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.
npx skills add mohitagw15856/pm-claude-skills --skill data-pipeline-spec --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 --- # Data Pipeline Spec: [Pipeline Name] **Purpose:** [One sentence — what decision or workflow does this pipeline enable?]
- Required Inputs
- Output Structure
- 1. Overview
- 2. Sources
- 3. Ingestion Layer
- 4. Transformation Logic
- 5. Destination
- 6. Scheduling & SLAs
- 7. Data Quality Rules
- 8. Error Handling & Recovery
- 9. Monitoring & Observability
- 10. Dependencies & Sequencing
- 11. Security & Compliance
- Quality Checks
What does the data-pipeline-spec skill do?
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.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-pipeline-spec --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.