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.
npx skills add wardawgmalvicious/agent-config --skill fabric-semantic-model-ai-instructionsgit clone --depth 1 https://github.com/wardawgmalvicious/agent-configWrote 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/wardawgmalvicious/agent-config/fabric-semantic-model-ai-instructions)<a href="https://agentmods.dev/skills/wardawgmalvicious/agent-config/fabric-semantic-model-ai-instructions"><img src="https://agentmods.dev/badge/skills/wardawgmalvicious/agent-config/fabric-semantic-model-ai-instructions/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/wardawgmalvicious/agent-config/fabric-semantic-model-ai-instructions"><img src="https://agentmods.dev/badge/skills/wardawgmalvicious/agent-config/fabric-semantic-model-ai-instructions.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 | $0.00154 | $0.03046 |
| Opus 5 | $0.00077 | $0.01523 |
| Sonnet 5 | $0.00031 | $0.00609 |
| Haiku 4.5 | $0.00015 | $0.00305 |
Grade A, and why
fabric-semantic-model-ai-instructions 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 yesterday.
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 — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Configuring Power BI Semantic Model AI Instructions
A practical, reusable guide for configuring AI instructions on a semantic model. Use this template across projects — the example domain (retail / sales) is illustrative; replace it with your own without changing the structure.
What semantic model AI instructions are
A single text blob (up to 10,000 characters) attached directly to the semantic model. It provides context, business logic, and guidance that Copilot uses when interpreting user questions against the model.
It applies wherever the model is consumed by Copilot — Power BI reports, Q&A visuals, the Copilot pane, and any downstream surface that uses this model. Not just a single surface.
When you use this vs. Data Agent instructions
- Semantic Model AI Instructions — guidance attached to one semantic model. No multi-source routing, no conversational flow. Applies to every Copilot interaction with this model.
- Data Agent — a separate conversational interface with multi-source routing, few-shot example queries, and conversational response formatting.
See the fabric-data-agent skill for data agent specifics.
Setup process
Authoring is now available in both Power BI Desktop and the Power BI service. Consumption happens everywhere Copilot exists.
- Open the semantic model in Power BI Desktop, or select the model in the Power BI service.
- On the Home ribbon, click Prep data for AI.
- If the tabs are disabled, enable Power BI Q&A on the model first.
- Go to the Add AI instructions tab.
- Paste or author the instructions.
- Click Apply.
- Test using the Copilot pane with the Answers questions about the data skill selected.
- Publish or save the model. Instructions take effect everywhere Copilot uses this model.
Notes:
- Each time you edit instructions during testing, close and reopen the Copilot pane to pick up the changes.
- End users cannot see or disable these instructions.
What ships with it
1 file 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.
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.
- yesterday Changed 58ff40046887
- 6d ago Changed b111ed6b6071
- 7d ago Changed · +3 lines ddd2b54d828d
- 11d ago First seen · 292 lines · 154 tokens per session scan A a288b91e15ce
fabric-semantic-model-ai-instructions is a skill published in the GitHub repository wardawgmalvicious/agent-config (0 stars, last pushed today), licensed MIT. It adds 154 tokens to every session and 3,046 once invoked, about $0.0008 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 2026-08-31.
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