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---
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 Activates when you request "excel pivot wizard" functionality.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash
version: 1.0.0
---
# 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:
1. Create pivot table on new sheet
2. Set row fields
3. Set column fields (if cross-tab)
4. Set value fields with aggregation function
5. Add filters/slicers if requested
6. 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 practices
- `examples/`: 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

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# Skill Assets
This directory contains static assets used by this skill.
## Purpose
Assets can include:
- Configuration files (JSON, YAML)
- Data files
- Templates
- Schemas
- Test fixtures
## Guidelines
- Keep assets small and focused
- Document asset purpose and format
- Use standard file formats
- Include schema validation where applicable
## Common Asset Types
- **config.json** - Configuration templates
- **schema.json** - JSON schemas
- **template.yaml** - YAML templates
- **test-data.json** - Test fixtures

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# Skill References
This directory contains reference materials that enhance this skill's capabilities.
## Purpose
References can include:
- Code examples
- Style guides
- Best practices documentation
- Template files
- Configuration examples
## Guidelines
- Keep references concise and actionable
- Use markdown for documentation
- Include clear examples
- Link to external resources when appropriate
## Types of References
- **examples.md** - Usage examples
- **style-guide.md** - Coding standards
- **templates/** - Reusable templates
- **patterns.md** - Design patterns

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# Skill Scripts
This directory contains optional helper scripts that support this skill's functionality.
## Purpose
Scripts here can be:
- Referenced by the skill for automation
- Used as examples for users
- Executed during skill activation
## Guidelines
- All scripts should be well-documented
- Include usage examples in comments
- Make scripts executable (`chmod +x`)
- Use `#!/bin/bash` or `#!/usr/bin/env python3` shebangs
## Adding Scripts
1. Create script file (e.g., `analyze.sh`, `process.py`)
2. Add documentation header
3. Make executable: `chmod +x script-name.sh`
4. Test thoroughly before committing