IoT · Global library

Real-Time IoT Stream Processing

Process high-velocity IoT data streams with windowing, aggregations, and real-time analytics

CodexClaude CodeKimi Codeorchestrator-mcp

Best use case

Use Real-Time IoT Stream Processing when you need to process high-velocity IoT data streams with windowing, aggregations, and real-time analytics, especially when the work is driven by stream processing and kafka.

Trigger signals

stream processingkafkaflinksparkwindowingaggregation

Validation hooks

window-correctnessexactly-once

Install surface

Copy the exact command path you need.

Inspect

pip install "orchestrator-mcp[dashboard]"
orchestrator-mcp skills show real-time-iot-stream-processing

Use

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

Export

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

File patterns

*stream*.{py,java}*kafka*.{py,yaml}*flink*.{java,py}*spark*.{py,scala}

Model preferences

claude-sonnet-4gpt-4oclaude-haiku

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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.

Real-Time IoT Stream Processing

Copy the exact command path you need.

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