eventschemaset-cli

bởi microsoft

Tác giả và kiểm tra Microsoft Fabric Event Schema Sets (danh mục tập trung về các loại sự kiện và lược đồ thông điệp) thông qua Fabric Items REST API với az rest:…

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

Telemetry — MANDATORY. Every api.fabric.microsoft.com call must carry x-ms-fabric-skill: eventschemaset-cli (az rest: --headers "x-ms-fabric-skill=eventschemaset-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 eventschemaset-cli for the EventSchemaSet catalog item (event types and message schemas). Eventstream is a different item -- a real-time ingestion and routing pipeline; use eventstream-cli for it. Eventhouse / KQL Database is another different item; use eventhouse-cli for it.

Fabric Event Schema Set -- CLI Skill

This one skill owns Fabric Event Schema Sets: authoring the catalog (create, rename, override definition, delete) plus read-only inspection and decoding of its eventTypes and schemas.

Preview: The Fabric EventSchemaSet item and its REST operations are currently in Preview -- behavior, response shapes, and availability may change. Every operation is documented as delegated (user) identity only (service principals and managed identities listed as not supported), but because the item is in Preview, SP/MI access can succeed and is tenant-dependent. Prefer az login as a user; on 401/403 with an SP/MI, fall back to a delegated user identity.

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, payload shapes, templates and gotchas; acting without it produces wrong payloads and wrong results.

Mode selection

ModeUse when the request ...Example triggersRead this first
authoringcreates, renames/re-describes, overrides the definition of, or deletes an Event Schema Set (Create, Update, UpdateDefinition, Delete)create event schema set, update event schema set definition, add event type to an event schema set, rename event schema set, delete event schema setreferences/authoring.md
consumptionlists or searches Event Schema Sets, inspects item properties, or decodes a definition to enumerate its eventTypes and schemaslist event schema sets, inspect event schema set, decode event schema set definition, get schema version, list business eventsreferences/consumption.md

Mode boundary rule

consumption may only issue read-only calls (List, Get, GetDefinition). Any state-changing operation (Create, Update, UpdateDefinition, Delete) requires the authoring mode: say so, read references/authoring.md, then proceed.

If a request genuinely spans modes (e.g. decode the current definition, then override it), 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 and planning the change is NOT completing the task. The authoring mode ends with one state-changing call. If you did not issue it, nothing was persisted -- say so explicitly rather than reporting success.

ModeTerminal write
authoringThe POST .../eventSchemaSets, PATCH .../eventSchemaSets/{id}, POST .../eventSchemaSets/{id}/updateDefinition, or DELETE .../eventSchemaSets/{id} for the requested change. Composing the request body and showing it to the user is not executing it.
consumptionnone -- this mode is read-only

Create and UpdateDefinition may return 202 Accepted (LRO): capture the Location header and poll until Succeeded before reporting success. Where the reference documents a readback, read the artefact back to prove the change landed.

Shared essentials (all modes)

Resolve the workspace and item first; every mode depends on it.

TaskReferenceNotes
Finding Workspaces and Items in FabricCOMMON-CLI.mdMandatory -- read before resolving any workspace or item id
Fabric Topology & Key ConceptsCOMMON-CORE.mdItem types, workspaces, capacities
Environment URLsCOMMON-CORE.mdSovereign / non-public cloud hosts
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
Gotchas & TroubleshootingCOMMON-CLI.mdaz rest audience, shell escaping, token expiry

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. Read it ONCE, in a single full read: do not re-open it, do not grep it again, and do not page through it. You already have it.
  • Apply the same read-once discipline to every other file you load, including shared common/*.md files: open only the ones you actually need, read each ONCE end to end, and never grep or re-open one you already loaded.
  • Resolve workspace and item ids by listing and filtering, never by guessing a GUID.
  • Announce a mode switch explicitly when the request crosses a boundary.
  • Confirm destructive and overwrite actions before executing -- Delete is permanent (with ?hardDelete=true) and UpdateDefinition replaces the whole definition. When the request already names the target item AND the destructive/overwrite action explicitly (e.g. "delete the Event Schema Set X", "override X's definition to ..."), that instruction IS the confirmation: echo the resolved displayName + id for the audit trail, then proceed with the single write. Pause for a separate yes/no confirmation ONLY when the target or intent is ambiguous (e.g. "clean up my workspace", an unqualified "the first one", or a bulk/wildcard delete).
  • 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.

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.
  • A delegated user identity (az login); fall back from SP/MI to a user on 401/403.
  • Reporting the mode you chose in your first response so the user can correct you.

AVOID

  • Acting from this dispatcher alone -- it intentionally omits the operational detail.
  • Answering with a summary of the reference instead of executing it.
  • Re-reading or re-grepping a reference you already loaded; it costs turns and tokens.
  • Mutating anything while in the read-only consumption mode.
  • Confusing EventSchemaSet with Eventstream or Eventhouse (see CRITICAL NOTES 3).

Examples

User requestModeReference to read
"List all Event Schema Sets in my Analytics workspace."consumptionreferences/consumption.md
"Create an Event Schema Set called orders-catalog in Analytics."authoringreferences/authoring.md
"Decode the definition of orders-catalog and tell me its event types and schemas."consumptionreferences/consumption.md
"Add a BusinessEventType event OrderPlaced (JSON) to orders-catalog."authoringreferences/authoring.md
"Delete the orders-catalog-old Event Schema Set from Analytics."authoringreferences/authoring.md

Thêm skills từ microsoft

oss-growth
microsoft
Cá tính tăng trưởng OSS
agent-framework-azure-ai-py
microsoft
Xây dựng các tác nhân Azure AI Foundry bằng SDK Python của Microsoft Agent Framework (agent-framework-azure-ai). Sử dụng khi tạo các tác nhân bền vững với AzureAIAgentsProvider, sử dụng các công cụ được lưu trữ (trình thông dịch mã, tìm kiếm tệp, tìm kiếm web), tích hợp máy chủ MCP, quản lý chuỗi hội thoại hoặc triển khai phản hồi phát trực tuyến. Bao gồm các công cụ hàm, đầu ra có cấu trúc và các tác nhân đa công cụ.
development
airunway-aks-setup
microsoft
Thiết lập AI Runway trên AKS — từ cụm trống đến mô hình đang chạy. Bao gồm xác minh cụm, cài đặt controller, đánh giá GPU, thiết lập nhà cung cấp và triển khai đầu tiên. KHI NÀO: "thiết lập AI Runway", "onboard cụm AKS", "cài đặt AI Runway", "thiết lập airunway", "triển khai mô hình lên AKS", "suy luận GPU trên AKS", "thiết lập KAITO trên AKS", "chạy LLM trên AKS", "vLLM trên AKS", "thiết lập phục vụ mô hình trên AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Hướng dẫn để instrument các ứng dụng web với Azure Application Insights. Cung cấp các mẫu telemetry, thiết lập SDK, và tài liệu tham khảo cấu hình. KHI NÀO: cách instrument ứng dụng, App Insights SDK, các mẫu telemetry, App Insights là gì, hướng dẫn Application Insights, ví dụ instrumentation, các phương pháp tốt nhất APM.
devops
applicationinsights-web-ts
microsoft
Instrument các ứng dụng trình duyệt/web bằng SDK JavaScript Application Insights (@microsoft/applicationinsights-web). Dùng cho Real User Monitoring (RUM) — lượt xem trang, nhấp chuột, phụ thuộc AJAX/fetch, ngoại lệ, sự kiện tùy chỉnh và dấu vết tác nhân GenAI phía trình duyệt tương quan với dấu vết OpenTelemetry phía backend. Bao gồm thiết lập SDK Loader Script và npm, tiện ích mở rộng framework (React, React Native, Angular), Click Analytics, trình khởi tạo telemetry và quy ước ngữ nghĩa OTel GenAI cho các span tác nhân/công cụ/mô hình phát ra từ trình duyệt.
devops
azure-ai-anomalydetector-java
microsoft
Xây dựng ứng dụng phát hiện bất thường với Azure AI Anomaly Detector SDK cho Java. Sử dụng khi triển khai phát hiện bất thường đơn biến/đa biến, phân tích chuỗi thời gian hoặc giám sát hỗ trợ AI.
development
azure-ai-language-conversations-py
microsoft
Triển khai Conversational Language Understanding (CLU) bằng SDK Python azure-ai-language-conversations. Sử dụng khi làm việc với ConversationAnalysisClient để phân tích ý định và thực thể trong hội thoại, xây dựng tính năng NLP, hoặc tích hợp hiểu ngôn ngữ vào ứng dụng.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 cho Python. Dùng cho không gian làm việc ML, công việc, mô hình, tập dữ liệu, tính toán và quy trình. Kích hoạt: "azure-ai-ml", "MLClient", "không gian làm việc", "đăng ký mô hình", "công việc đào tạo", "tập dữ liệu".
development