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SkipprLike Codex, but for data.

Go from raw source data to production-ready dbt models in a single command. Skippr handles extraction, loading, schema mapping, and dbt code generation -- so you can skip the weeks of pipeline plumbing and start querying clean data in minutes.

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Minutes, not months

A working data pipeline from first install to materialised dbt models, without writing a single line of SQL or YAML by hand.

AI-assisted schema mapping

Source schemas are automatically discovered and mapped to well-typed, cleanly named dbt models. You review the output, not the boilerplate.

Your data stays on your machine

Source data flows directly from your machine to your warehouse. Row-level data is never sent to Skippr. Only schema metadata leaves your network.

Incremental from day one

Re-runs sync only new and changed rows. No full reloads, no duplicate handling. It just works.

You own the output

The generated dbt project is standard dbt. Add tests, snapshots, custom gold models, or plug it into your existing CI/CD.

Full connector coverage

MSSQL, PostgreSQL, Snowflake, BigQuery, S3, Kafka, and dozens more sources and destinations out of the box.

Install ​

bash
curl -fsSL https://install.skippr.io | sh

Run your first pipeline ​

bash
skippr user login              # log in or create a new account
skippr init my-project
skippr connect warehouse snowflake
skippr connect source mssql
skippr run

Five commands. That's extract, load, and a full bronze/silver/gold dbt project -- compiled, validated, and materialised in your warehouse.

How it works ​

  1. Extract -- reads tables and files from your source systems.
  2. Load -- writes raw data into a bronze schema in your warehouse.
  3. Model -- generates, compiles, and materialises silver and gold dbt models using AI-assisted schema mapping.

See How It Works for the full pipeline breakdown.

Connectors ​

Sources ​

CategoryConnectors
DatabasesMSSQL, MySQL, PostgreSQL, Redshift, MongoDB, DynamoDB, ClickHouse, MotherDuck
Object StoresS3, SFTP, Delta Lake
StreamingKafka, SQS, Kinesis, AMQP (RabbitMQ), SNS, EventBridge, MQTT, WebSocket
HTTPHTTP Client, HTTP Server
OtherSocket (TCP/UDP/Unix), StatsD, Local File, Stdin

Destinations ​

CategoryConnectors
WarehousesSnowflake, BigQuery, PostgreSQL, Athena (S3 + Glue), Databricks, Synapse, Redshift, ClickHouse, MotherDuck
Cloud StorageGCS, Azure Blob, SFTP
MessagingAMQP (RabbitMQ)
OtherLocal File, Stdout

See the Source Connectors and Destination Connectors for setup instructions per provider.

Requirements ​

DependencyWhy
Python 3.10+Required by dbt
dbt-core + warehouse adapterModel compilation and materialisation
A Skippr accountProvides LLM keys, cloud storage, and usage metering