Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
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/github/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/github/awesome-copilot/power-bi-data-modeling-expert)<a href="https://agentmods.dev/agents/github/awesome-copilot/power-bi-data-modeling-expert"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/power-bi-data-modeling-expert.svg" alt="Measured on agentmods" 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 3d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- Power BI Data Modeling Expert Mode — 100% identical, 0 lines differ
- Power BI Data Modeling Expert Mode — 100% identical, 0 lines differ
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.
- 3d 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 github/awesome-copilot (38,675 stars, last pushed today), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
database-architect
Expert database architect for schema design, query optimization, migrations, and modern serverless databases. Use for database operations, schema changes, indexing, and data modeling. Triggers on database, sql, schema, migration, query, postgres, index, table.
sql-pro
Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems. Use PROACTIVELY for database optimization or complex analysis.
Environment Inspector
Background Snowflake environment scanner. Triggered from SessionStart or $cocoplus on when inspector mode is enabled, then writes a timestamped snapshot to .cocoplus/snapshots/.
ndv-explain
Technical documentation writer. Use when creating or updating docs, API references, session notes, or any written material explaining code to humans. Never assumes shared context — models the reader's knowledge gap and bridges it deliberately.
ndv-review
Code review specialist. Use when reviewing PRs, changed files, or any code that needs quality assessment. Sensory processing sensitivity — nothing is filtered as background noise, every inconsistency is fully registered and reported at the correct severity.
ndv-signal
Metrics skeptic. Use when reviewing engineering KPIs, OKRs, sprint velocity, test coverage targets, DORA metrics, or any measurement system. Audits whether metrics measure what they claim to measure. Goodhart's Law as a cognitive style — the moment a measure becomes a target, it stops being a measure, and Signal…