Cargo Storage

Cargo CLI — Storage

Data layer management: inspecting and modifying models, datasets, columns, relationships, and records, and running SQL queries against workspace storage.

> See references/response-shapes.md for full JSON response structures. > See references/troubleshooting.md for common errors and how to fix them. > See references/examples/models.md for model CRUD, DDL inspection, and schema discovery examples. > See references/examples/datasets.md for dataset listing and navigation examples. > See references/examples/columns.md for column creation and management examples. > See references/examples/queries.md for storage query execute / storage query download SQL examples (WHERE, aggregations, joins, pagination, exports). > See references/examples/ingest-webhook.md for ingest (webhook-fed) models — deriving the webhook URL and POSTing records.

Prerequisites

See [../cargo/references/prerequisites.md](../cargo/references/prerequisites.md) for install, login (--oauth / --token), JSON output conventions, and error shapes. Verify the session with cargo-ai whoami before running any of the commands below.

Discover resources first

Always list before inspecting or modifying.

cargo-ai storage dataset list              # all datasets (uuid, slug)
cargo-ai storage model list                # all models (uuid, name, slug, columns)
cargo-ai storage model list --dataset-uuid    # models in a specific dataset

Retrieve in the UI: models live at app.getcargo.io/workspaces//models/. Get ` from cargo-ai whoami under workspace.uuid`.

Quick reference

cargo-ai storage model list
cargo-ai storage model get 
cargo-ai storage model get-ddl 
cargo-ai storage dataset list
cargo-ai storage column list --model-uuid 
cargo-ai storage relationship list --model-uuid 
cargo-ai storage record list --model-uuid 
cargo-ai storage query execute "SELECT * FROM default.companies LIMIT 10"
cargo-ai storage query download --query "SELECT * FROM default.companies"

Models

Models are structured tables in your workspace (e.g. Companies, Contacts).

# List all models
cargo-ai storage model list

# List models in a dataset
cargo-ai storage model list --dataset-uuid 

# Get a single model (includes columns)
cargo-ai storage model get 

# Get the DDL (full schema, table name and SQL dialect)
cargo-ai storage model get-ddl 
# → Useful for column discovery and SQL dialect (BigQuery vs Snowflake) before writing queries

# Create a model
cargo-ai storage model create \
  --slug contacts \
  --name "Contacts" \
  --dataset-uuid  \
  --extractor-slug  \
  --config '{}'

# Update a model
cargo-ai storage model update --uuid  --name "New Name"

# Remove a model
cargo-ai storage model remove 

Querying: Use cargo-ai storage query execute "" (or storage query download --query "" for full exports) to run SQL against storage. Tables are referenced as . (e.g. default.companies) and rewritten to the underlying storage table under the hood. See [Query with SQL](#query-with-sql) below.

Ingest models (webhook-fed)

A model whose extractor has mode.kind === "ingest" — http.listenHook and friends — is filled by pushing records to Cargo. The app shows a "Webhook URL" on the model settings screen; no CLI command or API field returns it, but it's assembled from values the CLI already exposes:

/v1/models//records/ingest?token=

MODEL_UUID=
BASE=$(cargo-ai whoami | jq -r '.baseUrl')
TOKEN=$(cargo-ai workspaceManagement token list | jq -r '.tokens[0].token')
echo "$BASE/v1/models/$MODEL_UUID/records/ingest?token=$TOKEN"

Check the extractor's mode first — when it reports "autoIngest": true (calendly, smartlead, instantlyV2, heyReach, datachimp, cargo signals) Cargo registers the hook with the provider itself and the URL must not be handed out. Full flow, payload shapes, and limits: references/examples/ingest-webhook.md.

Datasets

Datasets are logical groupings of models.

# List all datasets
cargo-ai storage dataset list

# Get a single dataset
cargo-ai storage dataset get 

Columns

Columns define the schema of a model.

# List columns for a model
cargo-ai storage column list --model-uuid 

# Create a column
cargo-ai storage column create \
  --model-uuid  \
  --column '{"slug":"my_column","type":"string","label":"My Column","kind":"custom"}'

# Update a column (pass the full column object — columns are identified by slug, not UUID)
cargo-ai storage column update \
  --model-uuid  \
  --column '{"slug":"my_column","type":"string","label":"Updated Label","kind":"custom"}'

# Remove a column
cargo-ai storage column remove --model-uuid  --column-slug 

# Reorder a column (move to a specific index)
cargo-ai storage column reorder --model-uuid  --column-slug  --to-index 2

Column types: string, number, boolean, date, object, array, vector, any.

Column kinds: custom (user-defined), computed (expression over other columns), metric (aggregated from a related model), lookup (single field pulled from a related model via a join).

Preview what you built

A column list doesn't tell the user whether the model is right — rows do. Two checkpoints (the pack-wide convention lives in [../cargo/references/interaction.md](../cargo/references/interaction.md) §4):

1. Right after model create / column create — show the schema, not rows. A new model is empty; a LIMIT 10 here returns nothing and reads as failure. Echo the columns as a compact table instead (column, type, what will fill it).

2. As soon as data lands — show the rows. After a batch, play, or import writes into the model, preview it:

cargo-ai storage query execute \
  "SELECT * FROM . LIMIT 10"

Show ~10 rows and only the columns that carry meaning. Storage queries are free, so this costs nothing but a few lines of output — and it's the first moment the user can actually see what they built. When a play fills a new column, preview that column next to the record's identifying fields (name, domain) so filled vs. empty is obvious.

If the preview comes back empty or all-null when it shouldn't, that's a finding — surface it rather than reporting the write as a success. See [cargo-diagnostics](../cargo-diagnostics/SKILL.md) to trace why.

Relationships

Relationships link models together (e.g. Contacts belong to Companies).

# List relationships for a model
cargo-ai storage relationship list --model-uuid 

# Set a relationship between two models
cargo-ai storage relationship set \
  --from-model-uuid  \
  --to-model-uuid 

Records

# List records in a model
cargo-ai storage record list --model-uuid 

For advanced record queries (filtering, sorting, pagination), use segmentation segment fetch from the cargo-orchestration skill.

Query with SQL

Run SQL against workspace storage with storage query execute. Tables are referenced as . (e.g. default.companies) and rewritten to the underlying storage table under the hood — no DDL lookup is needed for the table name.

cargo-ai storage query execute \
  "SELECT name, domain FROM default.companies LIMIT 10"
# → { "rows": [...] } on success; non-zero exit with { "errorMessage": "..." } on error

For full exports, use storage query download — it returns a signed URL to a CSV (default) or Parquet file:

cargo-ai storage query download \
  --query "SELECT name, domain, revenue FROM default.companies ORDER BY revenue DESC"

cargo-ai storage query download \
  --query "SELECT * FROM default.companies" --format parquet

Get column slugs from storage column list --model-uuid (or run storage model get-ddl for the full schema and SQL dialect). Page through large result sets with LIMIT / OFFSET directly in the SQL.

See references/examples/queries.md for WHERE clauses, aggregations, joins, date queries, pagination, and the failure shapes returned on error.

Help

Every command supports --help:

cargo-ai storage model list --help
cargo-ai storage column create --help
cargo-ai storage relationship set --help
cargo-ai storage query execute --help
cargo-ai storage query download --help

Source & license

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

• Author: getcargohq
• Source: getcargohq/cargo-skills
• License: MIT
• Homepage: https://getcargo.ai

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