Data Pipeline Spec

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.

Install

npx pm-claude-skills add --agent claude   # or codex · cursor · gemini · hermes
# or one-line MCP (every skill, any client):
claude mcp add pm-skills -- npx -y pm-claude-skills-mcp

What to give it

• 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.)

Related skills