Plan and implement connections between Arduino devices and Azure IoT Hub, directly or through an IoT Edge gateway. Define sensor messages, device credentials, offline buffering, remote commands, and implementation tasks for firmware, gateways, and cloud services.
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Design and implement Arduino integration with Azure IoT Hub and IoT Edge, including secure provisioning, resilient telemetry, command handling, and production guardrails.
安装前可以查看交给 AI 代理的指令内容。
Use this skill when the user needs to connect Arduino-class devices to Azure IoT, especially in edge-heavy scenarios (gateways, intermittent networks, offline buffering, and local actuation).
Use this skill for requests such as:
Before recommending an IoT Edge topology or runtime behavior, review:
If documentation cannot be consulted, proceed with explicit assumptions and highlight them in a dedicated section.
Before proposing firmware, wiring, or communication implementation details, consult official Arduino sources first:
When choosing between implementation alternatives, prioritize official Arduino guidance over community snippets unless there is a clear technical reason to deviate.
Use when connectivity is stable and cloud latency is acceptable.
Use when links are constrained, local control is required, or batching improves cost/reliability.
Define:
Require:
Plan and document:
Define routes for:
Specify minimum operations telemetry:
When relevant, combine with:
azure-smart-city-iot-solution-builder for city-wide architecture and phased rollout.azure-resource-visualizer for relationship diagrams.appinsights-instrumentation for app and service telemetry patterns.Also use references/arduino-official-best-practices.md as a quality baseline for firmware and hardware recommendations, and references/arduino-iot-checklist.md before finalizing architecture or implementation guidance.
Always provide:
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Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
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Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
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Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
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Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
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Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.
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