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Routes users to specialized agents for data pipeline design, schema modeling, data quality, SQL optimization, lakehouse, AI pipelines, and data contracts. Provides access to 23 knowledge domains.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agentspec:data-engineering-guideThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You have access to 23 specialized knowledge base domains and 15+ data engineering agents. Route the user to the right tool based on their task.
You have access to 23 specialized knowledge base domains and 15+ data engineering agents. Route the user to the right tool based on their task.
| User Task | Command | Agent |
|---|---|---|
| Design a data pipeline / DAG | /agentspec:pipeline | pipeline-architect |
| Design a schema / star schema / data model | /agentspec:schema | schema-designer |
| Add data quality checks | /agentspec:data-quality | data-quality-analyst |
| Review SQL performance | /agentspec:sql-review | sql-optimizer |
| Choose table format (Iceberg/Delta) | /agentspec:lakehouse | lakehouse-architect |
| Build RAG / embedding pipeline | /agentspec:ai-pipeline | ai-data-engineer |
| Create a data contract | /agentspec:data-contract | data-contracts-engineer |
| Migrate legacy ETL | /agentspec:migrate | dbt-specialist + spark-engineer |
| Category | Domains |
|---|---|
| Core DE | dbt, spark, airflow, streaming, sql-patterns |
| Data Design | data-modeling, data-quality, medallion |
| Infrastructure | lakehouse, cloud-platforms, aws, gcp, microsoft-fabric, lakeflow, terraform |
| AI & Modern | ai-data-engineering, genai, prompt-engineering, modern-stack |
| Foundations | pydantic, python, testing |
${CLAUDE_PLUGIN_ROOT}/kb/{domain}/index.md/agentspec:schema or delegate to dbt-specialist/agentspec:pipeline/agentspec:data-quality/agentspec:sql-review/agentspec:lakehouse/agentspec:ai-pipeline/agentspec:data-contract/agentspec:migratenpx claudepluginhub luanmorenommaciel/agentspec --plugin agentspecDesigns and implements scalable batch and streaming data pipelines, modern data warehouses, and lakehouse architectures using Spark, dbt, Airflow, and cloud-native platforms.
Builds scalable data pipelines, modern data warehouses, and real-time streaming architectures using Apache Spark, dbt, Airflow, and cloud-native platforms.
Designs data pipelines using functional principles: idempotency, immutability, declarative transformations. Guides on ELT, partitioning, dbt layers, data quality tests, and DAG orchestration.