CLI

Scaffolding & Code Generation

Stop writing boilerplate by hand. The dcupl CLI scaffolds entire projects and generates individual artifacts — models, TypeScript types, JSON schemas, transformers, scripts, operators, and tests — so you start from working files instead of an empty editor. You can even infer a model directly from a CSV or JSON data file.

Scaffold a project

Use dcupl init (alias dcupl new) with an optional [target] directory.

Flags

Flag	Description
`--name <str>`	Project name
`--here`	Scaffold into the current directory
`--starter <type>`	Starter: minimal, empty, or existing
`--project-id <id>`	For existing: the cloud project to link
`--api-key <key>`	For existing: API key for the project

Starters

Starter	What you get
`minimal`	Default. A sample model + data, ready to push to Console.
`empty`	A bare folder structure. You add your own data.
`existing`	Pulls files from an existing Console project.

Generate a model from data

Infer a model from a data file. Preview it first with --dry-run, then --register it so a model and data resource are added to your dcupl.lc.json.

zsh — infer a model from products.csv

# preview the inferred model — scan 200 rows, write nothing

❯dcupl generate model --from products.csv --name Product \ --sample-size 200 --dry-run

{
  "key": "Product",
  "keyProperty": "id",
  "properties": {
    "id": "string",
    "productName": "string",
    "price": "string",
    "category": "string"
  }
}
# dry run — no files written

# looks right — generate for real and register it

❯dcupl generate model --from products.csv --name Product --register

✓ Wrote dcupl/models/product.dcupl.json
✓ Registered Product + data resource in dcupl.lc.json

❯▋

# preview the inferred model — scan 200 rows, write nothing

❯dcupl generate model --from products.csv --name Product \ --sample-size 200 --dry-run

{
  "key": "Product",
  "keyProperty": "id",
  "properties": {
    "id": "string",
    "productName": "string",
    "price": "string",
    "category": "string"
  }
}
# dry run — no files written

# looks right — generate for real and register it

❯dcupl generate model --from products.csv --name Product --register

✓ Wrote dcupl/models/product.dcupl.json
✓ Registered Product + data resource in dcupl.lc.json

❯▋

When inferring from CSV, columns default to string types. Use --sample-size to scan more rows for better inference, and prefer explicit, hand-tuned models when you need reliable numeric handling. Some generate subcommands are interactive and need a TTY.

Generate artifacts

Use dcupl generate [type] to scaffold individual artifacts inside an existing project.

Types

Type	Generates
`model`	A dcupl model definition
`typescript`	TypeScript types from a model
`json-schema`	A JSON schema from a model
`transformer`	A data transformer
`script`	A script
`operator`	A custom operator
`test`	A test file
`test-setup`	Test setup boilerplate
`test-config`	Test configuration

Key flags

Flag	Description
`--name <str>`	Name of the artifact (e.g. the model name)
`--file-name <str>`	Override the generated file name
`--from <file>`	Infer a model from a CSV, JSON, or NDJSON file
`--register`	Auto-add the model + data resource to dcupl.lc.json
`--key-property <str>`	Override the inferred key (comma-separate for composite)
`--sample-size <n>`	Rows to scan when inferring a schema
`--dry-run`	Preview the output without writing files