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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/microsoft/skills-for-fabricnpx agentmods add skills/microsoft/skills-for-fabric/semantic-model-authoringWrote 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/skills/microsoft/skills-for-fabric/semantic-model-authoring)<a href="https://agentmods.dev/skills/microsoft/skills-for-fabric/semantic-model-authoring"><img src="https://agentmods.dev/badge/skills/microsoft/skills-for-fabric/semantic-model-authoring/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/skills/microsoft/skills-for-fabric/semantic-model-authoring"><img src="https://agentmods.dev/badge/skills/microsoft/skills-for-fabric/semantic-model-authoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 40 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Data Exfiltration · line 386 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Prompt Injection · line 400 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Prompt Injection · line 406 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00092 | $0.08873 |
| Opus 5.5 | $0.00037 | $0.03549 |
| Sonnet 5.5 | $0.00018 | $0.01775 |
| Haiku 4.5 | $0.00009 | $0.00887 |
Grade A, and why
semantic-model-authoring 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 — 431 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Telemetry — MANDATORY. Every
api.fabric.microsoft.comcall must carryx-ms-fabric-skill: semantic-model-authoring(az rest:--headers "x-ms-fabric-skill=semantic-model-authoring"), including every LRO poll,fabric_lroand retry. Snippets omit it — add it anyway.
CRITICAL NOTES
- To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering
- To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering
- Always consider the Tool selection priority when choosing which tool to use for each operation. Do not default to TMDL edits or
az restif MCP is available and connected to the target model.
Power BI Semantic Model Authoring — CLI Skill
Workflow Selector
Use this decision tree to route to the correct workflow based on user intent:
| User wants to... | Workflow |
|---|---|
| Create a semantic model from scratch | Create new semantic model |
| Inspect or discover model metadata (tables, columns, measures, relationships) | Discover Semantic Model Metadata |
| Add/edit semantic model objects (e.g. measures, tables, columns, relationships) | Modify an Existing Model |
| Write or refactor DAX code | Modify an Existing Model |
| Create/edit a field parameter | Author a Field Parameter |
| Improve DAX query or measure performance | Optimize DAX Performance |
| Analyze semantic model against best practices | Analyze Best Practices |
| Prepare a semantic model for AI consumption (Copilot / Data Agents) | Semantic Model AI Readiness |
| Deploy a model to a Fabric workspace | Deploy to Fabric |
| Refresh a semantic model | Refresh Semantic Model |
| Configure data sources, parameters, or permissions | Manage Semantic Model in Fabric |
| Bind a semantic model to a Fabric connection (or unbind) | Bind Semantic Model to a Connection |
| Export / Get semantic model definition as PBIP | Export to PBIP |
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/connection-binding.md 11 KB
- references/dax-guidelines.md 11 KB
- references/dax-perf-decision-guide.md 32 KB
- references/dax-perf-patterns.md 30 KB
- references/direct-lake-guidelines.md 3.2 KB
- references/field-parameters.md 9.5 KB
- references/metadata-discovery.md 7.1 KB
- references/modeling-guidelines.md 16 KB
- references/naming-conventions.md 4.3 KB
- references/pbip.md 3.9 KB
- references/semantic-model-ai-readiness.md 13 KB
- references/semantic-model-rest-api.md 20 KB
- references/tmdl-guidelines.md 18 KB
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 Changed · +19 lines · -2 tokens per session 69196e96a5a2
- 2mo ago Changed · -8 lines 90d8242dfc06
- 2mo ago First seen · 420 lines · 94 tokens per session scan A b3aa133cedf0
semantic-model-authoring is a skill published in the GitHub repository microsoft/skills-for-fabric (1,321 stars, last pushed yesterday), licensed MIT. It adds 92 tokens to every session and 8,873 once invoked, about $0.0004 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.
semantic-model-authoring compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| semantic-model-authoringmicrosoft/skills-for-fabric | Skill | 1.3k | yesterday | MIT | A | 92 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |