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Activator Cli

OrganizationPopular
microsoft
activator-cli

Creates and inspects Fabric Activator (Reflex) alerts: rules, their data sources, conditions, and the Teams or email action they fire, plus decoding an existing ReflexEntities definition. Choose it whenever someone wants to be told, or something done, when values cross a threshold. The pipeline delivering those values is eventstream-cli.

Overview

Publishermicrosoft
Repositoryskills-for-fabric
Skill nameactivator-cli
Stars
1.2K
Forks
354
Bundled files
10
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.

  • 10 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 Activator 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/activator-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/activator-cli .claude/skills/activator-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Activator 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 Activator 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 Activator 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: 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

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 Activator Cli AI skill do?

Creates and inspects Fabric Activator (Reflex) alerts: rules, their data sources, conditions, and the Teams or email action they fire, plus decoding an existing ReflexEntities definition. Choose it whenever someone wants to be told, or something done, when values cross a threshold. The pipeline delivering those values is eventstream-cli.

Why use Activator Cli on TypingMind?

Because you install it once and use it with any model. Activator 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 Activator 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/activator-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 Activator 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 Activator Cli?

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

Is the Activator 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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