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eventstream-consumption-cli

Inspects Microsoft Fabric Eventstream pipelines via REST API and decodes base-64 topologies to trace flows.

Updated Jun 1, 2026
Search Tags:#pipeline monitoring#configuration validation#eventstream#microsoft fabric#real-time ingestion#topology decoding
One-click install
npx skills add https://github.com/weiflycc-cmd/skills-for-fabric --skill eventstream-consumption-cli-weiflycc-cmd
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eventstream-consumption-cli
Source: https://github.com/weiflycc-cmd/skills-for-fabric/tree/main/plugins/fabric-skills/skills/eventstream-consumption-cli
Command: npx skills add https://github.com/weiflycc-cmd/skills-for-fabric --skill eventstream-consumption-cli-weiflycc-cmd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Users managing Microsoft Fabric real-time data pipelines struggle to quickly discover Eventstreams across workspaces, trace event flow from source to destination, and validate critical configuration settings like retention policies and throughput levels without manual UI navigation, leading to slow troubleshooting and undetected misconfigurations.

Core Features & Use Cases

  • List and discover Eventstreams: Quickly find all Eventstreams in a Fabric workspace, filter by name, and retrieve full item details via the REST API.
  • Decode event topology: Extract and decode base64-encoded graph definitions to visualize the full event flow from sources through operators to destinations.
  • Validate pipeline configuration: Check source connection IDs, destination wiring, retention policies (1-90 days), and throughput levels to catch misconfigurations before they cause data loss or pipeline failures. Use case example: If your real-time customer event pipeline is not delivering data to the target Lakehouse, use this skill to decode the Eventstream topology and verify all source and destination connections are correctly configured.

Quick Start

Use the eventstream-consumption-cli skill to list all Eventstreams in your Fabric workspace and decode the topology of the 'customer-events' pipeline to trace its end-to-end event flow.

Frequently Asked Questions about eventstream-consumption-cli

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I trace event flow from source to destination in a Fabric Eventstream?▼

To trace event flow in a Fabric Eventstream, you need to decode the base64-encoded graph topology. This extracts the pipeline definition to visualize the complete event path from sources through operators to destinations.

How do I list all Eventstreams across Microsoft Fabric workspaces?▼

You can list all Eventstreams across Microsoft Fabric workspaces by querying the Fabric Items REST API. This allows you to discover real-time ingestion pipelines, filter by name, and retrieve full item details without manual UI navigation.

Why is my real-time Eventstream pipeline not delivering data to the target Lakehouse?▼

An Eventstream pipeline might fail to deliver data to a Lakehouse due to incorrect destination wiring or invalid source connection IDs. Decode the topology to cross-reference downstream workloads and validate the pipeline configuration.

What retention policy and throughput levels can I validate for an Eventstream?▼

You can validate Eventstream retention policies ranging from 1 to 90 days and check configured throughput levels. Verifying these settings helps catch misconfigurations that could cause data loss or pipeline failures.

Can I validate Eventstream source connections without accessing the Fabric UI?▼

Yes, you can validate source connection IDs and destination wiring read-only via the Fabric Items REST API. This cross-references downstream workloads like Eventhouse and Lakehouse for end-to-end pipeline validation without UI access.

Does this approach support modifying Eventstream pipelines or is it read-only?▼

This approach supports read-only consumption workflows for Eventstream management. It is designed for inspecting topologies and validating configurations rather than modifying the real-time ingestion pipelines.

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