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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpowerbi-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches powerbi-expert from personamanagmentlayer/pcl and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate powerbi-expert. Access via /powerbi-expert in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
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
Basic Measures:
// Simple aggregations
Total Sales = SUM(FactSales[Amount])
Total Quantity = SUM(FactSales[Quantity])
Average Sale = AVERAGE(FactSales[Amount])
Distinct Customers = DISTINCTCOUNT(FactSales[CustomerKey])
// Count rows
Total Orders = COUNTROWS(FactSales)
// Conditional sum
Sales Above 100 = SUMX(
FILTER(FactSales, FactSales[Amount] > 100),
FactSales[Amount]
)
// Alternative with CALCULATE
Sales Above 100 = CALCULATE(
[Total Sales],
FactSales[Amount] > 100
)
CALCULATE - The Most Important Function:
// Basic filter
Sales USA = CALCULATE(
[Total Sales],
DimCustomer[Country] = "USA"
)
// Multiple filters (AND logic)
Sales USA Electronics = CALCULATE(
[Total Sales],
DimCustomer[Country] = "USA",
DimProduct[Category] = "Electronics"
)
// OR logic using ||
Sales USA or Canada = CALCULATE(
[Total Sales],
DimCustomer[Country] = "USA" || DimCustomer[Country] = "Canada"
)
// Using IN for multiple values
Sales North America = CALCULATE(
[Total Sales],
DimCustomer[Country] IN {"USA", "Canada", "Mexico"}
)
// Remove filters with ALL
Total Sales All Countries = CALCULATE(
[Total Sales],
ALL(DimCustomer[Country])
)
// Keep only specific filter
Sales Ignoring Other Filters = CALCULATE(
[Total Sales],
ALL(DimCustomer),
DimCustomer[Country] = "USA"
)
// Remove all filters
Grand Total = CALCULATE(
[Total Sales],
ALL(FactSales)
)
Time Intelligence:
// Year to date
YTD Sales = TOTALYTD(
[Total Sales],
DimDate[Date]
)
// Quarter to date
QTD Sales = TOTALQTD(
[Total Sales],
DimDate[Date]
)
// Month to date
MTD Sales = TOTALMTD(
[Total SaleMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
erichowens/some_claude_skills
sickn33/antigravity-awesome-skills
erichowens/some_claude_skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
Keeps context tight: powerbi-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
powerbi-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added powerbi-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in powerbi-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
powerbi-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
powerbi-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for powerbi-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for powerbi-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: powerbi-expert is focused, and the summary matches what you get after install.
powerbi-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
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