dex

Built by Exmergo · The AI Stack for Your Data Stack.

Install (Any Agent)

Run this command in your terminal

npx skills add exmergo/dex

Install (Claude Code)

Run these commands inside Claude Code one at a time

/plugin marketplace add exmergo/exmergo-agent-plugins

/plugin install dex@exmergo

Update later with /plugin marketplace update exmergo. The skills appear as /dex:explore, /dex:transform, and /dex:maintain and auto-trigger on matching intent. Ask it to warm dex once after installing, so the first real command does not wait for the engine to install (see Prerequisite: uv).

dex: the agent-native analytics engineering toolkit

dex is analytics engineering for Claude Code and any agent: data warehouse exploration, dbt transformation and semantic modeling, and schema-drift maintenance. Point it at your warehouse (or a local DuckDB file, or the one dex demo generates for you) and at your repository; it learns the landscape, writes and refactors your dbt transformations and your semantic layer, and tells you what to fix when anything drifts. Your repository is the source of truth, on two independent axes: the transformation project, which is dbt, and the semantic layer, which is dbt's own, a hosted dbt Cloud deployment, or native Apache Ossie documents that need no dbt project at all. Every change is a reviewable diff. Read-only against your data.

It closes the gap a general coding agent still has: agents re-learn the schema each session, have no strategy for thousands of tables, are blind to warehouse cost, will pull sensitive data into context, do not treat a dbt project as a first-class object, and have no concept of a semantic model to keep coherent over time. dex owns exactly that loop.

The loop

Explore. Transform. Maintain. (ETM)

• Explore an unfamiliar warehouse: rank what matters, profile selectively, infer and verify joins, answer ad-hoc questions with guarded SQL probes behind a PII-aware query firewall, read the semantic layer as the object graph it is (semantic models, metrics with their composition, measures, dimensions, and the declared join graph, each resolved to the relation and column behind it, and the whole of it searchable and budgeted), read a dimension's value domain before filtering on it, and query its metrics (locally via MetricFlow or against a hosted dbt Cloud deployment; a native Apache Ossie layer is catalog-first and refuses a metric query by name, because the format specifies interchange metadata and no query runtime), and render the map as a Mermaid ER diagram that draws the joins the semantic layer declares and never claims a cardinality the data has not proven. Persist a draft map. Fully read-only.

• Transform the project: author dbt models (staging to marts) with tests and docs, and the semantic layer on top, either as dbt semantic models (MetricFlow YAML: entities, dimensions, measures, metrics) or as native Apache Ossie documents written back byte for byte, with a free Viz preview. Validated against a dev target, cost-guarded.