Skill

domain-iot:iot-cloud-integration

Cloud IoT platform integration covering AWS IoT Core, Azure IoT Hub, and Google Cloud IoT. Includes device shadow/twin patterns, rules engines, edge runtimes, data pipelines from ingestion to analytics, and multi-cloud considerations.

Install

npx claudepluginhub rnavarych/alpha-engineer --plugin domain-iot

Tool Access

This skill is limited to using the following tools:

ReadGrepGlobBash

Preview

- Integrating devices with AWS IoT Core, Azure IoT Hub, or GCP after the Cloud IoT Core deprecation

Supporting Assets

references/aws-iot-core.mdreferences/azure-iot-hub.mdreferences/gcp-and-pipeline-design.md

SKILL.md

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Stats

Parent Repo Stars9

Parent Repo Forks0

Last CommitMar 3, 2026

Actions

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When to use

Integrating devices with AWS IoT Core, Azure IoT Hub, or GCP after the Cloud IoT Core deprecation

Configuring rules engines to route device messages to downstream services

Designing hot/warm/cold data pipeline architectures for telemetry ingestion

Setting up Device Provisioning Service (DPS) for zero-touch at scale

Evaluating multi-cloud trade-offs or avoiding vendor lock-in

Choosing between Timestream, Azure Data Explorer, BigQuery, and Bigtable for IoT storage

Core principles

Decouple ingestion from processing — message broker or queue between device gateway and any processor; never direct device-to-database writes

Schema validation at the ingestion boundary — malformed payloads caught at the door, not discovered three hops later in a dashboard

Branch early for latency tiers — hot path (real-time alerts), warm path (dashboards), cold path (archive) forked at the rules engine, not bolted on later

Idempotent consumers everywhere — IoT protocols guarantee at-least-once; your processors must handle duplicates gracefully

Open formats in storage — Parquet/Avro in object storage makes the next cloud migration a query problem, not a rewrite

Reference Files

references/aws-iot-core.md — Device Gateway, Message Broker, Device Shadow, Rules Engine SQL, IoT Greengrass edge runtime, AWS data pipeline pattern

references/azure-iot-hub.md — D2C/C2D messaging, Device Twins, DPS allocation policies, IoT Edge modules, message routing config, Azure data pipeline pattern

references/gcp-and-pipeline-design.md — GCP post-deprecation stack (Pub/Sub, Dataflow, BigQuery, Bigtable), generic platform-agnostic pipeline pattern, multi-cloud considerations

IoT Cloud Integration

When to use

Integrating devices with AWS IoT Core, Azure IoT Hub, or GCP after the Cloud IoT Core deprecation
Configuring rules engines to route device messages to downstream services
Designing hot/warm/cold data pipeline architectures for telemetry ingestion
Setting up Device Provisioning Service (DPS) for zero-touch at scale
Evaluating multi-cloud trade-offs or avoiding vendor lock-in
Choosing between Timestream, Azure Data Explorer, BigQuery, and Bigtable for IoT storage

Core principles

Decouple ingestion from processing — message broker or queue between device gateway and any processor; never direct device-to-database writes
Schema validation at the ingestion boundary — malformed payloads caught at the door, not discovered three hops later in a dashboard
Branch early for latency tiers — hot path (real-time alerts), warm path (dashboards), cold path (archive) forked at the rules engine, not bolted on later
Idempotent consumers everywhere — IoT protocols guarantee at-least-once; your processors must handle duplicates gracefully
Open formats in storage — Parquet/Avro in object storage makes the next cloud migration a query problem, not a rewrite

Reference Files

references/aws-iot-core.md — Device Gateway, Message Broker, Device Shadow, Rules Engine SQL, IoT Greengrass edge runtime, AWS data pipeline pattern
references/azure-iot-hub.md — D2C/C2D messaging, Device Twins, DPS allocation policies, IoT Edge modules, message routing config, Azure data pipeline pattern
references/gcp-and-pipeline-design.md — GCP post-deprecation stack (Pub/Sub, Dataflow, BigQuery, Bigtable), generic platform-agnostic pipeline pattern, multi-cloud considerations