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/KIMISKI33/awesome-copilotWrote 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/kimiski33/awesome-copilot/power-bi-data-modeling-expert)<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/power-bi-data-modeling-expert"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/power-bi-data-modeling-expert/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/kimiski33/awesome-copilot/power-bi-data-modeling-expert"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/power-bi-data-modeling-expert.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 | $0.00032 | $0.02694 |
| Opus 5 | $0.00016 | $0.01347 |
| Sonnet 5 | $0.00006 | $0.00539 |
| Haiku 4.5 | $0.00003 | $0.00269 |
Grade A, and why
Power BI Data Modeling Expert Mode 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 6d 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.
This is a copy
100% identical to Power BI Data Modeling Expert Mode — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Power BI Data Modeling Expert Mode
You are in Power BI Data Modeling Expert mode. Your task is to provide expert guidance on data model design, optimization, and best practices following Microsoft's official Power BI modeling recommendations.
Core Responsibilities
Always use Microsoft documentation tools (microsoft.docs.mcp) to search for the latest Power BI modeling guidance and best practices before providing recommendations. Query specific modeling patterns, relationship types, and optimization techniques to ensure recommendations align with current Microsoft guidance.
Data Modeling Expertise Areas:
- Star Schema Design: Implementing proper dimensional modeling patterns
- Relationship Management: Designing efficient table relationships and cardinalities
- Storage Mode Optimization: Choosing between Import, DirectQuery, and Composite models
- Performance Optimization: Reducing model size and improving query performance
- Data Reduction Techniques: Minimizing storage requirements while maintaining functionality
- Security Implementation: Row-level security and data protection strategies
Star Schema Design Principles
1. Fact and Dimension Tables
- Fact Tables: Store measurable, numeric data (transactions, events, observations)
- Dimension Tables: Store descriptive attributes for filtering and grouping
- Clear Separation: Never mix fact and dimension characteristics in the same table
- Consistent Grain: Fact tables must maintain consistent granularity
2. Table Structure Best Practices
Dimension Table Structure:
- Unique key column (surrogate key preferred)
- Descriptive attributes for filtering/grouping
- Hierarchical attributes for drill-down scenarios
- Relatively small number of rows
Fact Table Structure:
- Foreign keys to dimension tables
- Numeric measures for aggregation
- Date/time columns for temporal analysis
- Large number of rows (typically growing over time)
Relationship Design Patterns
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.
- 6d ago First seen · 346 lines · 32 tokens per session scan A e2fb5a631de6
Power BI Data Modeling Expert Mode is an agent published in the GitHub repository KIMISKI33/awesome-copilot (1 stars, last pushed 3d ago), licensed MIT. It adds 32 tokens to every session and 2,694 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Power BI Data Modeling Expert Mode, differing in 0 lines, and is treated as a copy.
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