[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CDAWNMMZ6GDGZ2TV1S5CM8":3,"skill-same:01M6CDAWNMMZ6GDGZ2TV1S5CM8":92},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6CDAWNMMZ6GDGZ2TV1S5CM8","excel-pivot-wizard",null,"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.","BbgnsurfTech\u002Fclaude-skills-collection","BbgnsurfTech","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F59598605?v=4","01M6C5ZT4G3G4QPQB64QDVW438","Bbgnsurf Technologies","01M6C5ZT8JN4VWSZZH3DD8J4EG",29,"MIT","https:\u002F\u002Fgithub.com\u002FBbgnsurfTech\u002Fclaude-skills-collection\u002Fblob\u002Fmain\u002Fplugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Fbusiness-tools\u002Fexcel-analyst-pro\u002Fskills\u002Fexcel-pivot-wizard\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000004","电子表格","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"xlsx","wizard","excel pivot wizard","summarize","create","generate","data","Charts","Bash","visualizations","analyze","grep","read","write","edit","sales","2026-11-10 10:12:30",false,"---\ndescription: Generate pivot tables and charts from raw data using natural language\n  - analyze sales by region, summarize data by category, and create visualizations\n  effortlessly Activates when you request \"excel pivot wizard\" functionality.\nallowed-tools:\n- Read\n- Write\n- Edit\n- Grep\n- Glob\n- Bash\nname: excel-pivot-wizard\nlicense: MIT\n---\n# Excel Pivot Wizard\n\nCreates pivot tables and visualizations from raw data using natural language commands.\n\n## When to Invoke This Skill\n\nAutomatically load this Skill when the user asks to:\n- \"Create a pivot table\"\n- \"Analyze [data] by [dimension]\"\n- \"Summarize sales by region\"\n- \"Show revenue breakdown\"\n- \"Group data by category\"\n- \"Cross-tab analysis\"\n- \"Compare [X] across [Y]\"\n\n## Capabilities\n\n### Pivot Table Generation\n- **Rows**: Group data by one or more fields\n- **Columns**: Cross-tabulate across another dimension\n- **Values**: Aggregate functions (sum, average, count, min, max)\n- **Filters**: Slice data by specific criteria\n- **Calculated Fields**: Create custom formulas\n\n### Visualization\n- Column\u002Fbar charts for comparisons\n- Line charts for trends over time\n- Pie charts for composition\n- Combo charts for multiple metrics\n- Conditional formatting for heatmaps\n\n## Common Analysis Patterns\n\n### Pattern 1: Single Dimension Summary\n**Request:** \"Show total sales by region\"\n\n**Output:**\n```\n| Region    | Total Sales |\n|-----------|-------------|\n| Northeast | $1,250,000  |\n| Southeast | $980,000    |\n| Midwest   | $1,100,000  |\n| West      | $1,450,000  |\n| Total     | $4,780,000  |\n```\n\n### Pattern 2: Cross-Tabulation\n**Request:** \"Sales by region and product category\"\n\n**Output:**\n```\n| Region    | Electronics | Clothing | Home Goods | Total     |\n|-----------|-------------|----------|------------|-----------|\n| Northeast | $400K       | $500K    | $350K      | $1,250K   |\n| Southeast | $300K       | $380K    | $300K      | $980K     |\n| Midwest   | $450K       | $350K    | $300K      | $1,100K   |\n| West      | $550K       | $500K    | $400K      | $1,450K   |\n| Total     | $1,700K     | $1,730K  | $1,350K    | $4,780K   |\n```\n\n### Pattern 3: Time-Based Trending\n**Request:** \"Monthly revenue trend for 2024\"\n\n**Output:**\n```\nLine chart showing:\n- X-axis: Jan, Feb, Mar, ..., Dec\n- Y-axis: Revenue\n- Line: Monthly revenue with data labels\n```\n\n### Pattern 4: Top N Analysis\n**Request:** \"Top 10 products by revenue\"\n\n**Output:**\n```\n| Rank | Product       | Revenue   | % of Total |\n|------|---------------|-----------|------------|\n| 1    | Product A     | $450,000  | 9.4%       |\n| 2    | Product B     | $380,000  | 7.9%       |\n| 3    | Product C     | $350,000  | 7.3%       |\n| ...  | ...           | ...       | ...        |\n| 10   | Product J     | $180,000  | 3.8%       |\n|      | Top 10 Total  | $2,850,000| 59.6%      |\n|      | All Others    | $1,930,000| 40.4%      |\n|      | Grand Total   | $4,780,000| 100.0%     |\n```\n\n## Step-by-Step Workflow\n\n### 1. Understand the Data\n\nAsk clarifying questions if needed:\n- What does each column represent?\n- What grain is the data? (transaction-level, daily summary, etc.)\n- What fields should be aggregated vs grouped?\n\n### 2. Interpret the Request\n\nParse natural language into pivot table structure:\n\n**\"Show sales by region and month\"** →\n```\nRows: Region\nColumns: Month\nValues: Sum of Sales\n```\n\n**\"Average order value by customer segment\"** →\n```\nRows: Customer Segment\nValues: Average of Order Value\n```\n\n**\"Count of orders by product category and sales rep\"** →\n```\nRows: Product Category\nColumns: Sales Rep\nValues: Count of Orders\n```\n\n### 3. Build the Pivot Table\n\nUse Excel MCP server to:\n1. Create pivot table on new sheet\n2. Set row fields\n3. Set column fields (if cross-tab)\n4. Set value fields with aggregation function\n5. Add filters\u002Fslicers if requested\n6. Apply formatting (currency, thousands separator, etc.)\n\n### 4. Add Enhancements\n\n**Conditional Formatting:**\n```\n- Top 10% values: Dark green\n- Bottom 10% values: Dark red\n- Color scale for heatmap effect\n```\n\n**Calculated Fields:**\n```\n- Profit Margin % = (Revenue - Cost) \u002F Revenue\n- Growth % = (This Year - Last Year) \u002F Last Year\n- % of Total = Value \u002F Grand Total\n```\n\n**Sorting:**\n```\n- Largest to smallest by default\n- Or chronological for dates\n```\n\n### 5. Create Visualization\n\nIf visualization is requested or would be helpful:\n\n**For Comparisons:** Column chart\n**For Trends:** Line chart\n**For Composition:** Pie\u002Fdonut chart\n**For Correlations:** Scatter plot\n**For Multiple Metrics:** Combo chart (column + line)\n\n## Example Interactions\n\n### Example 1: Sales Analysis\n\n**User:** \"Analyze sales by region and product category\"\n\n**Skill Response:**\n```\nI'll create a pivot table to analyze sales by region and product category.\n\nLooking at your data, I see:\n- 5,234 transaction rows\n- Date range: Jan 1 - Oct 27, 2025\n- 4 regions, 12 product categories\n\nCreating pivot table...\n\n✅ Pivot Table Created!\n\n? Sales by Region and Product Category:\n\nTop Insights:\n1. West region leads with $1.45M (30.3% of total)\n2. Electronics is #1 category at $1.70M (35.6%)\n3. Northeast + Electronics = $400K (highest cell)\n4. Southeast underperforming in Home Goods ($300K vs avg $338K)\n\nVisualization: Added column chart comparing regions\n\nWould you like me to:\n- Add a slicer to filter by quarter?\n- Calculate % of total for each cell?\n- Create a separate view for top products?\n```\n\n### Example 2: Time Trend\n\n**User:** \"Show monthly revenue trend\"\n\n**Skill Response:**\n```\nCreating monthly revenue trend analysis...\n\nI see data spanning 10 months (Jan-Oct 2025).\n\n✅ Trend Analysis Complete!\n\n? Monthly Revenue:\n\nKey Findings:\n- Strong start: Q1 averaged $520K\u002Fmonth\n- Dip in summer: Jun-Aug averaged $420K\u002Fmonth\n- Recovery: Sep-Oct back to $510K\u002Fmonth\n- YTD Total: $4.78M\n- Average: $478K\u002Fmonth\n\nCreated line chart with:\n- Trend line showing slight upward trajectory\n- Data labels for easy reading\n- Target line at $500K (if provided)\n\n? Growth Rates:\n- Jan-Oct: +8.3% overall\n- Best month: March ($575K)\n- Weakest month: August ($385K)\n\nRecommendation: Investigate summer seasonality (Jun-Aug dip)\n```\n\n### Example 3: Customer Segmentation\n\n**User:** \"Show average order value by customer segment\"\n\n**Skill Response:**\n```\nAnalyzing average order value (AOV) across customer segments...\n\n✅ Customer Segmentation Analysis Complete!\n\n| Segment       | Orders | Total Revenue | Avg Order Value | % of Revenue |\n|---------------|--------|---------------|-----------------|--------------|\n| Enterprise    | 245    | $1,850,000    | $7,551         | 38.7%        |\n| Mid-Market    | 892    | $1,920,000    | $2,152         | 40.2%        |\n| Small Business| 2,456  | $835,000      | $340           | 17.5%        |\n| Consumer      | 1,641  | $175,000      | $107           | 3.7%         |\n| Total         | 5,234  | $4,780,000    | $913           | 100.0%       |\n\n? Key Insights:\n- Enterprise: Highest AOV ($7.5K) but only 4.7% of orders\n- Mid-Market: Sweet spot (40% of revenue, $2.2K AOV)\n- Small Business: High volume (47% of orders) but low AOV\n- Consumer: Explore if worth serving (low AOV, high volume)\n\nCreated bar chart showing AOV by segment with order count overlay.\n\n? Strategic Questions:\n1. Should we focus more resources on Enterprise (high AOV)?\n2. Can we up-sell Small Business to Mid-Market?\n3. Is Consumer segment profitable at $107 AOV?\n```\n\n### Example 4: Product Performance\n\n**User:** \"Which products are driving the most revenue?\"\n\n**Skill Response:**\n```\nCreating product revenue ranking...\n\n✅ Top Products Analysis Complete!\n\n? Top 10 Products (out of 147 total):\n\n| Rank | Product          | Revenue   | Orders | AOV    | % of Total |\n|------|------------------|-----------|--------|--------|------------|\n| 1    | Pro Laptop 15\"   | $450,000  | 385    | $1,169 | 9.4%       |\n| 2    | Business Suite   | $380,000  | 892    | $426   | 7.9%       |\n| 3    | Smart Display    | $350,000  | 1,245  | $281   | 7.3%       |\n| 4    | Wireless Buds    | $310,000  | 2,156  | $144   | 6.5%       |\n| 5    | Office Chair Pro | $295,000  | 428    | $689   | 6.2%       |\n| 6    | Standing Desk    | $280,000  | 356    | $787   | 5.9%       |\n| 7    | Monitor 27\"      | $265,000  | 487    | $544   | 5.5%       |\n| 8    | Keyboard Mech    | $245,000  | 1,823  | $134   | 5.1%       |\n| 9    | Webcam 4K        | $220,000  | 678    | $324   | 4.6%       |\n| 10   | Mouse Wireless   | $195,000  | 2,145  | $91    | 4.1%       |\n|      | **Top 10 Total** | **$2,990,000** | | | **62.5%** |\n|      | All Others (137) | $1,790,000 |    |        | 37.5%       |\n|      | **Grand Total**  | **$4,780,000** | | | **100.0%** |\n\n? Insights:\n- Top 10 products = 62.5% of revenue (strong concentration)\n- Pro Laptop alone = 9.4% of total revenue\n- High-volume low-AOV products: Wireless Buds, Keyboard, Mouse\n- High-AOV low-volume products: Pro Laptop, Standing Desk\n\nCreated:\n- Bar chart: Top 10 products by revenue\n- Pareto chart: Cumulative % of revenue\n\n? Strategic Implications:\n1. Focus inventory on Top 20 products (likely 80% of revenue)\n2. Bundle high-volume + high-margin items\n3. Wireless Buds: Explore upsell to Pro Buds (higher margin?)\n```\n\n## Advanced Features\n\n### Calculated Fields\n\n**Request:** \"Show profit margin by product category\"\n\n**Response:**\n```\nI'll add a calculated field for profit margin.\n\nAssuming you have Cost and Revenue columns, I'll calculate:\nProfit Margin % = (Revenue - Cost) \u002F Revenue × 100%\n\n| Category    | Revenue | Cost    | Profit  | Margin % |\n|-------------|---------|---------|---------|----------|\n| Electronics | $1,700K | $1,020K | $680K   | 40.0%    |\n| Clothing    | $1,730K | $1,125K | $605K   | 35.0%    |\n| Home Goods  | $1,350K | $945K   | $405K   | 30.0%    |\n| Total       | $4,780K | $3,090K | $1,690K | 35.4%    |\n\nElectronics has the highest margin (40%) despite not being the highest revenue category.\n```\n\n### Slicers & Filters\n\n**Request:** \"Add filters for date range and region\"\n\n**Response:**\n```\nI've added slicers for:\n1. Date Range (select quarters or specific months)\n2. Region (select one or multiple regions)\n3. Product Category (filter to specific categories)\n\nThese slicers are linked to the pivot table - any selection will update the data automatically.\n\nTry clicking Q3 in the date slicer to see summer performance!\n```\n\n### Multiple Aggregations\n\n**Request:** \"Show sum and average of sales by region\"\n\n**Response:**\n```\n| Region    | Total Sales | Avg Sale | Order Count |\n|-----------|-------------|----------|-------------|\n| Northeast | $1,250,000  | $625     | 2,000       |\n| Southeast | $980,000    | $490     | 2,000       |\n| Midwest   | $1,100,000  | $524     | 2,100       |\n| West      | $1,450,000  | $690     | 2,100       |\n\nInteresting: Southeast has lowest average sale despite same order count as Northeast.\nThis suggests smaller basket sizes in Southeast - worth investigating!\n```\n\n## Formatting Best Practices\n\nThe Skill applies professional formatting:\n\n### Numbers\n```\nRevenue: $1,250,000 or $1.25M (use M for millions)\nCounts: 2,000 (thousands separator)\nPercentages: 35.0% (1 decimal)\n```\n\n### Conditional Formatting\n```\nTop performers: Green highlight\nBottom performers: Red highlight\nHeatmap: Color gradient from red (low) to green (high)\n```\n\n### Layout\n```\n- Bold headers\n- Freeze top row and left column\n- Subtotals and grand totals\n- Alternating row colors for readability\n```\n\n## Resources\n\nSee resources folder for:\n- `REFERENCE.md`: Pivot table best practices\n- `examples\u002F`: Sample pivot tables for common analyses\n\n## Limitations\n\nThis Skill creates standard pivot tables for:\n- Summarization and aggregation\n- Cross-tabulation\n- Basic calculations (sum, average, count)\n\nFor advanced analysis, you may need:\n- Power Pivot (for complex data models)\n- Pivot charts with custom formatting\n- Integration with external data sources\n- Real-time data refresh\n\n## Version History\n\n- v1.0.0 (2025-10-27): Initial release with core pivot table generation\n","# Excel Pivot Wizard\n\nCreates pivot tables and visualizations from raw data using natural language commands.\n\n## When to Invoke This Skill\n\nAutomatically load this Skill when the user asks to:\n- \"Create a pivot table\"\n- \"Analyze [data] by [dimension]\"\n- \"Summarize sales by region\"\n- \"Show revenue breakdown\"\n- \"Group data by category\"\n- \"Cross-tab analysis\"\n- \"Compare [X] across [Y]\"\n\n## Capabilities\n\n### Pivot Table Generation\n- **Rows**: Group data by one or more fields\n- **Columns**: Cross-tabulate across another dimension\n- **Values**: Aggregate functions (sum, average, count, min, max)\n- **Filters**: Slice data by specific criteria\n- **Calculated Fields**: Create custom formulas\n\n### Visualization\n- Column\u002Fbar charts for comparisons\n- Line charts for trends over time\n- Pie charts for composition\n- Combo charts for multiple metrics\n- Conditional formatting for heatmaps\n\n## Common Analysis Patterns\n\n### Pattern 1: Single Dimension Summary\n**Request:** \"Show total sales by region\"\n\n**Output:**\n```\n| Region    | Total Sales |\n|-----------|-------------|\n| Northeast | $1,250,000  |\n| Southeast | $980,000    |\n| Midwest   | $1,100,000  |\n| West      | $1,450,000  |\n| Total     | $4,780,000  |\n```\n\n### Pattern 2: Cross-Tabulation\n**Request:** \"Sales by region and product category\"\n\n**Output:**\n```\n| Region    | Electronics | Clothing | Home Goods | Total     |\n|-----------|-------------|----------|------------|-----------|\n| Northeast | $400K       | $500K    | $350K      | $1,250K   |\n| Southeast | $300K       | $380K    | $300K      | $980K     |\n| Midwest   | $450K       | $350K    | $300K      | $1,100K   |\n| West      | $550K       | $500K    | $400K      | $1,450K   |\n| Total     | $1,700K     | $1,730K  | $1,350K    | $4,780K   |\n```\n\n### Pattern 3: Time-Based Trending\n**Request:** \"Monthly revenue trend for 2024\"\n\n**Output:**\n```\nLine chart showing:\n- X-axis: Jan, Feb, Mar, ..., Dec\n- Y-axis: Revenue\n- Line: Monthly revenue with data labels\n```\n\n### Pattern 4: Top N Analysis\n**Request:** \"Top 10 products by revenue\"\n\n**Output:**\n```\n| Rank | Product       | Revenue   | % of Total |\n|------|---------------|-----------|------------|\n| 1    | Product A     | $450,000  | 9.4%       |\n| 2    | Product B     | $380,000  | 7.9%       |\n| 3    | Product C     | $350,000  | 7.3%       |\n| ...  | ...           | ...       | ...        |\n| 10   | Product J     | $180,000  | 3.8%       |\n|      | Top 10 Total  | $2,850,000| 59.6%      |\n|      | All Others    | $1,930,000| 40.4%      |\n|      | Grand Total   | $4,780,000| 100.0%     |\n```\n\n## Step-by-Step Workflow\n\n### 1. Understand the Data\n\nAsk clarifying questions if needed:\n- What does each column represent?\n- What grain is the data? (transaction-level, daily summary, etc.)\n- What fields should be aggregated vs grouped?\n\n### 2. Interpret the Request\n\nParse natural language into pivot table structure:\n\n**\"Show sales by region and month\"** →\n```\nRows: Region\nColumns: Month\nValues: Sum of Sales\n```\n\n**\"Average order value by customer segment\"** →\n```\nRows: Customer Segment\nValues: Average of Order Value\n```\n\n**\"Count of orders by product category and sales rep\"** →\n```\nRows: Product Category\nColumns: Sales Rep\nValues: Count of Orders\n```\n\n### 3. Build the Pivot Table\n\nUse Excel MCP server to:\n1. Create pivot table on new sheet\n2. Set row fields\n3. Set column fields (if cross-tab)\n4. Set value fields with aggregation function\n5. Add filters\u002Fslicers if requested\n6. Apply formatting (currency, thousands separator, etc.)\n\n### 4. Add Enhancements\n\n**Conditional Formatting:**\n```\n- Top 10% values: Dark green\n- Bottom 10% values: Dark red\n- Color scale for heatmap effect\n```\n\n**Calculated Fields:**\n```\n- Profit Margin % = (Revenue - Cost) \u002F Revenue\n- Growth % = (This Year - Last Year) \u002F Last Year\n- % of Total = Value \u002F Grand Total\n```\n\n**Sorting:**\n```\n- Largest to smallest by default\n- Or chronological for dates\n```\n\n### 5. Create Visualization\n\nIf visualization is requested or would be helpful:\n\n**For Comparisons:** Column chart\n**For Trends:** Line chart\n**For Composition:** Pie\u002Fdonut chart\n**For Correlations:** Scatter plot\n**For Multiple Metrics:** Combo chart (column + line)\n\n## Example Interactions\n\n### Example 1: Sales Analysis\n\n**User:** \"Analyze sales by region and product category\"\n\n**Skill Response:**\n```\nI'll create a pivot table to analyze sales by region and product category.\n\nLooking at your data, I see:\n- 5,234 transaction rows\n- Date range: Jan 1 - Oct 27, 2025\n- 4 regions, 12 product categories\n\nCreating pivot table...\n\n✅ Pivot Table Created!\n\n? Sales by Region and Product Category:\n\nTop Insights:\n1. West region leads with $1.45M (30.3% of total)\n2. Electronics is #1 category at $1.70M (35.6%)\n3. Northeast + Electronics = $400K (highest cell)\n4. Southeast underperforming in Home Goods ($300K vs avg $338K)\n\nVisualization: Added column chart comparing regions\n\nWould you like me to:\n- Add a slicer to filter by quarter?\n- Calculate % of total for each cell?\n- Create a separate view for top products?\n```\n\n### Example 2: Time Trend\n\n**User:** \"Show monthly revenue trend\"\n\n**Skill Response:**\n```\nCreating monthly revenue trend analysis...\n\nI see data spanning 10 months (Jan-Oct 2025).\n\n✅ Trend Analysis Complete!\n\n? Monthly Revenue:\n\nKey Findings:\n- Strong start: Q1 averaged $520K\u002Fmonth\n- Dip in summer: Jun-Aug averaged $420K\u002Fmonth\n- Recovery: Sep-Oct back to $510K\u002Fmonth\n- YTD Total: $4.78M\n- Average: $478K\u002Fmonth\n\nCreated line chart with:\n- Trend line showing slight upward trajectory\n- Data labels for easy reading\n- Target line at $500K (if provided)\n\n? Growth Rates:\n- Jan-Oct: +8.3% overall\n- Best month: March ($575K)\n- Weakest month: August ($385K)\n\nRecommendation: Investigate summer seasonality (Jun-Aug dip)\n```\n\n### Example 3: Customer Segmentation\n\n**User:** \"Show average order value by customer segment\"\n\n**Skill Response:**\n```\nAnalyzing average order value (AOV) across customer segments...\n\n✅ Customer Segmentation Analysis Complete!\n\n| Segment       | Orders | Total Revenue | Avg Order Value | % of Revenue |\n|---------------|--------|---------------|-----------------|--------------|\n| Enterprise    | 245    | $1,850,000    | $7,551         | 38.7%        |\n| Mid-Market    | 892    | $1,920,000    | $2,152         | 40.2%        |\n| Small Business| 2,456  | $835,000      | $340           | 17.5%        |\n| Consumer      | 1,641  | $175,000      | $107           | 3.7%         |\n| Total         | 5,234  | $4,780,000    | $913           | 100.0%       |\n\n? Key Insights:\n- Enterprise: Highest AOV ($7.5K) but only 4.7% of orders\n- Mid-Market: Sweet spot (40% of revenue, $2.2K AOV)\n- Small Business: High volume (47% of orders) but low AOV\n- Consumer: Explore if worth serving (low AOV, high volume)\n\nCreated bar chart showing AOV by segment with order count overlay.\n\n? Strategic Questions:\n1. Should we focus more resources on Enterprise (high AOV)?\n2. Can we up-sell Small Business to Mid-Market?\n3. Is Consumer segment profitable at $107 AOV?\n```\n\n### Example 4: Product Performance\n\n**User:** \"Which products are driving the most revenue?\"\n\n**Skill Response:**\n```\nCreating product revenue ranking...\n\n✅ Top Products Analysis Complete!\n\n? Top 10 Products (out of 147 total):\n\n| Rank | Product          | Revenue   | Orders | AOV    | % of Total |\n|------|------------------|-----------|--------|--------|------------|\n| 1    | Pro Laptop 15\"   | $450,000  | 385    | $1,169 | 9.4%       |\n| 2    | Business Suite   | $380,000  | 892    | $426   | 7.9%       |\n| 3    | Smart Display    | $350,000  | 1,245  | $281   | 7.3%       |\n| 4    | Wireless Buds    | $310,000  | 2,156  | $144   | 6.5%       |\n| 5    | Office Chair Pro | $295,000  | 428    | $689   | 6.2%       |\n| 6    | Standing Desk    | $280,000  | 356    | $787   | 5.9%       |\n| 7    | Monitor 27\"      | $265,000  | 487    | $544   | 5.5%       |\n| 8    | Keyboard Mech    | $245,000  | 1,823  | $134   | 5.1%       |\n| 9    | Webcam 4K        | $220,000  | 678    | $324   | 4.6%       |\n| 10   | Mouse Wireless   | $195,000  | 2,145  | $91    | 4.1%       |\n|      | **Top 10 Total** | **$2,990,000** | | | **62.5%** |\n|      | All Others (137) | $1,790,000 |    |        | 37.5%       |\n|      | **Grand Total**  | **$4,780,000** | | | **100.0%** |\n\n? Insights:\n- Top 10 products = 62.5% of revenue (strong concentration)\n- Pro Laptop alone = 9.4% of total revenue\n- High-volume low-AOV products: Wireless Buds, Keyboard, Mouse\n- High-AOV low-volume products: Pro Laptop, Standing Desk\n\nCreated:\n- Bar chart: Top 10 products by revenue\n- Pareto chart: Cumulative % of revenue\n\n? Strategic Implications:\n1. Focus inventory on Top 20 products (likely 80% of revenue)\n2. Bundle high-volume + high-margin items\n3. Wireless Buds: Explore upsell to Pro Buds (higher margin?)\n```\n\n## Advanced Features\n\n### Calculated Fields\n\n**Request:** \"Show profit margin by product category\"\n\n**Response:**\n```\nI'll add a calculated field for profit margin.\n\nAssuming you have Cost and Revenue columns, I'll calculate:\nProfit Margin % = (Revenue - Cost) \u002F Revenue × 100%\n\n| Category    | Revenue | Cost    | Profit  | Margin % |\n|-------------|---------|---------|---------|----------|\n| Electronics | $1,700K | $1,020K | $680K   | 40.0%    |\n| Clothing    | $1,730K | $1,125K | $605K   | 35.0%    |\n| Home Goods  | $1,350K | $945K   | $405K   | 30.0%    |\n| Total       | $4,780K | $3,090K | $1,690K | 35.4%    |\n\nElectronics has the highest margin (40%) despite not being the highest revenue category.\n```\n\n### Slicers & Filters\n\n**Request:** \"Add filters for date range and region\"\n\n**Response:**\n```\nI've added slicers for:\n1. Date Range (select quarters or specific months)\n2. Region (select one or multiple regions)\n3. Product Category (filter to specific categories)\n\nThese slicers are linked to the pivot table - any selection will update the data automatically.\n\nTry clicking Q3 in the date slicer to see summer performance!\n```\n\n### Multiple Aggregations\n\n**Request:** \"Show sum and average of sales by region\"\n\n**Response:**\n```\n| Region    | Total Sales | Avg Sale | Order Count |\n|-----------|-------------|----------|-------------|\n| Northeast | $1,250,000  | $625     | 2,000       |\n| Southeast | $980,000    | $490     | 2,000       |\n| Midwest   | $1,100,000  | $524     | 2,100       |\n| West      | $1,450,000  | $690     | 2,100       |\n\nInteresting: Southeast has lowest average sale despite same order count as Northeast.\nThis suggests smaller basket sizes in Southeast - worth investigating!\n```\n\n## Formatting Best Practices\n\nThe Skill applies professional formatting:\n\n### Numbers\n```\nRevenue: $1,250,000 or $1.25M (use M for millions)\nCounts: 2,000 (thousands separator)\nPercentages: 35.0% (1 decimal)\n```\n\n### Conditional Formatting\n```\nTop performers: Green highlight\nBottom performers: Red highlight\nHeatmap: Color gradient from red (low) to green (high)\n```\n\n### Layout\n```\n- Bold headers\n- Freeze top row and left column\n- Subtotals and grand totals\n- Alternating row colors for readability\n```\n\n## Resources\n\nSee resources folder for:\n- `REFERENCE.md`: Pivot table best practices\n- `examples\u002F`: Sample pivot tables for common analyses\n\n## Limitations\n\nThis Skill creates standard pivot tables for:\n- Summarization and aggregation\n- Cross-tabulation\n- Basic calculations (sum, average, count)\n\nFor advanced analysis, you may need:\n- Power Pivot (for complex data models)\n- Pivot charts with custom formatting\n- Integration with external data sources\n- Real-time data refresh\n\n## Version History\n\n- v1.0.0 (2025-10-27): Initial release with core pivot table 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