Agent skill · data analytics · aiskillstore
creating-bauplan-pipelines
Creates bauplan data pipeline projects with SQL and Python models. Use when starting a new pipeline, defining DAG transformations, writing models, or setting up bauplan project structure from scratch.
Why this skill is useful
Provides specific commands and project structure for creating bauplan data pipelines that the AI wouldn't reliably generate on its own.
What it needs
Requires bauplan installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Creating a New Bauplan Data Pipeline This skill guides you through creating a new bauplan data pipeline project from scratch, including the project configuration and SQL/Python transformation models. CRITICAL: Branch Safety NEVER run pipelines on main branch. Always use a development branch. Branch naming convention: <username>.<branchname> (e.g., john.feature-pipeline). Get your username with bauplan info. See Workflow Checklist for exact commands. Prerequisites Before creating the pipeline, verify that: 1. You have a development branch (not main) 2. Source tables exist in the bauplan lakehouse (the default namespace is bauplan) 3. You understand the schema of the source tables Pipeline as a DAG A bauplan pipeline is a DAG of functions (models). Key rules: 1. Models: SQL or Python functions that transform data 2. Source Tables: Existing lakehouse tables - entry points to your DAG 3. Inputs: Each model can take multiple tables via bauplan.Model() references 4. Outputs: Each model produces exactly one table: SQL: output name = filename (trips.sql → trips) Python: output name = function name (def cleantrips() → cleantrips) 5. Topology: Implicitly defined by input references - bauplan determines execution order Expectations: Data quality functions that take tables as input and return a boolean. Example DAG In this example: taxifhvhv and taxizones are source tables (already in lakehouse) trips.sql reads from taxifhvhv (SQL model, first node) cleantrips takes trips and taxizones as inputs (Python model, multiple inputs) dailysummary takes cleantrips as input (Python model, single input) Required User Input Before writing a pipeline, you MUST gather the following information from the user: 1. Pipeline purpose (required): What transformations should the DAG perform? What is the business logic or goal? 2. Source tables (required): Which tables from the lakehouse should be used as inputs? Verify they exist with bauplan table get 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/creating-bauplan-pipelines