data-orchestration-skill
Data Orchestration Skill for Claude
Expert guidance for orchestrating data pipelines. Dagster-first for greenfield projects, Airflow for brownfield. Covers scheduling, dependencies, monitoring, retries, alerting, and dbt/DLT integration.
When to Use This Skill
Activate when:
- Choosing between orchestration tools (Dagster vs Airflow vs Prefect vs embedded)
- Building Dagster assets, resources, sensors, schedules, or partitions
- Writing Airflow DAGs, operators, TaskFlow tasks, or connections
- Integrating orchestrators with dbt or DLT
- Implementing retry logic, alerting, failure handling, or partitioned backfills
- Deciding whether you need an orchestrator at all
Don't use for: dbt model writing (use dbt-skill), DLT source/destination config (use integration-patterns-skill), Kafka/Flink streaming (use streaming-data-skill), IaC provisioning, or CI/CD pipelines.
Scope Constraints
More from dtsong/data-engineering-skills
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3duckdb
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2data-testing
Use this skill when designing testing strategies for data pipelines, writing SQL assertions, validating pipeline output, or packaging tests as client deliverables. Covers dbt test patterns, pipeline validation, SQL assertion libraries, test coverage targets, and test-as-deliverable packaging. Common phrases: \"data testing strategy\", \"pipeline validation\", \"SQL assertions\", \"test coverage\", \"test as deliverable\", \"data quality tests\". Do NOT use for writing dbt models (use dbt-transforms), DuckDB analytical queries (use duckdb), or pipeline scheduling (use data-pipelines).
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Use this skill when building real-time or near-real-time data pipelines. Covers Kafka, Flink, Spark Streaming, Snowpipe, BigQuery streaming, materialized views, and batch-vs-streaming decisions. Common phrases: \"real-time pipeline\", \"Kafka consumer\", \"streaming vs batch\", \"low latency ingestion\". Do NOT use for batch integration patterns (use data-integration) or pipeline orchestration (use data-pipelines).
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