| name | excel-pivot-wizard |
| description | Generate pivot tables and charts from raw data using natural language - analyze sales by region, summarize data by category, and create visualizations effortlessly |
| version | 1.0.0 |
| dependencies | node>=18.0.0 |
Excel Pivot Wizard
Creates pivot tables and visualizations from raw data using natural language commands.
When to Invoke This Skill
Automatically load this Skill when the user asks to:
- "Create a pivot table"
- "Analyze [data] by [dimension]"
- "Summarize sales by region"
- "Show revenue breakdown"
- "Group data by category"
- "Cross-tab analysis"
- "Compare [X] across [Y]"
Capabilities
Pivot Table Generation
- Rows: Group data by one or more fields
- Columns: Cross-tabulate across another dimension
- Values: Aggregate functions (sum, average, count, min, max)
- Filters: Slice data by specific criteria
- Calculated Fields: Create custom formulas
Visualization
- Column/bar charts for comparisons
- Line charts for trends over time
- Pie charts for composition
- Combo charts for multiple metrics
- Conditional formatting for heatmaps
Common Analysis Patterns
Pattern 1: Single Dimension Summary
Request: "Show total sales by region"
Output:
| Region | Total Sales |
|-----------|-------------|
| Northeast | $1,250,000 |
| Southeast | $980,000 |
| Midwest | $1,100,000 |
| West | $1,450,000 |
| Total | $4,780,000 |
Pattern 2: Cross-Tabulation
Request: "Sales by region and product category"
Output:
| Region | Electronics | Clothing | Home Goods | Total |
|-----------|-------------|----------|------------|-----------|
| Northeast | $400K | $500K | $350K | $1,250K |
| Southeast | $300K | $380K | $300K | $980K |
| Midwest | $450K | $350K | $300K | $1,100K |
| West | $550K | $500K | $400K | $1,450K |
| Total | $1,700K | $1,730K | $1,350K | $4,780K |
Pattern 3: Time-Based Trending
Request: "Monthly revenue trend for 2024"
Output:
Line chart showing:
- X-axis: Jan, Feb, Mar, ..., Dec
- Y-axis: Revenue
- Line: Monthly revenue with data labels
Pattern 4: Top N Analysis
Request: "Top 10 products by revenue"
Output:
| Rank | Product | Revenue | % of Total |
|------|---------------|-----------|------------|
| 1 | Product A | $450,000 | 9.4% |
| 2 | Product B | $380,000 | 7.9% |
| 3 | Product C | $350,000 | 7.3% |
| ... | ... | ... | ... |
| 10 | Product J | $180,000 | 3.8% |
| | Top 10 Total | $2,850,000| 59.6% |
| | All Others | $1,930,000| 40.4% |
| | Grand Total | $4,780,000| 100.0% |
Step-by-Step Workflow
1. Understand the Data
Ask clarifying questions if needed:
- What does each column represent?
- What grain is the data? (transaction-level, daily summary, etc.)
- What fields should be aggregated vs grouped?
2. Interpret the Request
Parse natural language into pivot table structure:
"Show sales by region and month" →
Rows: Region
Columns: Month
Values: Sum of Sales
"Average order value by customer segment" →
Rows: Customer Segment
Values: Average of Order Value
"Count of orders by product category and sales rep" →
Rows: Product Category
Columns: Sales Rep
Values: Count of Orders
3. Build the Pivot Table
Use Excel MCP server to:
- Create pivot table on new sheet
- Set row fields
- Set column fields (if cross-tab)
- Set value fields with aggregation function
- Add filters/slicers if requested
- Apply formatting (currency, thousands separator, etc.)
4. Add Enhancements
Conditional Formatting:
- Top 10% values: Dark green
- Bottom 10% values: Dark red
- Color scale for heatmap effect
Calculated Fields:
- Profit Margin % = (Revenue - Cost) / Revenue
- Growth % = (This Year - Last Year) / Last Year
- % of Total = Value / Grand Total
Sorting:
- Largest to smallest by default
- Or chronological for dates
5. Create Visualization
If visualization is requested or would be helpful:
For Comparisons: Column chart
For Trends: Line chart
For Composition: Pie/donut chart
For Correlations: Scatter plot
For Multiple Metrics: Combo chart (column + line)
Example Interactions
Example 1: Sales Analysis
User: "Analyze sales by region and product category"
Skill Response:
I'll create a pivot table to analyze sales by region and product category.
Looking at your data, I see:
- 5,234 transaction rows
- Date range: Jan 1 - Oct 27, 2025
- 4 regions, 12 product categories
Creating pivot table...
✅ Pivot Table Created!
📊 Sales by Region and Product Category:
Top Insights:
1. West region leads with $1.45M (30.3% of total)
2. Electronics is #1 category at $1.70M (35.6%)
3. Northeast + Electronics = $400K (highest cell)
4. Southeast underperforming in Home Goods ($300K vs avg $338K)
Visualization: Added column chart comparing regions
Would you like me to:
- Add a slicer to filter by quarter?
- Calculate % of total for each cell?
- Create a separate view for top products?
Example 2: Time Trend
User: "Show monthly revenue trend"
Skill Response:
Creating monthly revenue trend analysis...
I see data spanning 10 months (Jan-Oct 2025).
✅ Trend Analysis Complete!
📈 Monthly Revenue:
Key Findings:
- Strong start: Q1 averaged $520K/month
- Dip in summer: Jun-Aug averaged $420K/month
- Recovery: Sep-Oct back to $510K/month
- YTD Total: $4.78M
- Average: $478K/month
Created line chart with:
- Trend line showing slight upward trajectory
- Data labels for easy reading
- Target line at $500K (if provided)
📊 Growth Rates:
- Jan-Oct: +8.3% overall
- Best month: March ($575K)
- Weakest month: August ($385K)
Recommendation: Investigate summer seasonality (Jun-Aug dip)
Example 3: Customer Segmentation
User: "Show average order value by customer segment"
Skill Response:
Analyzing average order value (AOV) across customer segments...
✅ Customer Segmentation Analysis Complete!
| Segment | Orders | Total Revenue | Avg Order Value | % of Revenue |
|---------------|--------|---------------|-----------------|--------------|
| Enterprise | 245 | $1,850,000 | $7,551 | 38.7% |
| Mid-Market | 892 | $1,920,000 | $2,152 | 40.2% |
| Small Business| 2,456 | $835,000 | $340 | 17.5% |
| Consumer | 1,641 | $175,000 | $107 | 3.7% |
| Total | 5,234 | $4,780,000 | $913 | 100.0% |
🎯 Key Insights:
- Enterprise: Highest AOV ($7.5K) but only 4.7% of orders
- Mid-Market: Sweet spot (40% of revenue, $2.2K AOV)
- Small Business: High volume (47% of orders) but low AOV
- Consumer: Explore if worth serving (low AOV, high volume)
Created bar chart showing AOV by segment with order count overlay.
💡 Strategic Questions:
1. Should we focus more resources on Enterprise (high AOV)?
2. Can we up-sell Small Business to Mid-Market?
3. Is Consumer segment profitable at $107 AOV?
Example 4: Product Performance
User: "Which products are driving the most revenue?"
Skill Response:
Creating product revenue ranking...
✅ Top Products Analysis Complete!
📊 Top 10 Products (out of 147 total):
| Rank | Product | Revenue | Orders | AOV | % of Total |
|------|------------------|-----------|--------|--------|------------|
| 1 | Pro Laptop 15" | $450,000 | 385 | $1,169 | 9.4% |
| 2 | Business Suite | $380,000 | 892 | $426 | 7.9% |
| 3 | Smart Display | $350,000 | 1,245 | $281 | 7.3% |
| 4 | Wireless Buds | $310,000 | 2,156 | $144 | 6.5% |
| 5 | Office Chair Pro | $295,000 | 428 | $689 | 6.2% |
| 6 | Standing Desk | $280,000 | 356 | $787 | 5.9% |
| 7 | Monitor 27" | $265,000 | 487 | $544 | 5.5% |
| 8 | Keyboard Mech | $245,000 | 1,823 | $134 | 5.1% |
| 9 | Webcam 4K | $220,000 | 678 | $324 | 4.6% |
| 10 | Mouse Wireless | $195,000 | 2,145 | $91 | 4.1% |
| | **Top 10 Total** | **$2,990,000** | | | **62.5%** |
| | All Others (137) | $1,790,000 | | | 37.5% |
| | **Grand Total** | **$4,780,000** | | | **100.0%** |
🎯 Insights:
- Top 10 products = 62.5% of revenue (strong concentration)
- Pro Laptop alone = 9.4% of total revenue
- High-volume low-AOV products: Wireless Buds, Keyboard, Mouse
- High-AOV low-volume products: Pro Laptop, Standing Desk
Created:
- Bar chart: Top 10 products by revenue
- Pareto chart: Cumulative % of revenue
💡 Strategic Implications:
1. Focus inventory on Top 20 products (likely 80% of revenue)
2. Bundle high-volume + high-margin items
3. Wireless Buds: Explore upsell to Pro Buds (higher margin?)
Advanced Features
Calculated Fields
Request: "Show profit margin by product category"
Response:
I'll add a calculated field for profit margin.
Assuming you have Cost and Revenue columns, I'll calculate:
Profit Margin % = (Revenue - Cost) / Revenue × 100%
| Category | Revenue | Cost | Profit | Margin % |
|-------------|---------|---------|---------|----------|
| Electronics | $1,700K | $1,020K | $680K | 40.0% |
| Clothing | $1,730K | $1,125K | $605K | 35.0% |
| Home Goods | $1,350K | $945K | $405K | 30.0% |
| Total | $4,780K | $3,090K | $1,690K | 35.4% |
Electronics has the highest margin (40%) despite not being the highest revenue category.
Slicers & Filters
Request: "Add filters for date range and region"
Response:
I've added slicers for:
1. Date Range (select quarters or specific months)
2. Region (select one or multiple regions)
3. Product Category (filter to specific categories)
These slicers are linked to the pivot table - any selection will update the data automatically.
Try clicking Q3 in the date slicer to see summer performance!
Multiple Aggregations
Request: "Show sum and average of sales by region"
Response:
| Region | Total Sales | Avg Sale | Order Count |
|-----------|-------------|----------|-------------|
| Northeast | $1,250,000 | $625 | 2,000 |
| Southeast | $980,000 | $490 | 2,000 |
| Midwest | $1,100,000 | $524 | 2,100 |
| West | $1,450,000 | $690 | 2,100 |
Interesting: Southeast has lowest average sale despite same order count as Northeast.
This suggests smaller basket sizes in Southeast - worth investigating!
Formatting Best Practices
The Skill applies professional formatting:
Numbers
Revenue: $1,250,000 or $1.25M (use M for millions)
Counts: 2,000 (thousands separator)
Percentages: 35.0% (1 decimal)
Conditional Formatting
Top performers: Green highlight
Bottom performers: Red highlight
Heatmap: Color gradient from red (low) to green (high)
Layout
- Bold headers
- Freeze top row and left column
- Subtotals and grand totals
- Alternating row colors for readability
Resources
See resources folder for:
REFERENCE.md: Pivot table best practicesexamples/: Sample pivot tables for common analyses
Limitations
This Skill creates standard pivot tables for:
- Summarization and aggregation
- Cross-tabulation
- Basic calculations (sum, average, count)
For advanced analysis, you may need:
- Power Pivot (for complex data models)
- Pivot charts with custom formatting
- Integration with external data sources
- Real-time data refresh
Version History
- v1.0.0 (2025-10-27): Initial release with core pivot table generation
