[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AE0YNBQBEPRQ5PYJY7DVMJ":3,"skill-same:01M6AE0YNBQBEPRQ5PYJY7DVMJ":94},{"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":18,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":45,"repositoryHtmlUrl":46,"repositoryHomepageUrl":47,"ownerHtmlUrl":48,"ownerBlogUrl":49,"ownerDescription":50,"updatedAt":51,"install":52,"resources":65},"01M6AE0YNBQBEPRQ5PYJY7DVMJ","python-azure-iot-edge-modules",null,"Build and operate Python Azure IoT Edge modules with robust messaging, deployment manifests, observability, and production readiness checks.","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41046,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fpython-azure-iot-edge-modules\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"azure","azure iot","azure iot edge","modules","python azure iot edge modules","edge","iot","python","Production","messaging","observability","build","production readiness","readiness","deployment","with","operate","manifests","2026-11-10 10:12:30",false,"---\nname: python-azure-iot-edge-modules\ndescription: 'Build and operate Python Azure IoT Edge modules with robust messaging, deployment manifests, observability, and production readiness checks.'\n---\n\n# Python Azure IoT Edge Modules\n\nUse this skill to design, implement, and validate Python-based IoT Edge modules for telemetry processing, local inference, protocol translation, and edge-to-cloud integration.\n\n## When To Use\n\nUse this skill for requests like:\n\n- \"quiero crear un modulo Python para IoT Edge\"\n- \"como despliego modulos edge con manifest\"\n- \"necesito filtrar\u002Fagregar telemetria antes de subirla\"\n- \"como manejo desconexiones y reintentos en edge\"\n\n## Mandatory Docs Review\n\nBefore recommending runtime behavior or deployment decisions, review:\n\n- https:\u002F\u002Flearn.microsoft.com\u002Fazure\u002Fiot-edge\u002F\n- https:\u002F\u002Flearn.microsoft.com\u002Fes-es\u002Fazure\u002Fiot-edge\u002F\n\nMinimum checks:\n\n- Runtime architecture and module lifecycle.\n- Supported host OS and versions.\n- Deployment model and configuration flow.\n- Current release\u002Fversion guidance.\n\nIf documentation cannot be fetched, proceed with explicit assumptions and flag them clearly.\n\n## Python Official References and Best Practices (Required)\n\nBefore proposing Python implementation details, consult official Python sources:\n\n- https:\u002F\u002Fwww.python.org\u002F\n- https:\u002F\u002Fdocs.python.org\u002F3\u002F\n- https:\u002F\u002Fdocs.python.org\u002F3\u002Freference\u002F\n- https:\u002F\u002Fdocs.python.org\u002F3\u002Flibrary\u002F\n- references\u002Fpython-official-best-practices.md\n\nPrefer official docs over community snippets unless there is a specific compatibility reason to deviate.\n\n## Goals\n\n- Deliver module architecture and implementation plan that is production-focused.\n- Ensure reliable edge messaging under network variability.\n- Provide deployment, observability, and validation artifacts.\n\n## Module Use Cases\n\n- Protocol adapter (serial\u002FModbus\u002FOPC-UA to IoT message format).\n- Telemetry enrichment and normalization.\n- Local anomaly detection or inference.\n- Command orchestration and local actuator control.\n\n## Delivery Workflow\n\n### 1) Contract and Interfaces\n\nDefine:\n\n- Module inputs and outputs.\n- Message schema and versioning policy.\n- Routes and priorities for normal vs critical telemetry.\n- Desired properties used for dynamic configuration.\n\n### 2) Runtime and Packaging\n\nSpecify:\n\n- Python runtime version target.\n- Container image strategy (base image, slim footprint, CVE hygiene).\n- Resource profile (CPU\u002Fmemory bounds).\n- Startup and health checks.\n\n### 3) Reliability Design\n\nImplement and validate:\n\n- Retries with exponential backoff and jitter.\n- Graceful degradation on upstream failures.\n- Local queueing strategy where needed.\n- Idempotent processing for replayed messages.\n\n### 4) Security Controls\n\nRequire:\n\n- No plaintext secrets in code or manifest.\n- Least-privilege module behavior.\n- Secure transport and trusted cert chain handling.\n- Traceability for command handling and state changes.\n\n### 5) Deployment and Operations\n\nDefine:\n\n- Environment-specific deployment manifests.\n- Rollout strategy (pilot, staged, broad).\n- Rollback criteria.\n- SLOs and alerting conditions.\n\n## Reuse Other Skills\n\nWhen relevant, combine with:\n\n- `azure-smart-city-iot-solution-builder` for platform-level architecture.\n- `appinsights-instrumentation` for telemetry instrumentation approaches.\n- `azure-resource-visualizer` for architecture diagrams and dependency mapping.\n\nAlso use `references\u002Fpython-official-best-practices.md` as baseline quality criteria for module design and implementation guidance.\n\n## Required Output\n\nAlways provide:\n\n1. Module design brief (purpose, inputs, outputs).\n2. Deployment model (image, manifest, env settings).\n3. Reliability and error-handling strategy.\n4. Security and operations checklist.\n5. Test matrix (functional, chaos, performance, rollback).\n\n## Output Template\n\nUse `references\u002Fpython-edge-module-template.md` to structure implementation proposals and reviews.\n\n1. Context and assumptions\n2. Module architecture\n3. Deployment and configuration\n4. Reliability, security, observability\n5. Validation and rollout plan\n\n## Guardrails\n\n- Do not recommend direct production rollout without pilot stage.\n- Do not embed secrets in Dockerfiles, source, or manifests.\n- Do not omit health probes, restart behavior, and rollback criteria.\n","# Python Azure IoT Edge Modules\n\nUse this skill to design, implement, and validate Python-based IoT Edge modules for telemetry processing, local inference, protocol translation, and edge-to-cloud integration.\n\n## When To Use\n\nUse this skill for requests like:\n\n- \"quiero crear un modulo Python para IoT Edge\"\n- \"como despliego modulos edge con manifest\"\n- \"necesito filtrar\u002Fagregar telemetria antes de subirla\"\n- \"como manejo desconexiones y reintentos en edge\"\n\n## Mandatory Docs Review\n\nBefore recommending runtime behavior or deployment decisions, review:\n\n- https:\u002F\u002Flearn.microsoft.com\u002Fazure\u002Fiot-edge\u002F\n- https:\u002F\u002Flearn.microsoft.com\u002Fes-es\u002Fazure\u002Fiot-edge\u002F\n\nMinimum checks:\n\n- Runtime architecture and module lifecycle.\n- Supported host OS and versions.\n- Deployment model and configuration flow.\n- Current release\u002Fversion guidance.\n\nIf documentation cannot be fetched, proceed with explicit assumptions and flag them clearly.\n\n## Python Official References and Best Practices (Required)\n\nBefore proposing Python implementation details, consult official Python sources:\n\n- https:\u002F\u002Fwww.python.org\u002F\n- https:\u002F\u002Fdocs.python.org\u002F3\u002F\n- https:\u002F\u002Fdocs.python.org\u002F3\u002Freference\u002F\n- https:\u002F\u002Fdocs.python.org\u002F3\u002Flibrary\u002F\n- references\u002Fpython-official-best-practices.md\n\nPrefer official docs over community snippets unless there is a specific compatibility reason to deviate.\n\n## Goals\n\n- Deliver module architecture and implementation plan that is production-focused.\n- Ensure reliable edge messaging under network variability.\n- Provide deployment, observability, and validation artifacts.\n\n## Module Use Cases\n\n- Protocol adapter (serial\u002FModbus\u002FOPC-UA to IoT message format).\n- Telemetry enrichment and normalization.\n- Local anomaly detection or inference.\n- Command orchestration and local actuator control.\n\n## Delivery Workflow\n\n### 1) Contract and Interfaces\n\nDefine:\n\n- Module inputs and outputs.\n- Message schema and versioning policy.\n- Routes and priorities for normal vs critical telemetry.\n- Desired properties used for dynamic configuration.\n\n### 2) Runtime and Packaging\n\nSpecify:\n\n- Python runtime version target.\n- Container image strategy (base image, slim footprint, CVE hygiene).\n- Resource profile (CPU\u002Fmemory bounds).\n- Startup and health checks.\n\n### 3) Reliability Design\n\nImplement and validate:\n\n- Retries with exponential backoff and jitter.\n- Graceful degradation on upstream failures.\n- Local queueing strategy where needed.\n- Idempotent processing for replayed messages.\n\n### 4) Security Controls\n\nRequire:\n\n- No plaintext secrets in code or manifest.\n- Least-privilege module behavior.\n- Secure transport and trusted cert chain handling.\n- Traceability for command handling and state changes.\n\n### 5) Deployment and Operations\n\nDefine:\n\n- Environment-specific deployment manifests.\n- Rollout strategy (pilot, staged, broad).\n- Rollback criteria.\n- SLOs and alerting conditions.\n\n## Reuse Other Skills\n\nWhen relevant, combine with:\n\n- `azure-smart-city-iot-solution-builder` for platform-level architecture.\n- `appinsights-instrumentation` for telemetry instrumentation approaches.\n- `azure-resource-visualizer` for architecture diagrams and dependency mapping.\n\nAlso use `references\u002Fpython-official-best-practices.md` as baseline quality criteria for module design and implementation guidance.\n\n## Required Output\n\nAlways provide:\n\n1. Module design brief (purpose, inputs, outputs).\n2. Deployment model (image, manifest, env settings).\n3. Reliability and error-handling strategy.\n4. Security and operations checklist.\n5. Test matrix (functional, chaos, performance, rollback).\n\n## Output Template\n\nUse `references\u002Fpython-edge-module-template.md` to structure implementation proposals and reviews.\n\n1. Context and assumptions\n2. Module architecture\n3. Deployment and configuration\n4. Reliability, security, observability\n5. Validation and rollout plan\n\n## Guardrails\n\n- Do not recommend direct production rollout without pilot stage.\n- Do not embed secrets in Dockerfiles, source, or manifests.\n- Do not omit health probes, restart behavior, and rollback criteria.\n",{"name":5,"description":7},"main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Fpython-azure-iot-edge-modules",5340,"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 13:41:32",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add github\u002Fawesome-copilot --skill python-azure-iot-edge-modules",{"tool":58,"command":59},"bunx","bunx skills add github\u002Fawesome-copilot --skill python-azure-iot-edge-modules",{"tool":61,"command":62},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill python-azure-iot-edge-modules","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fpython-azure-iot-edge-modules\u002FSKILL.md",true,[66,72,76,80,84,88],{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"README.md","REFERENCE","参考文档",896,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fkairos-lite\u002FREADME.md",{"path":73,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fprompt-template.md",592,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fkairos-lite\u002Freferences\u002Fprompt-template.md",{"path":77,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fpython-edge-module-template.md",1339,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fpython-azure-iot-edge-modules\u002Freferences\u002Fpython-edge-module-template.md",{"path":81,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fpython-official-best-practices.md",2223,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fpython-azure-iot-edge-modules\u002Freferences\u002Fpython-official-best-practices.md",{"path":85,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fsource-notes.md",378,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fkairos-lite\u002Freferences\u002Fsource-notes.md",{"path":89,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":92,"sourceHtmlUrl":93},"scripts\u002Fjob_spec.py","SCRIPT","脚本",986,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fkairos-lite\u002Fscripts\u002Fjob_spec.py",[95,129,157,185,219,236],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":109,"publishedAt":37},"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",[101,106],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":107,"categoryName":108,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000023","Skill与智能体开发",[110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,34],"supabase","rls","supabase rls","supabase rls policies","project","when","review","change","read","write","security","isolation","tables","as","tests","prove","use","users","functions",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":136,"publishedAt":37},"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",[135],{"categoryNo":107,"categoryName":108,"taxonomyType":104,"taxonomyTypeDesc":105},[137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156],"distribution","skill distribution audit","audit","SET","deterministic","installer","for","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":161,"sourceHtmlUrl":162,"categories":163,"keywords":169,"publishedAt":37},"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",[164],{"categoryNo":165,"categoryName":166,"taxonomyType":167,"taxonomyTypeDesc":168},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[170,171,172,173,174,175,143,114,33,176,177,19,178,179,180,126,181,182,183,184],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","applications","local","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":189,"sourceHtmlUrl":190,"categories":191,"keywords":196,"publishedAt":37},"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",[192,195],{"categoryNo":193,"categoryName":194,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":165,"categoryName":166,"taxonomyType":167,"taxonomyTypeDesc":168},[197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,115,212,213,214,215,216,143,217,218],"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":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":223,"licenseFileUrl":224,"sourceHtmlUrl":225,"categories":226,"keywords":232,"publishedAt":37},"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",[227,228,231],{"categoryNo":193,"categoryName":194,"taxonomyType":104,"taxonomyTypeDesc":105},{"categoryNo":229,"categoryName":230,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":107,"categoryName":108,"taxonomyType":104,"taxonomyTypeDesc":105},[233,140,206,234,235],"resemble detect","https","identity",{"skillNo":237,"name":238,"nameZh":6,"description":239,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":223,"licenseFileUrl":240,"sourceHtmlUrl":241,"categories":242,"keywords":247,"publishedAt":37},"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",[243,246],{"categoryNo":244,"categoryName":245,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":107,"categoryName":108,"taxonomyType":104,"taxonomyTypeDesc":105},[248,249,250,251,171,116,9,139,252,253,234,254,255,256,257,258,259,260,261,262,263,264,124,265,266,267,268],"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"]