BauplanLabs

Assesses whether a business question can be answered with bauplan data.

Creates bauplan data pipeline projects.

Generates data quality check code for bauplan pipelines and ingestion.

Diagnoses and fixes a failed bauplan pipeline.

Explores data in a bauplan lakehouse.

Ingests data from S3 into bauplan.

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Ingest data from S3 into Bauplan safely using branch isolation and quality checks before publishing. Use when loading new data from S3, importing parquet/csv/jsonl files, or when the user needs to safely load data with validation before merging to main.

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.

Assesses whether a business question can be answered with data available in a Bauplan lakehouse. Maps business concepts to tables and columns, checks data quality on the relevant subset, validates semantic fit, and renders a verdict: answerable, partially answerable, or not answerable. Produces a structured feasibility report. Use when a user brings a business question, asks 'can we answer this', wants to know if the data supports an analysis, or before building a one-off analysis or pipeline.

Generates data quality check code for bauplan pipelines and ingestion workflows. Invoked by the bauplan-data-pipeline and bauplan-safe-ingestion skills, or directly by the user. Produces expectations.py for pipelines or validation logic for WAP scripts. Output is always code, never reports.

Diagnose a failed Bauplan job, pin the exact data state, collect evidence, apply a minimal fix, and rerun. Evidence first, changes second.

Explores data in a Bauplan lakehouse safely using the Bauplan Python SDK. Use to inspect namespaces, tables, schemas, samples, and profiling queries; and to export larger result sets to files. Read-only exploration only; no writes or pipeline runs.

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.

Ingest data from S3 into bauplan using the Write-Audit-Publish pattern for safe data loading. Use when loading new data from S3, performing safe data ingestion, or when the user mentions WAP, data ingestion, importing parquet/csv/jsonl files, or needs to safely load data with quality checks.

Initialize the Valentine Data Challenge scenario by ingesting telemetry data from S3 and creating a basic transformation pipeline. This sets up a naive workflow that will later be improved with proper data quality safeguards.