dex

Most deadly errors arise from obsolete assumptions.

Frank Herbert, Children of Dune

Dex

Dex lets your agent build your analytics.

Explore your data, build models and semantic layers, and keep them in sync as schemas change.

Works with databases, data warehouses, lakehouses, and SQL engine endpoints through supported connectors.

Built by Exmergo

Install

Any agent (run in your terminal):

npx skills add exmergo/dex

Claude Code (run these inside Claude Code, one at a time):

/plugin marketplace add exmergo/exmergo-agent-plugins

/plugin install dex@exmergo

Codex:

npx skills add exmergo/dex --agent codex

What you get

• Explore your database, data warehouse, or SQL endpoint. Find the tables that matter, understand their structure, and verify how they join. Get column profiles, personal-data flags, and an ER diagram.
• Transform your dbt project and semantic layer. Create models, tests, docs, and metrics as reviewable plans, then build and verify on a dev target.
• Maintain your models and metrics as sources change. Detect drift in schemas, volumes, grain, and metric definitions, with proposed fixes to review.

Your source data stays read-only. Builds run on a dev target, query costs are quoted before spend, and every project change is a diff you review. Works in Claude Code, Codex, or any agent that loads Agent Skills.

Important Dex needs uv on your PATH. No agent installs it for you. Run brew install uv or pipx install uv, or see the uv installation docs for other platforms. uv supplies Python and the engine. Use uv 0.13.4 or later (uv self update): earlier releases still trust the emSign CA roots withdrawn after their keys were compromised.

In Claude Code the skills appear as /dex:explore, /dex:transform, and /dex:maintain and trigger on matching intent. Update later with /plugin marketplace update exmergo. Codex also gets a native plugin, with no MCP server needed. Any other agent can be pointed at AGENTS.md.

Run Dex from your project directory, so it finds your .dex/config.yml and keeps its cache next to your project. Dex needs an agent with a local shell, so it does not run in claude.ai chat.

Tip Skip the first-run wait. The first command installs the engine, which takes tens of seconds on a cold uv cache. Ask your agent to warm Dex once after installing, or run the command below in a CI or container build step. Add --connector snowflake (or any connector) to warm one before a project exists.

uv run --no-project --script skills/<skill>/scripts/run.py --warm

Why

Writing SQL is one part of analytics engineering. Your agent also needs to:

• understand the schema without starting from scratch every session,
• find the relevant tables among thousands,
• account for what a query costs,
• keep personal data out of its context,
• keep models and metric definitions coherent as their sources change.

Dex gives the agent a repeatable explore, transform, maintain loop, with guardrails enforced in the engine.

Try it in three commands

No existing database, no credentials, no cloud account, and no network once the package is installed.

pip install "exmergo-dex-core[duckdb]"
dex demo
dex explore map

dex demo writes a small e-commerce DuckDB database (dex_demo.duckdb, 7 tables, 29,512 rows) and a .dex/config.yml into the current directory, so every command after it runs with no flags. It refuses rather than overwrite anything. The data comes from a pinned seed, so what you see is what is written here.

explore map maps all 7 tables, infers 5 joins, flags 6 columns as personal data, and reports 6 data-quality findings. Every command answers with one JSON envelope. An excerpt, for one table:

{
  "identifier": "dex_demo.main.order_items",
  "row_count": 14000,
  "grain": null,
  "data_quality": [
    "order_item_id is not unique: 13000 distinct over 14000 rows (1000 rows would have to be removed for it to be unique, so it is unique for 92.9% of rows); joins on it will fan out"
  ]
}

It is broken on purpose

A first run that reports a clean bill of health teaches you nothing, so the demo ships with real defects. Dig into them:

dex explore profile order_items products
dex explore relationships --verify
dex explore query "select email from customers"

• A broken grain. A batch was loaded twice, so order_item_id is unique for only 92.9% of rows. Dex reports the duplicates and leaves the grain unknown rather than guessing a key.
• A key that mixes id schemes. sku is 90% numeric, 10% 32-character hexadecimal (md5-shaped). Cast it to a number and you silently drop 10% of products.
• A join that looks right and is not. web_events.customer_id shares a name with the CRM's key, but verification finds 100% of its values have no match.
• A refusal. The query firewall will not put customers.email into context. select count(distinct email) from customers runs, because a statistic is not a value.

There is also a table an interrupted load left empty, two columns whose declared type contradicts their content, and two designed PII false positives. See the dex demo reference.

Note DuckDB is free and local, so nothing here asks you to confirm a spend. On a metered connector the same commands return an estimate first and run only once you agree to it.

How it works

flowchart LR
  Agent[Coding agent] --> Skills[Dex skills]
  Skills --> Engine[exmergo-dex-core engine]
  Engine -->|SELECT-only SQL, cost-capped| Database[(Your database, data warehouse, or SQL endpoint)]
  Engine -->|dbt builds, dev target only| Database
  Engine -->|one sanitized JSON envelope| Agent
  Engine -->|plans as diffs| Repo[Your repository: dbt project + semantic layer]
  Engine <--> Cache[.dex/ cache and snapshot]

The skills connect your agent to the exmergo-dex-core engine, which holds the logic and guardrails behind a small command contract. Your dbt project and semantic-layer definitions live in your repository as reviewable files. The .dex/ directory holds Dex's cache and snapshots.

Explore

Find the tables that matter, profile their columns, and verify joins before writing SQL. Answer ad-hoc questions through a query firewall that checks for personal data, read and query the semantic layer, and map relationships as a Mermaid ER diagram. Exploration is read-only and needs no dbt project. Skill · methodology · commands

Transform

Build and refactor dbt models from staging to marts, with tests, docs, and declared grain and population. Define semantic models and metrics over them, or work with a native semantic layer without a dbt project. Review each stored plan before applying it, then build and verify on a dev target. Skill · what Dex writes · commands

Maintain

Compare database schemas and profiles, the dbt project, and the semantic layer with the last snapshot. See which models and metrics a change affects and review the proposed edits. maintain verify also checks a project as it stands, with no baseline needed. Skill · commands

Tip Give every agent in your repository the same procedure. dex procedure --block prints the procedure every Dex skill follows, ready to paste into your CLAUDE.md or AGENTS.md, so an agent follows it even before it loads a skill. Dex never writes the file; dex procedure --check CLAUDE.md tells you when an upgrade has made your copy stale. See dex procedure.

Connectors

Connector	Type	Self-hostable	Extra / --connector	Cost guardrail	Connection details
Snowflake	Cloud warehouse	❌	snowflake	Warehouse-seconds, with credits	connections.toml, SNOWFLAKE_* env, or a dbt profile
BigQuery	Cloud warehouse	❌	bigquery	Bytes scanned	Application Default Credentials
Databricks	Lakehouse via SQL endpoint	❌	databricks	Warehouse-seconds, with DBUs	databricks auth login, DATABRICKS_* env, or a dbt profile
Amazon Redshift	Cloud warehouse, Serverless-first	❌	redshift	Compute-seconds, with RPU-hours	The AWS credential chain or REDSHIFT_* env
Postgres	Operational database	✅	postgres	Database-seconds	pg_service.conf, DATABASE_URL, PG* env, or a dbt profileAlso works with Supabase, Neon, and more managed Postgres platforms