Building with Kafka, Kinesis, Pulsar, Flink, Beam, or Debezium CDC? GreatCTO auto-detects the streaming archetype and ships exactly-once semantics, idempotency proofs, backpressure strategy, DLQ + poison-message handling, schema evolution, and checkpoint storage gates from day one.
kafkajs / @confluentinc/kafka-javascript / debezium →Compliance auto-suggested: gdpr · soc2-cc7. Specialist agents activated:
At-most-once / at-least-once / exactly-once decision · idempotency proof · partition-key strategy · backpressure mechanism · DLQ + poison-message handling · schema evolution · stateful checkpoint storage.
End-to-end p50 / p95 / p99 targets · tail-latency causes (GC, network, checkpoint pauses) · throughput capacity test · load test with realistic burst.
Stream → batch handoff · GDPR retention in event store · CDC fidelity (snapshot + log-based replication) · OpenLineage event emission.
PII classification per event type · authorization on producer/consumer · audit log emission for regulated topics · encryption in flight (TLS) + at rest.
Packs auto-attach when CLI detects pack-specific signals (e.g. twilio in deps → voice-pack). Each pack adds its own reviewer agents + human gates on top of the base archetype pipeline.
$ npx great-cto init