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Eventstream Cli

OrganizationPopular
microsoft
eventstream-cli

Owns Fabric Eventstream items end to end: sources, operators, destinations, routing, retention, throughput and connection strings, plus read-only topology checks. Pick it for anything that names an Eventstream, creating and listing included. Where the rows finally land is eventhouse-cli.

Overview

Publishermicrosoft
Repositoryskills-for-fabric
Skill nameeventstream-cli
Stars
1.2K
Forks
354
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Eventstream Cli AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/microsoft/skills-for-fabric/tree/main/plugins/fabric-skills/skills/eventstream-cli
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/skills-for-fabric.git /tmp/skills-for-fabric
mkdir -p .claude/skills
cp -r /tmp/skills-for-fabric/plugins/fabric-skills/skills/eventstream-cli .claude/skills/eventstream-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Eventstream Cli in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Eventstream Cli on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Eventstream Cli is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Telemetry — MANDATORY. Every api.fabric.microsoft.com call must carry x-ms-fabric-skill: eventstream-cli (az rest: --headers "x-ms-fabric-skill=eventstream-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 eventstream-cli for the Eventstream item itself -- how events flow from source through operators to destinations. Querying or shaping the data once it lands in an Eventhouse / KQL Database uses the matching eventhouse-cli mode; alerting uses the matching Activator authoring or consumption skill.

Fabric Eventstream -- CLI Skill

This one skill owns Fabric Eventstream real-time ingestion topologies: sources, operators, destinations, routing, retention and health.

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, updates, wires, pauses, resumes or deletes an Eventstream topologycreate eventstream, deploy topology, add source, add filter operator, wire destination, update definitionreferences/authoring.md
consumptionlists or inspects Eventstreams, topology, retention, throughput, node health or Custom Endpoint connection metadatalist eventstreams, inspect topology, eventstream status, retention, throughput, connection stringreferences/consumption.md

Mode boundary rule

consumption is read-only for Eventstream definitions and topology. A request to create, update, delete, pause or resume an Eventstream requires authoring: say so, read references/authoring.md, then proceed.

Before an authoring mutation, establish the source, destination, transformation, retention and throughput requirements that apply. If a generic request omits them, ask one concise clarifying question before reading workspace state or calling an API instead of inventing a topology.

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 and planning the topology is NOT completing the task. Each mutating 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
authoringPOST /v1/workspaces/{ws}/items or /eventstreams to create, POST .../updateDefinition to persist topology changes, bodyless POST .../pause or POST .../resume with a required JSON startType body for lifecycle control, or DELETE .../eventstreams/{id} to remove the item. Building or base64-encoding eventstream.json is not the write.
consumptionnone -- this mode is read-only

Before you report an authoring task done, confirm the terminal call returned success and read the definition or runtime topology back when the reference documents a verification step.

Shared essentials (all modes)

Resolve the workspace and Eventstream 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.
  • Resolve workspace and Eventstream 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.
  • Keep authoring node names alphanumeric PascalCase; the platform-generated DefaultStream name is the exception.
  • Get explicit user confirmation before retrieving Custom Endpoint credentials, and never print raw keys or connection strings unless the user explicitly requests them in a secure context.

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.
  • Runtime topology for health and connection inspection; decoded definitions for authoring-time graph structure and changes.

AVOID

  • Acting from this dispatcher alone -- it intentionally omits the operational detail.
  • Answering with a summary of the reference instead of executing it.
  • Mutating anything while in the read-only consumption mode.
  • Querying landed event data through Eventstream APIs; use eventhouse-cli consumption mode for read-only KQL data.
  • Loading a different skill for work this skill already owns (see CRITICAL NOTES 3).

Examples

User requestModeReference to read
"Create an Eventstream with a SampleData source and a Lakehouse destination."authoringreferences/authoring.md
"Add a filter for temperature above 30 to my Eventstream."authoringreferences/authoring.md
"Show every source, operator and destination in SensorIngestion."consumptionreferences/consumption.md
"What retention and throughput settings does this Eventstream use?"consumptionreferences/consumption.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Eventstream Cli AI skill do?

Owns Fabric Eventstream items end to end: sources, operators, destinations, routing, retention, throughput and connection strings, plus read-only topology checks. Pick it for anything that names an Eventstream, creating and listing included. Where the rows finally land is eventhouse-cli.

Why use Eventstream Cli on TypingMind?

Because you install it once and use it with any model. Eventstream Cli is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Eventstream Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/skills-for-fabric/tree/main/plugins/fabric-skills/skills/eventstream-cli. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Eventstream Cli?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Eventstream Cli?

As many as you like. As long as a model supports skills, you can use Eventstream Cli with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Eventstream Cli AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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