.agents/skills/bootstrap/SKILL.md
Create a new Starlake project from a template
npx skillsauth add starlake-ai/starlake-skills bootstrapInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Creates a new Starlake project with the standard directory structure and starter configuration files. Optionally uses a named template to scaffold a project with sample data and configurations.
starlake bootstrap [options]
--template <value>: Template name to use (default: interactive selection). Examples: quickstart, simple--no-exit: Keep the JVM running after project creation (used in testing)--reportFormat <value>: Report output format: console, json, or htmlmy-project/
├── metadata/
│ ├── application.sl.yml # Global configuration, connections
│ ├── env.sl.yml # Environment variables
│ ├── types/
│ │ └── default.sl.yml # Data type definitions
│ ├── load/
│ │ └── {domain}/
│ │ ├── _config.sl.yml # Domain configuration
│ │ └── {table}.sl.yml # Table schemas
│ ├── transform/
│ │ └── {domain}/
│ │ ├── _config.sl.yml # Transform defaults
│ │ └── {task}.sql # SQL transformations
│ ├── dags/ # Orchestration DAG configs
│ ├── expectations/ # Data quality macros
│ └── extract/ # Extraction configurations
├── datasets/ # Processed data storage
├── incoming/ # Raw data landing area
└── sample-data/ # Example datasets
application.sl.ymlversion: 1
application:
connectionRef: "{{connectionRef}}"
audit:
sink:
connectionRef: "{{connectionRef}}"
connections:
duckdb:
type: "jdbc"
options:
url: "jdbc:duckdb:{{SL_ROOT}}/datasets/duckdb.db"
driver: "org.duckdb.DuckDBDriver"
bigquery:
type: "bigquery"
options:
location: "europe-west1"
authType: "APPLICATION_DEFAULT"
dagRef:
load: "airflow_load_shell"
transform: "airflow_transform_shell"
env.sl.ymlversion: 1
env:
root_path: "{{SL_ROOT}}"
incoming_path: "{{SL_ROOT}}/datasets/incoming"
connectionRef: duckdb
starlake bootstrap
starlake bootstrap --template quickstart
starlake bootstrap --template simple
development
Design SQL transformations for data pipelines with quality checks and dependency management. Use when the user says "design transforms" or "create SQL transformations".
devops
Plan and track sprint progress for data pipeline implementation. Use when the user says "sprint planning" or "plan data sprint".
testing
Analyze data sources in depth: schema, quality, volume, and extraction strategy. Use when the user says "analyze data source" or "profile this data source".
data-ai
Design Starlake-compatible table schemas with types, constraints, privacy, and expectations. Use when the user says "design schema" or "create table definition".