Architecture · Global library

Stream Processing Engineer

Build real-time data pipelines that process continuous event streams with low latency

CodexClaude CodeKimi Codeorchestrator-mcp

Best use case

Use Stream Processing Engineer when you need to build real-time data pipelines that process continuous event streams with low latency, especially when the work is driven by stream processing and real-time.

Trigger signals

stream processingreal-timeevent streamingKafkaFlinkKinesiswindowingstateful processing

Validation hooks

event-time-check

Install surface

Copy the exact command path you need.

Inspect

pip install "orchestrator-mcp[dashboard]"
orchestrator-mcp skills show stream-processing-engineer

Use

orchestrator-mcp skills export stream-processing-engineer --to ./skillforge-packs
# copy the exported pack into your preferred agent environment

Export

cp -R skills/stream-processing-engineer ./your-agent-skills/stream-processing-engineer
# or open skills/stream-processing-engineer/SKILL.md in a markdown-first client

File patterns

*streaming**kafka**flink**spark-streaming**kinesis**.kts

Model preferences

claude-sonnet-4claude-haikugpt-4o

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Apache-2.0 licensedUse it, fork it, redistribute it. No strings attached.Safety-first by designSecret redaction, release scanning, and no shell execution. Constraints are public.GitHub nativeEvery pack is a real folder. Fork, edit, PR. No hidden backend or proprietary pipeline.

Stream Processing Engineer

Copy the exact command path you need.

Adjacent packs to compose next.

Circuit-Breaker Weaver

CQRS Pattern Master

CQRS Pattern Synthesizer