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As I've been working on efficiently using the Fabric CLI with coding agents (GitHub Copilot, Codex), I've come up with the following repo.
In this repo, I created skills that you can install locally, and your coding agent can pick them up depending on the context. For example, if you want to get details about a recent failed pipeline, you can ask your agent:
"What was the cause of my pipeline 'bronze_load' in the playground workspace?"
It will automatically pick up the fab-job-ops skill and dig into the root cause of the pipeline's failure. Under the hood, it uses the Fabric CLI.
For installing all necessary dependencies, see the README of the repo. The simplest way is to clone the repo and install the skills first. After that, use the fab-bootstrap skill to install the Fabric CLI and authenticate against Fabric. From there on, it’s only up to your imagination what you want to create.
For example, you could say:
"Create a logistics workspace attached to my capacity and generate dummy data. Create bronze, silver, and gold schema."
There are also conventions baked in, such as naming conventions for folders, items, columns, and more.
Additionally, when modeling a semantic model, several common best practices are already included.
GitHub repository: dc-floriangaerner/fab-cli-skills
Florian — thank you for sharing this. Just cloned the repo and installed seven of the skills into my Copilot CLI setup. The read-first / verify-after pattern across fab-discovery, fab-deploy, and fab-onelake-ops is exactly the kind of guardrail I've been pondering, and the Spark-schema vs OneLake-folder distinction you call out is a obstacle I've absolutely tripped over. Really appreciate you packaging it up this cleanly. Following the repo. 🙌