xlsx

name	xlsx
description	Use this skill when designing Excel or Google Sheets workbooks with formulas, named ranges, pivot tables, charts, and data validation, or when creating financial models, trackers, and dashboards in spreadsheet format. Not for database design or SQL queries. Not for generating CSV files without workbook structure.
version	1.0.0
author	community
tags	["files","excel","spreadsheet","xlsx","formulas"]
license	MIT

XLSX

Overview

This skill covers the design and implementation of Excel-compatible workbooks (.xlsx) and Google Sheets for professional use. It addresses workbook architecture (sheet organization, tab naming, data vs. calculation vs. presentation layers), formula design (lookup formulas, array formulas, dynamic arrays), named ranges for readable and maintainable formulas, pivot tables for summarization, charts for visualization, data validation for input integrity, and conditional formatting for visual cues. Outputs are either complete spreadsheet designs with formula specifications or Python code using openpyxl or xlsxwriter for programmatic workbook generation.

When to Use

• Designing a financial model (budget, forecast, P&L, cash flow) in spreadsheet format
• Building a data tracker with validation, dropdowns, and summary dashboards
• Creating a reporting workbook that automatically calculates KPIs from raw data input
• Designing a pivot table structure for multi-dimensional data summarization
• Specifying formulas and structure for a workbook someone else will build in Excel
• Generating Excel workbooks programmatically from application data using Python
• Auditing or improving an existing spreadsheet's formula logic and structure

When NOT to Use

• Replacing a proper relational database for data with more than ~100,000 rows
• Building real-time dashboards that require live database connectivity (use BI tools like Tableau or Power BI)
• Designing data pipelines or ETL processes (use data engineering tools)
• Creating presentations (use pptx skill)
• Generating PDF reports (use pdf or docx skills)

Quick Reference

Task	Approach
Look up a value from a table	XLOOKUP(lookup_value, lookup_range, return_range) or INDEX/MATCH
Sum with conditions	SUMIFS(sum_range, criteria_range1, criteria1, ...)
Count with conditions	COUNTIFS(range1, criteria1, range2, criteria2, ...)
Dynamic subtotals	SUBTOTAL(9, range) — respects filters; 9 = SUM
Protect formula cells	Lock formula cells; protect sheet; leave input cells unlocked
Create a dropdown list	Data Validation → List → specify source range or comma-separated values
Name a range	Formulas → Name Manager → New; use in formulas as =SUM(Revenue)
Pivot table refresh	Right-click pivot → Refresh; or VBA ThisWorkbook.RefreshAll

Instructions

1. Design the workbook architecture — Separate concerns across sheets:

   • README / Instructions sheet: Document the workbook purpose, how to use it, input cells, and assumptions.
   • Inputs / Assumptions sheet: All user-modifiable inputs in one place. Color-code input cells (e.g., blue font or yellow fill).
   • Data / Raw Data sheet(s): Source data that feeds calculations. Formatted as structured Tables (Ctrl+T).
   • Calculations / Model sheet(s): Formulas and intermediate calculations. No hard-coded values — all reference the Inputs sheet.
   • Dashboard / Summary sheet: Charts, KPIs, and formatted output for stakeholders. No formulas — only links to calculation sheets.

2. Define named ranges — Name every important range or cell: input assumptions (e.g., Discount_Rate, Headcount_2025), summary outputs, and frequently-referenced lookup tables. Named ranges make formulas readable: =Revenue * Gross_Margin_Rate is clearer than =B12 * Assumptions!C5.

3. Build lookup structures — Use structured Tables (Ctrl+T) for all data ranges. Table references (Table1[Column]) update automatically when rows are added, unlike fixed ranges. For lookups, prefer XLOOKUP over VLOOKUP — it supports left-lookups, exact/approximate match, and a default-if-not-found value.

4. Design the formula layer — Write formulas that are: (a) readable — use named ranges and intermediate cells rather than nesting 5 functions deep; (b) auditable — one calculation step per cell; (c) consistent — the same formula across a row or column, not one-off adjustments; (d) error-handled — wrap in IFERROR or IFNA where appropriate.

5. Add data validation — Apply data validation to all input cells to prevent invalid entries:

   • Numeric inputs: whole number or decimal with min/max bounds
   • Category inputs: dropdown list from a named range or fixed list
   • Date inputs: date validation with reasonable min/max

Examples

Example 1: Monthly Expense Tracker Workbook

Input: A small team needs a personal/team monthly expense tracker with categories, budget vs. actual comparison, and a summary dashboard.

Output:

Workbook structure:

Sheet 1: README
  - Purpose, how to use, input cells highlighted
  - Version: 1.0 | Owner: Finance Team | Updated: 2025-01

Sheet 2: Budgets (Inputs)
  Named ranges: Budget_Jan, Budget_Feb, ... Budget_Dec (or annual budget split by month)
  Table structure:
    | Category | Annual Budget | Jan Budget | Feb Budget | ... | Dec Budget |
    | Salaries | $120,000      | $10,000    | $10,000    | ... | $10,000    |
    | Software  | $24,000      | $2,000     | $2,000     | ... | $2,000     |
    | Travel    | $12,000      | $0         | $500       | ... | $2,000     |
  Input validation: Numeric, min $0
  Note: Only the Budgets sheet should be editable by most users

Sheet 3: Transactions (Data)
  Formatted as Excel Table "Transactions"
  Columns: Date | Description | Category | Amount | Approved By | Notes
  Category column: Dropdown validation from Budget category list
  Amount: Positive number validation; no negatives (use separate refund row)

Sheet 4: Monthly Summary (Calculations)
  SUMIFS formula for actuals by category by month:
    =SUMIFS(Transactions[Amount], Transactions[Category], [@Category],
            Transactions[Date], ">="&DATE(Year, Month, 1),
            Transactions[Date], "<"&DATE(Year, Month+1, 1))
  
  Variance formula: =Budget_Amount - Actual_Amount
  Variance %: =IF(Budget_Amount=0, "N/A", Variance/Budget_Amount)
  Status: =IF(Variance_Pct < -0.1, "Over Budget", IF(Variance_Pct < 0, "Watch", "On Track"))
  Conditional formatting: Status cell — red/yellow/green based on text value

Sheet 5: Dashboard
  KPI Cards (top row):
    Total Budget YTD | Actual Spend YTD | Remaining Budget | % Budget Used
  
  Chart 1: Stacked bar chart — Budget vs Actual by Category (current month)
  Chart 2: Line chart — Cumulative spend vs cumulative budget over the year
  Chart 3: Donut chart — Spend by category as % of total
  Slicer: Month selector (updates all charts)

Key formulas:

-- Actual spend for a category in a specific month --
=SUMIFS(
    Transactions[Amount],
    Transactions[Category], B5,
    Transactions[Date], ">="&DATE($B$2, $C$2, 1),
    Transactions[Date], "<"&EOMONTH(DATE($B$2, $C$2, 1), 0)+1
)

-- Budget utilization status (with color via conditional formatting) --
=IF(D5/C5 > 1, "OVER", IF(D5/C5 > 0.9, "WATCH", "OK"))

-- Year-to-date actual spend (all months through current) --
=SUMIFS(
    Transactions[Amount],
    Transactions[Date], ">="&DATE(YEAR(TODAY()), 1, 1),
    Transactions[Date], "<="&TODAY()
)

Example 2: Sales Pipeline Spreadsheet with Dashboard

Input: A sales team needs a pipeline tracker that shows deals by stage, weighted pipeline value, forecast by month, and rep performance.

Output:

Workbook structure and formulas:

Sheet 1: Deals (Data — Excel Table "Pipeline")
  Columns and validation:
    Deal ID        | Auto-increment (=MAX(Pipeline[Deal_ID])+1 for new row)
    Company        | Text, required
    Deal Owner     | Dropdown: named range "SalesReps" from Config sheet
    Stage          | Dropdown: Prospect / Qualified / Proposal / Negotiation / Closed Won / Closed Lost
    Deal Value ($) | Number, min $0
    Close Date     | Date, must be >= TODAY()
    Probability (%)| Auto-fill from Stage using XLOOKUP:
                     =XLOOKUP([@Stage], Config!A:A, Config!B:B, "")
    Weighted Value  | =[@[Deal Value]]*[@[Probability]]
    Days in Stage   | =TODAY()-[@[Stage Entry Date]]
    Notes          | Text

Sheet 2: Config (Inputs — locked except admin)
  Stage Probabilities:
    | Stage        | Probability | Target Days in Stage |
    | Prospect     | 10%         | 14                   |
    | Qualified    | 25%         | 21                   |
    | Proposal     | 50%         | 14                   |
    | Negotiation  | 75%         | 10                   |
    | Closed Won   | 100%        | —                    |
    | Closed Lost  | 0%          | —                    |
  Named range: SalesReps (list of rep names)
  Named range: Stages (list of stage names for validation)

Sheet 3: Forecast (Calculations)
  Monthly forecast by rep:
    =SUMPRODUCT(
        (Pipeline[Deal_Owner]=A3) *
        (MONTH(Pipeline[Close_Date])=B2) *
        (YEAR(Pipeline[Close_Date])=YEAR(TODAY())) *
        Pipeline[Weighted_Value]
    )
  
  Stage funnel (count by stage):
    =COUNTIFS(Pipeline[Stage], "Qualified", Pipeline[Deal_Owner], $A3)

Sheet 4: Rep Scorecards (Calculations)
  Per-rep metrics:
    | Rep Name | Open Pipeline | Wtd Pipeline | Deals Won MTD | Win Rate | Avg Deal Size |
  
  Win Rate: =COUNTIFS(Pipeline[Deal_Owner], A3, Pipeline[Stage], "Closed Won") /
              MAX(1, COUNTIFS(Pipeline[Deal_Owner], A3, Pipeline[Stage], "Closed Won") +
                     COUNTIFS(Pipeline[Deal_Owner], A3, Pipeline[Stage], "Closed Lost"))

Sheet 5: Dashboard
  Slicers: Month, Deal Owner, Stage
  
  Chart 1: Funnel chart — deals count and value by stage
  Chart 2: Bar chart — weighted pipeline by rep (sorted descending)
  Chart 3: Line chart — monthly forecast vs. monthly quota
  Chart 4: Horizontal bar — days in stage by deal (aging heatmap — highlight if > target)
  
  KPI Cards:
    Total Open Pipeline: =SUMIFS(Pipeline[Deal Value], Pipeline[Stage], "<>Closed Won", Pipeline[Stage], "<>Closed Lost")
    Weighted Pipeline: =SUMIFS(Pipeline[Weighted Value], ...)
    Deals Closing This Month: =COUNTIFS(...)
    Forecast vs. Quota: =[Forecast MTD] / [Quota MTD]

Conditional formatting rules:

Days in Stage column:
  Green: <= target days for stage
  Yellow: target days < days <= 2× target
  Red: > 2× target days (deal is stalled)

Win Rate cell:
  Green: >= 30%
  Yellow: 20–30%
  Red: < 20%

Best Practices

• Never hard-code values in formula cells — all constants belong in named cells on the Inputs sheet
• Use Excel Tables (Ctrl+T) for all data ranges — they auto-expand and work with structured references
• Color-code consistently: blue/yellow = user input, white = calculated, gray = locked/reference
• Keep one concept per column — never store two data points in one cell (e.g., "100 units - estimate")
• Test formulas at boundary conditions: empty cells, zero values, negative values, future dates
• Document complex formulas with a comment in the adjacent cell explaining what it calculates
• Use IFERROR to handle divide-by-zero and missing lookups gracefully rather than showing #N/A

Common Mistakes

• Using VLOOKUP with a fixed column index — it breaks if columns are inserted; use XLOOKUP or INDEX/MATCH
• Circular references — often caused by accidentally referencing a cell in its own formula; use iterative calc only when intentional
• Mixing data and formatting in the same cells (e.g., writing "$1,200 est." — store 1200 and format the cell)
• Not protecting formula cells — users accidentally overwrite calculated cells with typed values
• Using merged cells in data tables — they break sorting, filtering, and SUMIFS functions
• Forgetting to refresh pivot tables after updating source data — add a refresh button with a simple macro
• Building massive all-in-one sheets instead of separating inputs, calculations, and outputs into layers

Tips & Tricks

• Press Ctrl+~ to toggle between formula view and value view — great for auditing
• Use Ctrl+[ to jump to a formula's precedent cells — follow the formula chain
• LAMBDA function lets you define reusable custom functions in Excel without VBA
• LET function stores intermediate calculations in named variables within a single formula
• For programmatic generation, openpyxl handles formulas and styles; xlsxwriter is faster for large datasets
• Use Power Query (Get & Transform) to pull and transform data from external sources without VBA
• Name your Excel Tables descriptively (Pipeline, Budgets, not Table1, Table2) — structured references become self-documenting

Related Skills

• docx
• pdf