What do I need to use Data Pipeline Spec?

Pipeline purpose; Source systems; Destination; Transformation type; Frequency / SLA; Volume estimate; Data quality requirements; Team or stack.

Is Data Pipeline Spec free to use?

Yes — run it free in the browser playground (no API key needed for the sponsored trial, or bring any provider key), or install it into Claude Code, Cursor, ChatGPT and more via npx pm-claude-skills. MIT-licensed and open source.

Data Pipeline Spec

Q: How do I know the Data Pipeline Spec output is good?

The skill verifies itself against its own quality checks: Every source has an incremental key or full-refresh justification; Business logic rules are documented, not just the SQL; SLAs are agreed with consumers, not set unilaterally by engineering; DQ checks cover completeness, uniqueness, freshness, and volume.

🔵 Stable🕐 updated 2026-06-08 🔷 SkillSpec L3 pm-data

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.

▶ Run it free — no key needed 📝 Grade your existing draft View SKILL.md ↗

🗣 Say this to your agent

“Design a data pipeline for our Salesforce to Snowflake sync”“Write a pipeline spec for ingesting Stripe events into our data warehouse”“Build an ETL spec for our user activity data”“Document our dbt pipeline from raw events to the analytics mart”“Spec out the pipeline that feeds the executive dashboard”

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.)

✅ The bar it holds itself to

Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.

✓Every source has an incremental key or full-refresh justification

✓Business logic rules are documented, not just the SQL

✓SLAs are agreed with consumers, not set unilaterally by engineering

✓DQ checks cover completeness, uniqueness, freshness, and volume

✓Failure modes include a documented recovery owner

✓PII fields are identified and a treatment plan is specified

⚠️ What it refuses to do

Do not spec a pipeline without defining SLAs — "as fast as possible" is not an acceptable freshness target

Do not omit error handling and dead-letter queue strategy — every pipeline must specify what happens to failed records

Do not design idempotent loads without documenting the deduplication key — assume reruns will happen

Do not leave data quality rules implicit — schema validation, null checks, and referential integrity must be explicit

Do not ignore schema evolution — specify how upstream schema changes are detected and handled

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

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💬 Discussion

Data Pipeline Spec is one of 1078 open-source professional AI agent skills — all SkillSpec L3. Try them all in the browser · ⭐ Star on GitHub · Browse the full catalog