excel-pivot-wizard
About
The excel-pivot-wizard skill enables developers to generate pivot tables and charts from raw data using natural language commands. It activates when users request analysis like summarizing sales by region or creating cross-tab reports. Key capabilities include grouping data by fields, applying aggregate functions, and creating visualizations through natural language prompts.
Quick Install
Claude Code
Recommended/plugin add https://github.com/jeremylongshore/claude-code-plugins-plusgit clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/excel-pivot-wizardCopy and paste this command in Claude Code to install this skill
Documentation
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
GitHub Repository
Related Skills
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
Algorithmic Art Generation
MetaThis skill helps developers create algorithmic art using p5.js, focusing on generative art, computational aesthetics, and interactive visualizations. It automatically activates for topics like "generative art" or "p5.js visualization" and guides you through creating unique algorithms with features like seeded randomness, flow fields, and particle systems. Use it when you need to build reproducible, code-driven artistic patterns.
webapp-testing
TestingThis Claude Skill provides a Playwright-based toolkit for testing local web applications through Python scripts. It enables frontend verification, UI debugging, screenshot capture, and log viewing while managing server lifecycles. Use it for browser automation tasks but run scripts directly rather than reading their source code to avoid context pollution.
requesting-code-review
DesignThis skill dispatches a code-reviewer subagent to analyze code changes against requirements before proceeding. It should be used after completing tasks, implementing major features, or before merging to main. The review helps catch issues early by comparing the current implementation with the original plan.
