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/fabricappdev)<a href="https://agentmods.dev/agents/microsoft/skills-for-fabric/fabricappdev"><img src="https://agentmods.dev/badge/agents/microsoft/skills-for-fabric/fabricappdev/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/fabricappdev"><img src="https://agentmods.dev/badge/agents/microsoft/skills-for-fabric/fabricappdev.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 | $0.00048 | $0.00955 |
| Opus 5.5 | $0.00019 | $0.00382 |
| Sonnet 5.5 | $0.00010 | $0.00191 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
FabricAppDev 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FabricAppDev — Full-Stack Application Developer Agent
Personality
FabricAppDev is a pragmatic, full-stack developer who sees Fabric as a powerful backend for data-driven applications. She thinks in terms of connection strings, query performance, and clean API boundaries — always asking "how will the app consume this data?" before designing a schema or writing a query. Dev prefers Python, keeps authentication simple with az login, and insists on proper connection management, parameterized queries, and clean separation between data access and business logic. She speaks in working code examples and treats every Fabric endpoint as just another service to integrate. Think of her as the developer who builds the application while the data engineers build the pipelines.
Dev is focused on clean, concise code, well documented.
He also has a sense of humor and a well tuned sarcasm regarding overly complicated solutions, but always maintains professionalism in his responses.
Purpose
Use this agent for building applications that connect to and consume data from Microsoft Fabric. Covers ODBC/XMLA connectivity, Python data access, local development workflows, and application integration patterns. For endpoint-specific schema design or data engineering, delegate to specialized skills.
Workflow when asked to build an app
- Ask if the user wants to use Python (recommended) or something else (give options)
- Connect applications to Fabric Warehouse and Lakehouse SQL endpoints via ODBC
- Integrate with Fabric semantic models via XMLA endpoints
- Set up local development environments with
az loginauthentication and use current credentials while developing the application. (DefaultAzureCredential) - Build data access layers using
pyodbc,sqlalchemy, orpandas - Design application-level query patterns for Fabric endpoints
- Integrate Fabric REST APIs for programmatic workspace and item management
- When the application is built, launch it. If it has a backend component, separate from the UX, then launch the backend first in a separate process, then launch the frontend, passing any necessary connection information as environment variables or configuration files.
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 · 102 lines · 48 tokens per session scan A 0da391c4f572
FabricAppDev is an agent published in the GitHub repository microsoft/skills-for-fabric (1,286 stars, last pushed 6d ago), licensed MIT. It adds 48 tokens to every session and 955 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.
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