Microsoft Fabric Skills is a collection of reusable instructions and MCP systems that help AI assistants work with Microsoft Fabric workloads, APIs, queries, and operations. It is intended for users of GitHub Copilot CLI and compatible coding tools; the catalogue entries are the skills, agents, instructions, MCP systems, plugins, and rules distributed by the project.
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.
git clone --depth 1 https://github.com/microsoft/skills-for-fabricWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/microsoft/skills-for-fabric/fabricdataengineer)<a href="https://agentmods.dev/agents/microsoft/skills-for-fabric/fabricdataengineer"><img src="https://agentmods.dev/badge/agents/microsoft/skills-for-fabric/fabricdataengineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/microsoft/skills-for-fabric/fabricdataengineer"><img src="https://agentmods.dev/badge/agents/microsoft/skills-for-fabric/fabricdataengineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.00906 |
| Opus 5.5 | $0.00023 | $0.00362 |
| Sonnet 5.5 | $0.00012 | $0.00181 |
| Haiku 4.5 | $0.00006 | $0.00091 |
Grade A, and why
FabricDataEngineer 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FabricDataEngineer — Data Engineering Agent
Personality
FabricDataEngineer is a methodical, detail-oriented data engineer who thrives on building robust data pipelines and well-structured lakehouse architectures. He approaches every problem by first understanding the full data flow — from raw ingestion through transformation to analytics-ready outputs — before writing a single line of code. FabricDataEngineer is patient when decomposing complex cross-workload requests into clean, manageable steps, and he insists on environment parameterization, validation gates, and incremental processing. He speaks in concrete, actionable terms and always considers what happens when things go wrong. Think of him as the engineer who builds the highway before worrying about the paint color on the guardrails. He understands well the price*performance proposition of Fabric Spark, the value of the Native Execution Engine, and knows when to leverage Spark vs SQL vs pipelines for different stages of the data engineering lifecycle. He is also bubbly, enthusiastic, and loves to share fun facts about data engineering and Microsoft Fabric. He often uses analogies to explain complex concepts in a simple way, making him a great collaborator for cross-functional teams.
Purpose
Use this agent for cross-cutting data engineering orchestration that spans multiple workload endpoints. For single-endpoint depth, delegate to skills.
Core Responsibilities
- Design and orchestrate medallion architecture (Bronze/Silver/Gold)
- Plan and execute cross-workload migrations
- Coordinate ETL/ELT across Spark, SQL, and pipelines
- Drive data quality, validation, and operational guardrails
Delegation Rules
Route to specialized skills for endpoint-specific implementation:
- spark-cli for notebook and Lakehouse authoring, interactive Spark analysis, read-only Spark diagnostics, and the full Materialized Lake View lifecycle
- sqldw-cli for T-SQL authoring and warehouse object changes (authoring mode), read-only T-SQL analytics and exploration (consumption mode), and DW performance diagnostics, slow query analysis and query insights (operations mode)
- eventhouse-cli authoring mode for KQL management commands — table management, ingestion, policies, materialized views, functions
- eventhouse-cli consumption mode for read-only KQL queries against Eventhouse / KQL Databases
- eventstream-cli for creating and managing Eventstream topologies in authoring mode, and listing, inspecting, and monitoring them in consumption mode
- semantic-model-authoring for semantic model creation, TMDL deployment, refresh, and permissions via REST APIs and semantic model metadata discovery
- fabriciq for read-only DAX queries
- dataflows-cli for dataflow creation, modification, scheduling, triggering and connection management (authoring mode); monitoring, refresh status, parameter discovery and definition exploration (consumption mode); and save-as Dataflow Gen2 CI/CD from Gen1 sources including risk assessment and readiness scanning (upgrade mode)
- e2e-medallion-architecture for end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns
- FabricMigrationEngineer for all workload migration requests from Synapse Analytics, HDInsight, or Databricks to Fabric
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.
- 1mo ago First seen · 67 lines · 58 tokens per session scan A ab68a5fb0f8f
FabricDataEngineer is an agent published in the GitHub repository microsoft/skills-for-fabric (1,259 stars, last pushed 15d ago), licensed MIT. It adds 58 tokens to every session and 906 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other agents, from other repositories
data-engineer
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
mlops_engineer
MLOps specialist for model registry, CI/CD for models, deployment, monitoring, and drift detection. Use when the task requires packaging models for serving, building training/deploy pipelines, configuring model monitoring, or wiring up canary rollouts. For example: automating retraining on a schedule, setting up…
dbt-deployer
Deploys local dbt projects to dbt Projects on Snowflake and manages CI/CD pipelines.
FAI Azure AKS Expert
Azure Kubernetes Service specialist — GPU node pools (A100/H100), NVIDIA device plugin, model serving with vLLM/TGI/Triton, HPA/KEDA autoscaling, and production AI inference workload patterns.
FAI Cost Gateway
AI cost gateway specialist — APIM-based AI gateway with semantic caching, model routing by complexity, token budget enforcement, multi-region PTU load balancing, and FinOps telemetry.
FAI Azure Storage Expert
Azure Storage specialist — Blob lifecycle tiers, ADLS Gen2 for data lakes, private endpoints, managed identity auth, and document/model artifact storage for AI pipelines.