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cargo-storage

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Inspect and modify Cargo workspace data models, then run SQL against storage.

What it does

Manage the data layer of a Cargo workspace through the CLI: list, create, and update models, datasets, columns, relationships, and records. Run SQL against workspace storage with `storage query execute` for ad-hoc reads or `storage query download` for full CSV/Parquet exports. Supports ingest (webhook-fed) models, column type and kind configuration, and previewing rows after data lands.

When to use it

  • Listing models, datasets, and columns to discover a workspace's schema
  • Adding or updating columns, models, and relationships to shape business data
  • Running ad-hoc SQL queries against workspace storage with execute or download
  • Setting up ingest (webhook-fed) models and previewing rows after a batch writes

The skill document

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 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 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 §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 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

Questions people ask

What data structures can I manage with this skill?
Models, datasets, columns, relationships, and records. Columns accept types like string, number, boolean, date, object, array, vector, and any, and kinds including custom, computed, metric, and lookup.
How do I run a SQL query against the workspace?
Use `cargo-ai storage query execute "..."` for row results, or `storage query download --query "..."` to get a signed URL to a CSV (default) or Parquet export. Tables are referenced as `dataset.table` and rewritten to the underlying storage table.
When should I use a different skill instead?
Use `cargo-orchestration` for run or batch telemetry and `cargo-segmentation` when naming a reusable filtered audience. This skill is for business data structures and SQL over workspace storage, not execution telemetry.

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