carto-explore-datawarehouse

Original：🇺🇸 English
Translated

Discover what's in the connected warehouse — schemas, tables, columns, and CARTO named sources.

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Sourcecartodb/agent-skills
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NPX Install

npx skill4agent add cartodb/agent-skills carto-explore-datawarehouse

Tags

Translated version includes tags in frontmatter
carto-clidata-warehouse-explorationnamed-sourcessql-preparationcloud-databases

SKILL.md Content

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carto-explore-datawarehouse

Before writing SQL or building maps, an agent typically needs to know what's in the warehouse. This skill covers two CARTO surfaces for that:
  • carto connections browse
     — walk the warehouse hierarchy (project → dataset → table).
  • carto connections describe
     — inspect a specific table's columns and types.
And one CARTO-specific concept:
  • Named sources — saved, parameterized SQL that maps and apps consume as if they were tables.

When to use this skill

  • You don't know which tables / schemas exist in a connection.
  • You need a column list and types before writing SQL or authoring a map.
  • The user references "the named source for X" and you need to find it.
If you already know the table and just want to query it, jump straight to 
carto-query-datawarehouse
.

Quick reference

bash
# What connections are registered?
carto connections list --json

# Walk the hierarchy (no path = top level)
carto connections browse <connection-name>

# Drill in
carto connections browse <connection-name> "carto-demo-data"
carto connections browse <connection-name> "carto-demo-data.demo_tables"

# Get columns + types for a specific table
carto connections describe <connection-name> "carto-demo-data.demo_tables.nyc_collisions"
The exact path syntax depends on the engine:
Engine
browse
path shape
BigQuery
project.dataset.table
Snowflake
DATABASE.SCHEMA.TABLE
Postgres / Redshift
schema.table
(no leading project/database)
Databricks
catalog.schema.table

What's in this skill

TopicReference
connections browse
and 
connections describe
in detail		references/connection-browse.md
Named sources — what they are, how to list and inspect themreferences/named-sources.md

Always-on guidance

  • Browse before you query. A two-second 
    connections browse
    usually saves a five-minute "table not found" loop.
  • Use 
    --page-size
     when a dataset has hundreds of tables; the default is 30.
  • describe
    returns column types     — use those types to write correct SQL (e.g. don't 
    ST_DWithin
    against a 
    STRING
    column the user mistakenly named 
    geom
    ).
  • Named sources ≠ tables. They're parameterized queries. Inspect the underlying tables before assuming a column you see in the source exists in raw form.
  • carto-demo-data
     is a public BigQuery dataset CARTO ships — 
    carto connections browse <bq-connection> "carto-demo-data"
    works on any BigQuery connection that has the right IAM, and is a fast way to validate a fresh connection without touching customer data.