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Carto Query Datawarehouse

skill-cartodb-agent-skills-carto-query-datawarehouse · by CartoDB

Write spatial SQL against the connected warehouse — dialect-specific guidance, performance defaults, and CARTO's query/job execution model.

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Install

$ agentstack add skill-cartodb-agent-skills-carto-query-datawarehouse

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

carto-query-datawarehouse

Run SQL — spatial or otherwise — against any connection CARTO has registered. The CLI exposes two surfaces:

  • carto sql querySELECT queries that return rows. Has a 1-minute timeout. Optional client-side caching.
  • carto sql job — DDL/DML jobs (CREATE TABLE AS SELECT, UPDATE, INSERT). No timeout; polls until done; returns no rows.

Plus a sibling for usage analytics:

  • carto activity query — DuckDB-backed SQL over downloaded CARTO activity data. Local execution, separate from warehouse SQL.

When to use this skill

  • The user wants to count rows, run an exploratory SELECT, or build a transformation.
  • The user is debugging slow / failing SQL.
  • The agent needs to materialize an intermediate table before authoring a map.
  • The user wants to run an ad-hoc spatial join, buffer, or H3 aggregation.

Quick reference

# Read query (returns rows; 1-min timeout)
carto sql query  "SELECT * FROM dataset.table LIMIT 10"

# Long-running job (DDL/DML; polls to completion; no rows back)
carto sql job  "CREATE TABLE my_ds.out AS SELECT ..."

# From file
carto sql query  --file query.sql

# Piped
echo "SELECT 1" | carto sql query 

| Use | Command | |---|---| | Exploratory SELECT (small result, fast) | sql query | | Cached SELECT (deterministic, 1y TTL) | sql query ... --cache | | CREATE TABLE AS SELECT, large UPDATE | sql job | | 5+ minute aggregation | sql job (queries time out at 1 min) |

--cache switches to GET with a cached response (1 year, 1 min timeout). Use only for queries that are deterministic and small enough for a URL.

What's in this skill

| Topic | Reference | |---|---| | sql query vs sql job, caching, timeouts | [references/sql-jobs-and-caching.md](references/sql-jobs-and-caching.md) | | Spatial SQL idioms — BigQuery dialect | [references/spatial-sql-bigquery.md](references/spatial-sql-bigquery.md) | | Spatial SQL idioms — Snowflake dialect | [references/spatial-sql-snowflake.md](references/spatial-sql-snowflake.md) | | Spatial SQL idioms — Postgres / PostGIS dialect | [references/spatial-sql-postgres.md](references/spatial-sql-postgres.md) | | Querying CARTO activity data (local DuckDB) | [references/activity-queries.md](references/activity-queries.md) |

Always-on guidance

  • Always specify a connection. ` in sql query ... is the connection name from connections list`, not the warehouse project ID.
  • Use --json when an agent will parse the output. Default text output is for humans.
  • Prefer sql job for any query that might exceed 60 s. sql query has a hard 1-minute server-side timeout regardless of the user's patience.
  • Don't SELECT * on warehouse tables blindly. Spatial tables can be 100M+ rows; always project columns and add LIMIT for exploration.
  • Dialect mismatch is the #1 source of confusion. ST_DWithin exists in PostGIS and Redshift, but is ST_DWITHIN in Snowflake and lives under ST_DWithin in BigQuery's bigquery-public-data.geo_us_boundaries style. The reference per dialect explains the canonical form.
  • For activity-data analysis (who edited what, quota usage, login patterns), use activity query — it runs DuckDB SQL locally over downloaded data. See [references/activity-queries.md](references/activity-queries.md).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.