# Excel Workbook Analyzer Comprehensively analyze Excel workbooks by combining factual extraction (via `xls-extract`) with AI-powered insights. ## Usage ``` \/analyze-excel \/path\/to\/workbook.xlsx ``` With custom output directory: ``` \/analyze-excel \/path\/to\/workbook.xlsx -o .\/my-output ``` Skip screenshots (required on macOS): ``` \/analyze-excel \/path\/to\/workbook.xlsx --no-screenshots ``` ### Using Pre-Existing Extraction Output If you already have extraction output (e.g., generated on Windows with screenshots), you can skip the extraction phase and go straight to AI analysis: ``` \/analyze-excel --existing \/path\/to\/existing_analysis ``` This is useful for: - Running extraction on Windows (with screenshots), then analyzing on Mac - Re-analyzing the same workbook without re-extracting - Sharing extraction output with others for AI analysis ## How This Skill Works This skill has two distinct phases: ### Phase 1: Factual Extraction (Python\/xls-extract) The `xls-extract` library handles all deterministic extraction: - Sheets, formulas, named ranges - VBA macros, Power Query M code - Charts, pivot tables, tables - Conditional formatting, data validation - Screenshots (Windows only) - HTML report and Markdown documentation Run the extraction: ```bash cd ~\/.claude\/skills\/excel-analyzer source .venv\/bin\/activate # .venv\Scripts\activate on Windows python -m src.main "" -o "" ``` ### Phase 2: AI Analysis (Claude) After extraction, Claude reads the generated files and provides insights that require understanding and interpretation: 1. **Read the generated files**: - `\/summary.md` - Start here - `\/sheets\/*.md` - Sheet details - `\/screenshots\/*.png` - Visual layout - `\/vba\/*.md` - VBA code (if present) - `\/formulas\/*.md` - Formula analysis 2. **Provide AI-powered analysis**: #### Workbook Purpose & Context - What is the business purpose? - Who are the likely users? - What decisions does it support? #### Sheet-by-Sheet Analysis For main sheets (skip `pbi-*`, `ref-*`, `config-*` data sheets): - **Purpose**: What business question does it answer? - **Key Inputs**: Dropdowns, filters, parameters users can change - **Data Layout**: What rows\/columns represent - **Key Outputs**: Important calculations and summaries - **Visualizations**: What charts show and why #### Architecture & Data Flow - How sheets connect to each other - Data flow diagram (inputs → calculations → outputs) - Dependencies between components #### Complexity Analysis - Most complex formulas explained in plain English - Clever techniques worth noting - Areas that would be hard to maintain #### Issues & Risks - Error cells explained with fix suggestions - External reference risks - Security concerns (macros, external links) - Data quality issues #### VBA Analysis (if present) - What each macro does - Security assessment - Whether macros are essential #### Recommendations - Improvements and modernization suggestions - User warnings and gotchas - Migration considerations ## Output Structure The extraction creates: ``` \/ ├── index.html # Interactive HTML report ├── README.md # Entry point for Claude ├── summary.md # Quick facts ├── sheets\/ # Per-sheet details │ ├── _index.md │ └── .md ├── formulas\/ # Formula analysis ├── vba\/ # VBA code (if .xlsm) ├── power_query\/ # M code (if present) ├── features\/ # CF, validations, etc. ├── issues\/ # Errors, external refs ├── screenshots\/ # Visual captures │ ├── _full.png │ └── _detail.png └── workbook\/ # Workbook-wide items ├── vba.html └── connections.html ``` ## Example Session When user runs `\/analyze-excel \/path\/to\/budget.xlsx`: **Step 1** - Run extraction: ```bash cd ~\/.claude\/skills\/excel-analyzer && source .venv\/bin\/activate python -m src.main "\/path\/to\/budget.xlsx" -o "\/path\/to\/budget_analysis" ``` **Step 2** - Claude reads and analyzes: ``` Reading summary.md... Reading sheets\/Input.md, sheets\/Calculations.md, sheets\/Summary.md... Viewing screenshots... ``` **Step 3** - Claude provides insights: > "This workbook is a departmental budget tracker used by finance teams to monitor monthly spending against plan. > > **Key sheets:** > - **Input**: Where users enter monthly actuals via data validation dropdowns > - **Calculations**: SUMIFS formulas aggregate by department and category > - **Summary**: Executive dashboard with YTD variance analysis > > **Data flow:** > ``` > [Input] → [Calculations] → [Summary] → [Charts] > ↑ > [ref-budget] > ``` > > **Complex formula on Calculations!E15:** > This SUMIFS with multiple criteria calculates YTD spend by department... > > **Issues found:** > - 3 #REF! errors in row 45 - likely caused by deleted rows > - External link to `\\server\data.xlsx` may break if moved > > **Recommendations:** > - Replace VLOOKUP formulas with XLOOKUP for better maintainability > - The manual copy-paste step could be automated with Power Query" ## Requirements - Python 3.11+ - `xls-extract` library (installed automatically) - **Screenshots**: Windows only with Desktop Excel ## Setup First-time setup: ```bash cd ~\/.claude\/skills\/excel-analyzer python -m venv .venv source .venv\/bin\/activate # .venv\Scripts\activate on Windows pip install -e . ``` For screenshots on Windows: ```bash pip install "xls-extract[screenshots]" ``` ## Limitations - **Screenshots**: Windows only (macOS not supported) - **DAX\/Power Pivot**: Can detect but not fully extract - **Very Hidden Sheets**: Documented but not screenshotted - **ActiveX Controls**: Limited extraction