[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AD5YZTM7TT68BJC21CSZPS":3,"skill-same:01M6AD5YZTM7TT68BJC21CSZPS":82},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6AD5YZTM7TT68BJC21CSZPS","arduino-azure-iot-edge-integration",null,"Design and implement Arduino integration with Azure IoT Hub and IoT Edge, including secure provisioning, resilient telemetry, command handling, and production guardrails.","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41028,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Farduino-azure-iot-edge-integration\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"azure iot","azure","iot","integration","edge","arduino azure iot edge integration","azure iot edge","arduino","Production","with","azure iot hub","hub","command","provisioning","Guardrails","design","secure","telemetry","implement","2026-11-10 10:12:30",false,"---\nname: arduino-azure-iot-edge-integration\ndescription: 'Design and implement Arduino integration with Azure IoT Hub and IoT Edge, including secure provisioning, resilient telemetry, command handling, and production guardrails.'\n---\n\n# Arduino Azure IoT Edge Integration\n\nUse 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).\n\n## When to use it\n\nUse this skill for requests such as:\n\n- \"I want to connect Arduino sensors to Azure\"\n- \"How do I send MQTT telemetry to IoT Hub?\"\n- \"I need an edge gateway for field devices\"\n- \"I want cloud-to-device commands and OTA configuration updates\"\n\n## Mandatory documentation review\n\nBefore recommending an IoT Edge topology or runtime behavior, review:\n\n- https:\u002F\u002Flearn.microsoft.com\u002Fazure\u002Fiot-edge\u002F\n\nIf documentation cannot be consulted, proceed with explicit assumptions and highlight them in a dedicated section.\n\n## Official Arduino references and best practices (required)\n\nBefore proposing firmware, wiring, or communication implementation details, consult official Arduino sources first:\n\n- https:\u002F\u002Fwww.arduino.cc\u002Fen\u002FGuide\n- https:\u002F\u002Fdocs.arduino.cc\u002F\n- https:\u002F\u002Fdocs.arduino.cc\u002Flanguage-reference\u002F\n- references\u002Farduino-official-best-practices.md\n\nWhen choosing between implementation alternatives, prioritize official Arduino guidance over community snippets unless there is a clear technical reason to deviate.\n\n## Objectives\n\n- Produce a secure end-to-end reference path from the Arduino device to cloud insights.\n- Handle unstable links (store-and-forward, retries, idempotency).\n- Define an actionable device and cloud backlog.\n\n## Integration patterns\n\n### Pattern A: Arduino direct to IoT Hub\n\nUse when connectivity is stable and cloud latency is acceptable.\n\n- Protocol: MQTT over TLS.\n- Identity: per-device credentials (SAS or X.509).\n- Telemetry payload: compact JSON with timestamp, device ID, metrics, and optional quality flags.\n\n### Pattern B: Arduino to local gateway, then IoT Edge\n\nUse when links are constrained, local control is required, or batching improves cost\u002Freliability.\n\n- Arduino communicates with a local gateway (serial, BLE, local MQTT, RS-485, Modbus bridge).\n- The gateway publishes upstream through the IoT Edge runtime and routes data to IoT Hub.\n- Local modules can filter, aggregate, and trigger actions even during cloud outages.\n\n## Design flow\n\n### 1) Device contract\n\nDefine:\n\n- Sensor catalog and units.\n- Sampling frequency and expected throughput.\n- Message schema versioning strategy.\n- Desired\u002Freported device twin properties to control runtime behavior.\n\n### 2) Security baseline\n\nRequire:\n\n- Unique identity per device.\n- No hardcoded secrets in source code or firmware artifacts.\n- Credential rotation strategy.\n- Signed firmware and a controlled update process when possible.\n\n### 3) Reliability and offline behavior\n\nPlan and document:\n\n- Backoff with jitter.\n- Local queue\u002Fbuffer strategy with bounded size.\n- Duplicate suppression or downstream idempotent processing.\n- Fallback to last-known-good configuration.\n\n### 4) Cloud and edge routing\n\nDefine routes for:\n\n- Raw telemetry to cold storage.\n- Curated telemetry to hot analytics.\n- Alerts to operations channels.\n- Commands and configuration back to edge\u002Fdevice.\n\n### 5) Observability\n\nSpecify minimum operations telemetry:\n\n- Device heartbeat and firmware version.\n- Connectivity state transitions.\n- Message send success\u002Ferror counters.\n- Gateway module health and restart reasons.\n\n## Reuse other skills\n\nWhen relevant, combine with:\n\n- `azure-smart-city-iot-solution-builder` for city-wide architecture and phased rollout.\n- `azure-resource-visualizer` for relationship diagrams.\n- `appinsights-instrumentation` for app and service telemetry patterns.\n\nAlso use `references\u002Farduino-official-best-practices.md` as a quality baseline for firmware and hardware recommendations, and `references\u002Farduino-iot-checklist.md` before finalizing architecture or implementation guidance.\n\n## Required output\n\nAlways provide:\n\n1. Chosen connectivity pattern and rationale.\n2. Message contract (fields, units, sample payload).\n3. Security checklist for identity\u002Fcredentials\u002Fupdates.\n4. Reliability plan (retry, buffering, dedupe).\n5. Implementation backlog (firmware, gateway, cloud).\n\n## Output template\n\n1. Scenario and assumptions\n2. Recommended architecture\n3. Device and gateway contract\n4. Security and reliability controls\n5. Deployment plan and validation tests\n\n## Guidelines\n\n- Do not propose production deployments with shared credentials across devices.\n- Do not assume always-on connectivity in field deployments.\n- Do not omit command authorization and auditing in actuator scenarios.\n","# Arduino Azure IoT Edge Integration\n\nUse 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).\n\n## When to use it\n\nUse this skill for requests such as:\n\n- \"I want to connect Arduino sensors to Azure\"\n- \"How do I send MQTT telemetry to IoT Hub?\"\n- \"I need an edge gateway for field devices\"\n- \"I want cloud-to-device commands and OTA configuration updates\"\n\n## Mandatory documentation review\n\nBefore recommending an IoT Edge topology or runtime behavior, review:\n\n- https:\u002F\u002Flearn.microsoft.com\u002Fazure\u002Fiot-edge\u002F\n\nIf documentation cannot be consulted, proceed with explicit assumptions and highlight them in a dedicated section.\n\n## Official Arduino references and best practices (required)\n\nBefore proposing firmware, wiring, or communication implementation details, consult official Arduino sources first:\n\n- https:\u002F\u002Fwww.arduino.cc\u002Fen\u002FGuide\n- https:\u002F\u002Fdocs.arduino.cc\u002F\n- https:\u002F\u002Fdocs.arduino.cc\u002Flanguage-reference\u002F\n- references\u002Farduino-official-best-practices.md\n\nWhen choosing between implementation alternatives, prioritize official Arduino guidance over community snippets unless there is a clear technical reason to deviate.\n\n## Objectives\n\n- Produce a secure end-to-end reference path from the Arduino device to cloud insights.\n- Handle unstable links (store-and-forward, retries, idempotency).\n- Define an actionable device and cloud backlog.\n\n## Integration patterns\n\n### Pattern A: Arduino direct to IoT Hub\n\nUse when connectivity is stable and cloud latency is acceptable.\n\n- Protocol: MQTT over TLS.\n- Identity: per-device credentials (SAS or X.509).\n- Telemetry payload: compact JSON with timestamp, device ID, metrics, and optional quality flags.\n\n### Pattern B: Arduino to local gateway, then IoT Edge\n\nUse when links are constrained, local control is required, or batching improves cost\u002Freliability.\n\n- Arduino communicates with a local gateway (serial, BLE, local MQTT, RS-485, Modbus bridge).\n- The gateway publishes upstream through the IoT Edge runtime and routes data to IoT Hub.\n- Local modules can filter, aggregate, and trigger actions even during cloud outages.\n\n## Design flow\n\n### 1) Device contract\n\nDefine:\n\n- Sensor catalog and units.\n- Sampling frequency and expected throughput.\n- Message schema versioning strategy.\n- Desired\u002Freported device twin properties to control runtime behavior.\n\n### 2) Security baseline\n\nRequire:\n\n- Unique identity per device.\n- No hardcoded secrets in source code or firmware artifacts.\n- Credential rotation strategy.\n- Signed firmware and a controlled update process when possible.\n\n### 3) Reliability and offline behavior\n\nPlan and document:\n\n- Backoff with jitter.\n- Local queue\u002Fbuffer strategy with bounded size.\n- Duplicate suppression or downstream idempotent processing.\n- Fallback to last-known-good configuration.\n\n### 4) Cloud and edge routing\n\nDefine routes for:\n\n- Raw telemetry to cold storage.\n- Curated telemetry to hot analytics.\n- Alerts to operations channels.\n- Commands and configuration back to edge\u002Fdevice.\n\n### 5) Observability\n\nSpecify minimum operations telemetry:\n\n- Device heartbeat and firmware version.\n- Connectivity state transitions.\n- Message send success\u002Ferror counters.\n- Gateway module health and restart reasons.\n\n## Reuse other skills\n\nWhen relevant, combine with:\n\n- `azure-smart-city-iot-solution-builder` for city-wide architecture and phased rollout.\n- `azure-resource-visualizer` for relationship diagrams.\n- `appinsights-instrumentation` for app and service telemetry patterns.\n\nAlso use `references\u002Farduino-official-best-practices.md` as a quality baseline for firmware and hardware recommendations, and `references\u002Farduino-iot-checklist.md` before finalizing architecture or implementation guidance.\n\n## Required output\n\nAlways provide:\n\n1. Chosen connectivity pattern and rationale.\n2. Message contract (fields, units, sample payload).\n3. Security checklist for identity\u002Fcredentials\u002Fupdates.\n4. Reliability plan (retry, buffering, dedupe).\n5. Implementation backlog (firmware, gateway, cloud).\n\n## Output template\n\n1. Scenario and assumptions\n2. Recommended architecture\n3. Device and gateway contract\n4. Security and reliability controls\n5. Deployment plan and validation tests\n\n## Guidelines\n\n- Do not propose production deployments with shared credentials across devices.\n- Do not assume always-on connectivity in field deployments.\n- Do not omit command authorization and auditing in actuator scenarios.\n",{"name":5,"description":7},"main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Farduino-azure-iot-edge-integration",5335,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot","https:\u002F\u002Fawesome-copilot.github.com\u002F","https:\u002F\u002Fgithub.com\u002Fgithub","https:\u002F\u002Fgithub.com\u002Fabout","How people build software.","2026-11-11 11:59:04",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add github\u002Fawesome-copilot --skill arduino-azure-iot-edge-integration",{"tool":64,"command":65},"bunx","bunx skills add github\u002Fawesome-copilot --skill arduino-azure-iot-edge-integration",{"tool":67,"command":68},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill arduino-azure-iot-edge-integration","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Farduino-azure-iot-edge-integration\u002FSKILL.md",true,[72,78],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Farduino-iot-checklist.md","REFERENCE","参考文档",1298,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Farduino-azure-iot-edge-integration\u002Freferences\u002Farduino-iot-checklist.md",{"path":79,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Farduino-official-best-practices.md",2337,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Farduino-azure-iot-edge-integration\u002Freferences\u002Farduino-official-best-practices.md",[83,115,143,172,206,223],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":95,"publishedAt":43},"01M70KXZNX9ES38N2A8M58ZZMZ","supabase-rls-policies","Write, review, and test Supabase Row Level Security (RLS) policies so that one user cannot read or change another user's rows. Use when creating or changing tables, policies, views, or security definer functions in a Supabase project, when reviewing a migration that touches RLS, or when asked to prove user isolation with pgTAP tests that sign in as two users.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fsupabase-rls-policies\u002FSKILL.md",[89,92],{"categoryNo":90,"categoryName":91,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":93,"categoryName":94,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,33],"supabase","rls","supabase rls","supabase rls policies","project","when","review","change","read","write","security","isolation","tables","as","tests","prove","use","users","functions",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":122,"publishedAt":43},"01M70KWA5YWXDV6V8DZ19E3T2Y","skill-distribution-audit","Audit a skill collection before release or investigate installer omissions, archived\u002Ftemplate leakage, duplicate identities, and manifest drift. Reconcile the intended public source set, distribution manifest, and actual discovery output for a specified consumer version. Includes a deterministic set-comparison helper.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fskill-distribution-audit\u002FSKILL.md",[121],{"categoryNo":93,"categoryName":94,"taxonomyType":21,"taxonomyTypeDesc":22},[123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142],"distribution","skill distribution audit","audit","SET","deterministic","installer","for","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":147,"sourceHtmlUrl":148,"categories":149,"keywords":155,"publishedAt":43},"01M70KCJPFVW7PPQ49CC5R8JGK","logicapps-custom-code-net10-migration","Migrate Azure Logic Apps Standard local-function custom-code projects from .NET 8 to .NET 10. Use for legacy Logic Apps custom-code workspace upgrades, project\u002Fpackage configuration, custom-worker settings, publication, and integrated VS Code debugging. Not for ordinary .NET applications, standalone Azure Functions, Consumption workflows, or cloud deployment.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FSKILL.md",[150],{"categoryNo":151,"categoryName":152,"taxonomyType":153,"taxonomyTypeDesc":154},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[156,157,158,159,160,161,129,100,162,163,164,25,165,166,167,112,168,169,170,171],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","deployment","applications","local","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":176,"sourceHtmlUrl":177,"categories":178,"keywords":183,"publishedAt":43},"01M6AEBN37RQMXJ3298PEY7PG1","winmd-api-search","Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI\u002FML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FSKILL.md",[179,182],{"categoryNo":180,"categoryName":181,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":151,"categoryName":152,"taxonomyType":153,"taxonomyTypeDesc":154},[184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,101,199,200,201,202,203,129,204,205],"winmd api search","search","api","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","features","full","camera","events","platform","methods","controls",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":210,"licenseFileUrl":211,"sourceHtmlUrl":212,"categories":213,"keywords":219,"publishedAt":43},"01M6AE571R3SC1ZGE202B3NB5S","resemble-detect","Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[214,215,218],{"categoryNo":180,"categoryName":181,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":216,"categoryName":217,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":93,"categoryName":94,"taxonomyType":21,"taxonomyTypeDesc":22},[220,126,193,221,222],"resemble detect","https","identity",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":210,"licenseFileUrl":227,"sourceHtmlUrl":228,"categories":229,"keywords":234,"publishedAt":43},"01M6AE30BSK2HCY4TQQ22Z8JRD","quality-playbook","Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[230,233],{"categoryNo":231,"categoryName":232,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":93,"categoryName":94,"taxonomyType":21,"taxonomyTypeDesc":22},[235,236,237,238,157,102,9,125,239,240,221,241,242,243,244,245,246,247,248,249,250,251,110,252,253,254,255],"playbook","quality playbook","quality","code review","tdd","run","report","spec","bug report","council","engineering","functional","spec audit","bug","verified","multi-model","three","coverage","model","regression","structural"]