# Quick Start

This guide walks through the four core operations: inspecting the credit ontology, computing expected credit losses, discovering causal structure, and launching the dashboard.

## 1. Inspect the Credit Ontology

Orxaq ships with a 19-variable credit ontology covering macroeconomic indicators, borrower characteristics, and loan performance metrics.

```bash
orxaq credit ontology --show
```

This prints all 19 variables with their causal roles (exogenous, mediator, outcome) and the edges connecting them.

## 2. Compute Expected Credit Loss

Run CECL-compliant expected credit loss computation on synthetic data:

```bash
orxaq credit ecl
```

Output includes total ECL, per-segment breakdown, forecast horizon, and confidence bands. Add `--json` for machine-readable output:

```bash
orxaq --json credit ecl
```

## 3. Discover Causal Structure

Run causal discovery on a set of variables using multi-algorithm consensus:

```bash
orxaq discover --consensus --variables unemployment_rate,income,credit_score
```

This runs the PC algorithm (constraint-based) and GES (score-based), then returns edges where both algorithms agree.

## 4. Launch the Dashboard

Start the Gamma Observatory — a local web dashboard with DAG visualization:

```bash
orxaq serve
```

Open `http://localhost:8741` to see the DAG, ECL metrics, stress test results, fair lending analysis, and audit trail.

## Try It: JSON Pipeline

Pipe structured output between commands:

```bash
orxaq --json validate
```

Returns d-separation validation results as JSON with `total_implications`, `violations`, and `passed` fields.
