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.

Install (Any Agent)

Run this command in your terminal

npx skills install exmergo/dex

`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 on dbt. Point it at your warehouse (or a local DuckDB file) and your
dbt project; it learns the landscape, writes and refactors your dbt transformations
and semantic models, and tells you what to fix when anything drifts. The dbt
project is the source of truth; 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, persist a draft map. Fully read-only.
Transform the dbt project: author dbt models (staging to marts) with tests
and docs, and the semantic layer on top (entities, dimensions, measures,
metrics) as dbt semantic models (MetricFlow YAML), with a free Viz preview.
Validated against a dev target, cost-guarded.
Maintain the project as it drifts: diff the warehouse and dbt against the
last snapshot, surface schema, volume, grain, and definition drift ranked by
blast radius, and propose edits.

Connectors

Cloud warehouse: Snowflake, BigQuery.
Embedded analytical: DuckDB.
Operational database: Postgres.

Credentials are discovered, never asked for: BigQuery through Application
Default Credentials (gcloud auth application-default login), Snowflake
through connections.toml, SNOWFLAKE_* env, or a dbt profile, Postgres
through pg_service.conf, DATABASE_URL, the PG* environment, or a dbt
profile. Every scan is estimated and confirmed before it spends, capped
server-side (maximum_bytes_billed on BigQuery; a per-statement statement
timeout on Snowflake and Postgres, whose budgets are warehouse-seconds with
credits alongside and database-seconds respectively), and recorded in a local
spend ledger.

Upcoming Connectors

Cloud warehouse: Databricks, AWS Redshift

The `exmergo-dex-core` package

dex also bundles the exmergo-dex-core Python package.
This is the reusable and agent-friendly package that contains all the core
explore, transform, and maintain logic. This also holds connectors and the
write logic for .dex/ which stores cache, snapshots, and query billing logs.

You can install it yourself in your projects:

pip install exmergo-dex-core

uv add exmergo-dex-core

More info in the package's README.md

Agent References

Cross-agent contract: AGENTS.md.
References (connectors, the contract, the canonical model, evaluation):
references/.

Contributing

See CONTRIBUTING.md for local setup, the Ruff lint and
format workflow, and the pre-commit hook. Every pull request into main must
pass the Lint workflow and CI before it can merge.

Community

Connect with the Analytics Engineering Community (Data Engineers welcome as well!)
and discover how Exmergo brings AI Agents to Your Data Stack.

License

Apache-2.0.