Skill

domain-iot:edge-computing

Provides IoT edge computing patterns including gateway architecture, edge vs cloud processing, ML inference on edge hardware, K3s/Azure IoT Edge containers, data sync, and offline resilience.

Kubernetes

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

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/domain-iot:edge-computing

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- Designing edge gateway architecture and selecting hardware (Raspberry Pi to industrial)

Supporting Files

references/edge-ml-and-containers.mdreferences/gateway-and-processing.mdreferences/sync-and-offline.md

SKILL.md

28 lines · ~488 tokens

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Parent stars13

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Last CommitMar 3, 2026

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Edge Computing for IoT

When to use

Designing edge gateway architecture and selecting hardware (Raspberry Pi to industrial)

Deciding what to process at the edge vs offload to the cloud

Deploying ML inference models on constrained edge hardware

Containerizing edge workloads with K3s or Azure IoT Edge modules

Implementing edge-cloud data sync and conflict resolution strategies

Building systems that must function during internet outages

Core principles

Intermittent connectivity is the default — design store-and-forward from day one; connectivity is the exception, not the guarantee

Edge runs inference, cloud runs training — push optimized models down, pull raw data and drift metrics up

Protocol translation lives at the gateway boundary — one cloud-side identity, many device-side protocols; canonical data model applied at the seam

Pin container versions on edge devices — latest in a factory is a production incident waiting to happen

100ms is the latency ceiling for control loops — anything safety-critical or real-time stays local regardless of network quality

Reference Files

references/gateway-and-processing.md — gateway responsibilities, hardware selection guide, edge vs cloud processing decision criteria, hybrid pattern

references/edge-ml-and-containers.md — TensorFlow Lite / ONNX / TensorRT frameworks, ML deployment pipeline, K3s, Azure IoT Edge, container best practices

references/sync-and-offline.md — data synchronization strategies, conflict resolution, offline operation with local storage and RTC drift handling

Edge Computing for IoT

When to use

Designing edge gateway architecture and selecting hardware (Raspberry Pi to industrial)
Deciding what to process at the edge vs offload to the cloud
Deploying ML inference models on constrained edge hardware
Containerizing edge workloads with K3s or Azure IoT Edge modules
Implementing edge-cloud data sync and conflict resolution strategies
Building systems that must function during internet outages

Core principles

Intermittent connectivity is the default — design store-and-forward from day one; connectivity is the exception, not the guarantee
Edge runs inference, cloud runs training — push optimized models down, pull raw data and drift metrics up
Protocol translation lives at the gateway boundary — one cloud-side identity, many device-side protocols; canonical data model applied at the seam
Pin container versions on edge devices — latest in a factory is a production incident waiting to happen
100ms is the latency ceiling for control loops — anything safety-critical or real-time stays local regardless of network quality

Reference Files

references/gateway-and-processing.md — gateway responsibilities, hardware selection guide, edge vs cloud processing decision criteria, hybrid pattern
references/edge-ml-and-containers.md — TensorFlow Lite / ONNX / TensorRT frameworks, ML deployment pipeline, K3s, Azure IoT Edge, container best practices
references/sync-and-offline.md — data synchronization strategies, conflict resolution, offline operation with local storage and RTC drift handling

domain-iot:edge-computing

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SKILL.md

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domain-iot:edge-computing

Popularity

Invocation

Tool Access

Context Preview

Supporting Files

SKILL.md

Edge Computing for IoT

When to use

Core principles

Reference Files

Similar Skills

Help us improve

Edge Computing for IoT

When to use

Core principles

Reference Files