cursorrules

Project rules for developing with Microsoft Fabric, Microsoft's cloud platform for data engineering, data warehouses, and analytics. They cover its REST API, authentication, project structure, and data-access patterns.

In plain words
What is it for?
Use them when creating or managing Fabric lakehouses, warehouses, semantic models, real-time data, and related engineering workflows through Azure authentication and Fabric APIs.
Why use it?
They give an agent consistent instructions for working with Fabric resources and choosing the right interface for each kind of data.

Cursor rule for Cursor

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/microsoft/skills-for-fabric/cursorrules
Clone the repo
git clone --depth 1 https://github.com/microsoft/skills-for-fabric

Made for: Cursor.

Per session 2,213 This file is loaded in full into every session.
When invoked 2,213 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.02213 $0.02213
Opus 5 $0.01107 $0.01107
Sonnet 5 $0.00443 $0.00443
Haiku 4.5 $0.00221 $0.00221

Measured 2d ago against content hash 034921dd87f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

cursorrules scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cursorrules · 180 lines

How it starts

The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Microsoft Fabric Development Rules

Updates: These instructions do not run a session-start update check. Refresh them through your host's plugin or marketplace update flow, or run git pull if you use a manual clone.

Context

This project works with Microsoft Fabric for data engineering, warehousing, and analytics. Primary API reference: https://learn.microsoft.com/en-us/rest/api/fabric/articles/

Architecture Mode

  • Follow repository layering: Agents -> Skills -> Common.
  • For medallion and other cross-endpoint workflows, start with agents/FabricDataEngineer.agent.md.
  • Delegate endpoint-specific tasks to corresponding skills in skills/.

Authentication

All operations require Azure AD tokens via az login:

az login
az account get-access-token --resource https://api.fabric.microsoft.com

Developer Pattern: REST + Protocol

Developers use REST APIs to manage artifacts, then protocol-specific connections to access data:

  • REST → ODBC for Warehouse
  • REST → Spark for Lakehouse
  • REST → XMLA/DAX for Semantic Models
  • REST → KQL for Real-Time Intelligence

Code Generation Rules

Lakehouse & Spark

  • Always use Delta Lake format for tables
  • Use mssparkutils for Fabric-specific operations (fs, credentials, notebook)
  • Include partition columns for large tables
  • Run OPTIMIZE and VACUUM for table maintenance
  • Use skills/spark-cli/SKILL.md for notebook authoring and runs, Livy analysis, Spark diagnostics, and the full Materialized Lake View lifecycle.
  • MLV authoring resources: skills/spark-cli/references/authoring/resources/materialized-lake-view-patterns.md and skills/spark-cli/references/authoring/resources/mlv-incremental-refresh-patterns.md.

Warehouse (T-SQL)

  • Check T-SQL surface area - not all SQL Server features are supported
  • Use CTAS for large transformations
  • Prefer stored procedures for reusable logic
  • Always include TOP/LIMIT when exploring data
  • Skill: skills/sqldw-cli/SKILL.md - one skill with authoring, consumption and operations modes

Read the full file on GitHub · 180 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 180 lines · 2,213 tokens per session scan A 034921dd87f1

Subscribe to this mod's changes

cursorrules is a cursor rule published in the GitHub repository microsoft/skills-for-fabric (1,087 stars, last pushed 5d ago), licensed MIT. It adds 2,213 tokens to every session, about $0.0111 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.