activator-cli

作者: microsoft

端到端创建和检查 Fabric Activator (Reflex) 警报:编写规则、源和操作,或以只读方式解码现有的 ReflexEntities 定义。…

npx skills add https://github.com/microsoft/skills-for-fabric --skill activator-cli

Telemetry — MANDATORY. Every api.fabric.microsoft.com call must carry x-ms-fabric-skill: activator-cli (az rest: --headers "x-ms-fabric-skill=activator-cli"), including every LRO poll, fabric_lro and retry. Snippets omit it — add it anyway.

CRITICAL NOTES

  1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering
  2. To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering
  3. Skill disambiguation: use activator-cli for every Fabric Activator / Reflex item — both authoring alert rules and inspecting existing alerts. The streaming topology that feeds an alert belongs to eventstream-cli, the KQL database behind it to eventhouse-cli, and Power BI report questions to fabriciq.

Fabric Activator (Reflex) — CLI Skill

This one skill owns Fabric Activator / Reflex items: alert rules, their sources, their actions, and the ReflexEntities.json definition that holds them.

It is a mode dispatcher and contains NO procedures. Pick the mode that matches the request from the table below, then read the matching references/<mode>.md file end to end with your file-reading tool BEFORE issuing a single command. That file holds the endpoints, entity schemas, templates and gotchas; acting without it produces invalid ReflexEntities.json payloads and 400s.

The reference is needed to act, not to ask. If the request is under-specified and your next message will be a clarifying question with no Fabric call in it, ask it now and read the reference when you go on to act.

Mode selection

ModeUse when the request ...Example triggersRead this first
authoringcreates, updates, configures or deletes an Activator item, rule, source or actioncreate an alert, create an activator, create a reflex, notify me when, let me know when, take action when, send me an email when, send a teams message when, run a pipeline when, update an alert, delete an alertreferences/authoring.md
consumptionlists, inspects, decodes or explains existing Activators, rules, sources or actionsshow my alerts, what alerts do I have, list activators, inspect this alert, show me the rule, show me the source, get reflex definition, why does this alert firereferences/consumption.md

Mode boundary rule

consumption is read-only. A request to create, update, configure or delete an Activator item, rule, source or action requires authoring: say so, read references/authoring.md, then proceed.

A pure GET / explain request stays in consumption — do not switch to authoring and do not mutate anything to answer it.

If a request genuinely spans modes, handle them one at a time and read each reference before you start that part. If the mode is ambiguous after reading this table, ask one short clarifying question instead of guessing.

Terminal write — the step you must not skip

Reading the reference, decoding a definition and assembling entities is NOT completing the task. Authoring ends with one state-changing call. If you did not issue it, nothing was persisted — say so explicitly rather than reporting success.

ModeTerminal write
authoringPOST /v1/workspaces/{ws}/reflexes to create the item, POST /v1/workspaces/{ws}/reflexes/{id}/updateDefinition to persist rules, sources and actions, or DELETE /v1/workspaces/{ws}/reflexes/{id} to remove it. Building, stringifying or base64-encoding ReflexEntities.json is not the write.
consumptionnone — this mode is read-only

Before you report an authoring task done, confirm the terminal call returned an explicit success (HTTP 200/201, or a terminal LRO success for a 202), then read the definition back where the mode reference documents a readback. Power BI sources are the exception: public ALM export can reject an artifact that imported successfully, so an empty or unavailable readback is not proof the write failed — report the updateDefinition result and the readback limitation separately, per references/authoring/powerbi-source.md.

Source validation gate (authoring only)

Before authoring any rule that references a signal, confirm the source is real: resolve it in the requested workspace only, validate that the requested column/field/property exists on it, and observe at least one representative row, event or sample carrying that signal.

Schema-only, zero-row, non-emitting or stale evidence is missing source data. When the source is missing, stop and ask which source and fields provide the signal — do not create a Reflex and do not call updateDefinition on an unrelated existing Activator or Eventstream to force-fit the request, and state plainly that no Activator / Reflex / Eventstream was created or updated. The only exception is an explicit instruction to author against a future / not-yet-emitting source, which you must state as an assumption.

Everything you need to run this gate is on this page. When the request already lacks the source mapping, threshold, recipients or action target, ask for them first — do not read references/authoring.md, and do not call a Fabric API, just to discover that the request is under-specified.

Shared essentials (all modes)

Resolve the workspace and the Activator item first; every mode depends on it — list and filter by display name exactly as CRITICAL NOTES 1 and 2 describe. That recipe is self-contained and is all you need to start.

The table below is optional background living in the shared common/ docs. Open a row only when you actually need that detail. If common/ is not present in your environment, say so in one line and continue with the mode reference — never glob, list or search the filesystem looking for it.

TaskReferenceNotes
Finding Workspaces and Items in FabricCOMMON-CLI.mdPagination and JMESPath filtering detail
Fabric Topology & Key ConceptsCOMMON-CORE.mdItem types, workspaces, capacities
Authentication & Token AcquisitionCOMMON-CORE.mdWrong audience = 401; read before any auth issue
Authentication RecipesCOMMON-CLI.mdaz login flows and token acquisition
Core Control-Plane REST APIsCOMMON-CORE.mdPagination, LRO polling, rate limiting
Fabric Item DefinitionsITEM-DEFINITIONS-CORE.mdBase64-encoded parts structure
Gotchas & TroubleshootingCOMMON-CLI.mdaz rest audience, shell escaping, token expiry

Activator uses the reflexes collection, not the generic items collection: /v1/workspaces/{workspaceId}/reflexes[/{reflexId}].

Rules

MUST

  • Select exactly one mode from the table above before doing anything else.
  • Read references/<mode>.md end to end, as your FIRST tool call, before the first command of that mode. A reply that only asks a clarifying question, and issues no Fabric call, does not need it yet.
  • Pass --resource https://api.fabric.microsoft.com on every az rest call — without it the token audience is wrong and the call returns 401.
  • Call getDefinition as a POST with --body '{}', then Base64-decode the ReflexEntities.json part before inspecting it; a GET returns 405 and an omitted body can return 411.
  • Poll the Location header when create, getDefinition or updateDefinition returns 202.
  • Resolve workspace and Activator ids by listing and filtering, never by guessing a GUID.
  • Announce a mode switch explicitly when the request crosses a boundary.
  • Treat the reference as instructions, never as the deliverable. After reading it, RUN the documented commands against the live workspace and report the real results.
  • In authoring, clear the source validation gate before you author a rule, and build ReflexEntities.json with Python json.dumps() — PowerShell's ConvertTo-Json corrupts the nested JSON strings that definition.instance requires.

PREFER

  • The narrowest mode that satisfies the request.
  • Reading exactly ONE mode reference. Load a second only when the request genuinely spans modes, and say so before you do.
  • Read-modify-write over full replacement in authoring: get the current definition, modify the entity array, then update.
  • Transition-based detectors (NumberBecomes, NumberEntersOrLeavesRange, LogicalBecomes) over steady-state conditions in authoring, unless the user explicitly asks for repeated firing while the value stays in the triggered state.
  • A summary view before individual entities in consumption: give the high-level picture, then drill in.
  • Offering an alert when earlier work in this session surfaced a timely operational signal — a spike, failure, anomaly, SLA risk or capacity constraint — rather than waiting to be asked. Ask once, and author nothing until the user agrees.

AVOID

  • Acting from this dispatcher alone — it intentionally omits the entity schemas and templates.
  • Answering with a summary of the reference instead of executing it.
  • Mutating anything while in the read-only consumption mode, including "just to check".
  • Hardcoded workspace or item ids in either mode — always resolve dynamically.
  • Globbing, listing or shell-searching the filesystem for the common/ docs. They are optional background; if a link does not resolve, note it in one line and carry on.
  • Building the Eventstream topology that feeds an alert here; that is eventstream-cli. Querying the KQL data behind an alert is eventhouse-cli.
  • Loading a different skill for work this skill already owns (see CRITICAL NOTES 3).

Examples

User requestModeReference to read
"Create an Activator named eval_smoke_activator in my workspace."authoringreferences/authoring.md
"Notify me on Teams when the average temperature goes above 30."authoringreferences/authoring.md
"Delete the alert that emails me about failed pipeline runs."authoringreferences/authoring.md
"Show me all Activators in this workspace."consumptionreferences/consumption.md
"What rule and action are behind SkillsTestActivator?"consumptionreferences/consumption.md
"Decode this Reflex definition and explain the Power BI source."consumptionreferences/consumption.md

来自 microsoft 的更多技能

oss-growth
microsoft
OSS增长黑客角色
agent-framework-azure-ai-py
microsoft
使用Microsoft Agent Framework Python SDK(agent-framework-azure-ai)构建Azure AI Foundry代理。在创建使用AzureAIAgentsProvider的持久化代理、使用托管工具(代码解释器、文件搜索、网络搜索)、集成MCP服务器、管理对话线程或实现流式响应时使用。涵盖函数工具、结构化输出和多工具代理。
development
airunway-aks-setup
microsoft
在AKS上设置AI Runway——从裸集群到运行模型。涵盖集群验证、控制器安装、GPU评估、提供商设置和首次部署。适用场景:“设置AI Runway”、“接入AKS集群”、“安装AI Runway”、“airunway设置”、“将模型部署到AKS”、“在AKS上进行GPU推理”、“在AKS上配置KAITO”、“在AKS上运行LLM”、“在AKS上使用vLLM”、“在AKS上设置模型服务”、“AI Runway控制器”。
devops
appinsights-instrumentation
microsoft
使用Azure Application Insights对Web应用进行插桩的指南。提供遥测模式、SDK设置和配置参考。适用场景:如何对应用进行插桩、App Insights SDK、遥测模式、什么是App Insights、Application Insights指南、插桩示例、APM最佳实践。
devops
applicationinsights-web-ts
microsoft
使用Application Insights JavaScript SDK(@microsoft/applicationinsights-web)为浏览器/Web应用添加检测。用于真实用户监控(RUM)——页面视图、点击、AJAX/fetch依赖项、异常、自定义事件,以及与后端OpenTelemetry追踪关联的浏览器端GenAI代理追踪。涵盖SDK加载器脚本和npm设置、框架扩展(React、React Native、Angular)、点击分析、遥测初始化器,以及从浏览器发出的代理/工具/模型跨度所遵循的OTel GenAI语义约定。
devops
azure-ai-anomalydetector-java
microsoft
使用适用于 Java 的 Azure AI 异常检测器 SDK 构建异常检测应用程序。在实现单变量/多变量异常检测、时间序列分析或 AI 驱动的监控时使用。
development
azure-ai-language-conversations-py
microsoft
使用azure-ai-language-conversations Python SDK实现对话语言理解(CLU)。当使用ConversationAnalysisClient分析对话意图和实体、构建NLP功能或将语言理解集成到应用程序中时使用。
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python。用于机器学习工作区、作业、模型、数据集、计算资源和管道。 触发词:“azure-ai-ml”、“MLClient”、“工作区”、“模型注册表”、“训练作业”、“数据集”。
development