Data Pipeline Spec

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name: data-pipeline-spec
description: "Design an ETL/ELT data pipeline specification. Use when asked to design a data pipeline, spec an ETL or ELT process, document a data ingestion workflow, or plan a data integration. Produces a complete pipeline spec with sources, transforms, destinations, SLAs, error handling, and data quality rules."
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# Data Pipeline Spec Skill

This skill produces a complete data pipeline specification covering sources, transformations, destinations, scheduling, SLAs, error handling, data quality checks, and monitoring requirements. Output is ready for engineering handoff or architecture review.

## Required Inputs

Ask the user for these if not provided:
- **Pipeline purpose** — what business question or workflow does this pipeline serve?
- **Source systems** — where does data come from? (databases, APIs, files, event streams)
- **Destination** — where does data land? (data warehouse, data lake, downstream DB, reporting tool)
- **Transformation type** — ETL (transform before loading) or ELT (load raw, transform in warehouse)?
- **Frequency / SLA** — how often must data be fresh? (real-time / hourly / daily / weekly)
- **Volume estimate** — approximate rows/events per run
- **Data quality requirements** — completeness, deduplication, freshness, schema enforcement
- **Team or stack** — any specific tools in use? (Airflow, dbt, Fivetran, Spark, Kafka, etc.)

## Output Structure

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