Borrowing it
Nothing to install: this file belongs to gurvinder-dhillon/powerbi-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gurvinder-dhillon/powerbi-mcp/main/.claude/skills/powerbi-expert/SKILL.mdgit clone --depth 1 https://github.com/gurvinder-dhillon/powerbi-mcpWrote 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/gurvinder-dhillon/powerbi-mcp/powerbi-expert)<a href="https://agentmods.dev/skills/gurvinder-dhillon/powerbi-mcp/powerbi-expert"><img src="https://agentmods.dev/badge/skills/gurvinder-dhillon/powerbi-mcp/powerbi-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/skills/gurvinder-dhillon/powerbi-mcp/powerbi-expert"><img src="https://agentmods.dev/badge/skills/gurvinder-dhillon/powerbi-mcp/powerbi-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 | $0.00029 | $0.05425 |
| Opus 5 | $0.00015 | $0.02712 |
| Sonnet 5 | $0.00006 | $0.01085 |
| Haiku 4.5 | $0.00003 | $0.00543 |
Grade A, and why
powerbi-expert 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 12d 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 powerbi-expert — 759 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 — 864 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Power BI Expert
You are an expert in Power BI with deep knowledge of DAX (Data Analysis Expressions), M language (Power Query), data modeling, relationships, measures, calculated columns, row-level security, and report design. You create performant, maintainable analytical solutions in Power BI.
Core Expertise
Data Modeling
Star Schema Design:
Fact Tables:
- FactSales (OrderID, ProductKey, CustomerKey, DateKey, Quantity, Amount)
- FactInventory (ProductKey, DateKey, StockLevel, ReorderPoint)
Dimension Tables:
- DimProduct (ProductKey, ProductName, Category, SubCategory, Price)
- DimCustomer (CustomerKey, CustomerName, Segment, Region, Country)
- DimDate (DateKey, Date, Year, Quarter, Month, MonthName, Week, Day)
- DimStore (StoreKey, StoreName, Region, Manager)
Relationships:
FactSales[ProductKey] -> DimProduct[ProductKey] (Many-to-One)
FactSales[CustomerKey] -> DimCustomer[CustomerKey] (Many-to-One)
FactSales[DateKey] -> DimDate[DateKey] (Many-to-One)
FactSales[StoreKey] -> DimStore[StoreKey] (Many-to-One)
Cardinality: Many-to-One (*:1)
Cross Filter Direction: Single (default) or Both (use sparingly)
Active Relationship: Yes
Relationship Types:
// One-to-Many (most common)
DimProduct[ProductKey] (1) -> FactSales[ProductKey] (*)
// Many-to-Many (use carefully)
FactSales (*) <-> BridgeTable (*) <-> DimPromotion (*)
// Inactive relationships (use USERELATIONSHIP)
FactSales[OrderDateKey] -> DimDate[DateKey] (Active)
FactSales[ShipDateKey] -> DimDate[DateKey] (Inactive)
// Use inactive relationship in measure
Sales by Ship Date = CALCULATE(
[Total Sales],
USERELATIONSHIP(FactSales[ShipDateKey], DimDate[DateKey])
)
Date Table (Essential):
// Calendar table using DAX
DimDate =
ADDCOLUMNS(
CALENDAR(DATE(2020, 1, 1), DATE(2025, 12, 31)),
"Year", YEAR([Date]),
"Quarter", "Q" & FORMAT([Date], "Q"),
"QuarterNum", QUARTER([Date]),
"Month", FORMAT([Date], "MMMM"),
"MonthNum", MONTH([Date]),
"MonthYear", FORMAT([Date], "MMM YYYY"),
"Week", WEEKNUM([Date]),
"Day", DAY([Date]),
"DayOfWeek", FORMAT([Date], "dddd"),
"DayOfWeekNum", WEEKDAY([Date]),
"IsWeekend", WEEKDAY([Date]) IN {1, 7},
"FiscalYear", IF(MONTH([Date]) <= 6, YEAR([Date]), YEAR([Date]) + 1),
"FiscalQuarter", IF(MONTH([Date]) <= 6, QUARTER([Date]) + 2, QUARTER([Date]) - 2)
)
// Mark as date table
// Table Tools -> Mark as Date Table -> Date column: [Date]
// Alternative: Auto date table (not recommended for production)
// File -> Options -> Data Load -> Auto Date/Time
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.
- 12d ago First seen · 864 lines · 29 tokens per session scan A 98666edef683
powerbi-expert is a skill published in the GitHub repository gurvinder-dhillon/powerbi-mcp (0 stars, last pushed 7mo ago), licensed MIT. It adds 29 tokens to every session and 5,425 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to powerbi-expert, differing in 759 lines, and is treated as a copy.
Other skills, from other repositories
powerbi-expert
Expert-level Power BI, DAX, M language, data modeling, Power Query, report design, and paginated reports. Use when the user mentions DAX, Power Query, BI, Microsoft platforms, analytics, or data modeling, or when the task involves DAX Fundamentals, Advanced DAX, Row-Level Security, or Report Design.
malloy-model
Build Malloy semantic models with base source and joined source files. Use when creating or modifying .malloy files, user asks to "create a malloy model", "add dimensions", "add measures", "create a source", or any Malloy model authoring task.
malloy-model-as-you-go
After answering a data question, write down what the answer assumed so the next reader can trust the number. A field with a.
malloy-define
Propose a source plan and field definitions for a Malloy semantic model. Covers picking which sources to model and at what grain, then proposing the specific renames, dimensions, and measures per source, every proposal backed by querying the data.
malloy-document
Add documentation with #(doc) tags to Malloy models so fields and sources are described in plain language. Use when user asks to "add documentation", "add doc tags", "document the model", or wants fields and sources described for natural-language search and discovery. For declaring parameterizable filters with…
malloy-review
Malloy semantic-model code review. Invoke when the user asks to review, audit, or critique a .malloy file, a folder of Malloy models, or a GitHub PR that touches Malloy. Enforces project modeling standards and emits a navigable review file.