen, model scaffolding, lineage |
| Data visualization | None | Auto-generated charts from SQL results |
| Observability | None | Local-first tracing of AI sessions and tool calls |

> **Benchmarked precision:** 100% F1 on SQL anti-pattern detection (1,077 queries, 19 rules, 0 false positives).
> 100% edge-match on column-level lineage (500 queries, 13 categories).
> [See methodology →](experiments/BENCHMARKS.md)

**What the harness provides:**
- **SQL Intelligence Engine** — deterministic SQL parsing and analysis (not LLM pattern matching). 19 rules, 100% F1, 0 false positives. Built for data engineers who've been burned by hallucinated SQL advice.
- **Column-Level Lineage** — automatic extraction from SQL across dialects. 100% edge-match on 500 benchmark queries.
- **Live Warehouse Intelligence** — indexed schemas, query history, and cost data from your actual warehouse. Not guesses.
- **dbt Native** — manifest parsing, test generation, model scaffolding, medallion patterns, impact analysis
- **FinOps** — credit consumption, expensive query detection, warehouse right-sizing, idle resource cleanup
- **PII Detection** — 15 categories, 30+ regex patterns, enforced pre-execution

**Works seamlessly with Claude Code and Codex.** Use `/configure-claude` or `/configure-codex` to set up integration in one step. altimate is the data engineering tool layer — use it standalone in your terminal, or mount it as the harness underneath whatever AI agent you already run. The two are complementary.

altimate-code is a fork of [OpenCode](https://github.com/anomalyco/opencode) rebuilt for data teams. Model-agnostic — bring your own LLM or run locally with Ollama.

## Quick demo

```bash
# 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 

... (truncated)