Skill: yinfeihaaaaaaaaaaa/xlsx-localized (version 1.0.0)

--- name: xlsx slug: xlsx-localized version: 1.0.0 author: yinfeihaaaaaaaaaaa license: MIT-0 description: "Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path." triggers: - xlsx - excel - spreadsheet - csv - tsv --- # Requirements for Outputs ## All Excel files ### Professional Font - Use a consistent, professional font (e.g., Arial, Times New Roman) for all deliverables unless otherwise instructed by the user ### Zero Formula Errors - Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?) ### Preserve Existing Templates (when updating templates) - Study and EXACTLY match existing format, style, and conventions when modifying files - Never impose standardized formatting on files with established patterns - Existing template conventions ALWAYS override these guidelines ## Financial models ### Color Coding Standards Unless otherwise stated by the user or existing template #### Industry-Standard Color Conventions - **Blue text (RGB: 0,0,255)**: Hardcoded inputs, and numbers users will change for scenarios - **Black text (RGB: 0,0,0)**: ALL formulas and calculations - **Green text (RGB: 0,128,0)**: Links pulling from other worksheets within same workbook - **Red text (RGB: 255,0,0)**: External links to other files - **Yellow background (RGB: 255,255,0)**: Key assumptions needing attention or cells that need to be updated ### Number Formatting Standards #### Required Format Rules - **Years**: Format as text strings (e.g., "2024" not "2,024") - **Currency**: Use ¥#,##0 format for CNY; $#,##0 for USD. ALWAYS specify units in headers ("Revenue (¥万)") - **Zeros**: Use number formatting to make all zeros "-", including percentages (e.g., "¥#,##0;(¥#,##0);-") - **Percentages**: Default to 0.0% format (one decimal) - **Multiples**: Format as 0.0x for valuation multiples (EV/EBITDA, P/E) - **Negative numbers**: Use parentheses (123) not minus -123 ### Formula Construction Rules #### Assumptions Placement - Place ALL assumptions (growth rates, margins, multiples, etc.) in separate assumption cells - Use cell references instead of hardcoded values in formulas - Example: Use =B5*(1+$B$6) instead of =B5*1.05 #### Formula Error Prevention - Verify all cell references are correct - Check for off-by-one errors in ranges - Ensure consistent formulas across all projection periods - Test with edge cases (zero values, negative numbers) - Verify no unintended circular references #### Documentation Requirements for Hardcodes - Comment or in cells beside (if end of table). Format: "Source: [System/Document], [Date], [Specific Reference], [URL if applicable]" - Examples: - "Source: Company 10-K, FY2024, Page 45, Revenue Note, [SEC EDGAR URL]" - "Source: Company 10-Q, Q2 2025, Exhibit 99.1, [SEC EDGAR URL]" - "Source: Bloomberg Terminal, 8/15/2025, AAPL US Equity" - "Source: Wind, 8/20/2025, Consensus Estimates" # XLSX creation, editing, and analysis ## Overview A user may ask you to create, edit, or analyze the contents of an .xlsx file. You have different tools and workflows available for different tasks. ## Important Requirements **LibreOffice Required for Formula Recalculation**: You can assume LibreOffice is installed for recalculating formula values using the `scripts/recalc.py` script. The script automatically configures LibreOffice on first run. **Windows Localization**: This skill has been adapted for Windows. The `scripts/office/soffice.py` helper searches for soffice.exe in standard Windows install paths and respects the `SOFFICE_PATH` environment variable. ## Reading and analyzing data ### Data analysis with pandas For data analysis, visualization, and basic operations, use **pandas** which provides powerful data manipulation capabilities: ```python import pandas as pd # Read Excel df = pd.read_excel('file.xlsx') # Default: first sheet all_sheets = pd.read_excel('file.xlsx', sheet_name=None) # All sheets as dict # Analyze df.head() # Preview data df.info() # Column info df.describe() # Statistics # Write Excel df.to_excel('output.xlsx', index=False) ``` ## Excel File Workflows ## CRITICAL: Use Formulas, Not Hardcoded Values **Always use Excel formulas instead of calculating values in Python and hardcoding them.** This ensures the spreadsheet remains dynamic and updateable. ### WRONG - Hardcoding Calculated Values ```python # Bad: Calculating in Python and hardcoding result total = df['Sales'].sum() sheet['B10'] = total # Hardcodes 5000 ``` ### CORRECT - Using Excel Formulas ```python # Good: Let Excel calculate the sum sheet['B10'] = '=SUM(B2:B9)' ``` This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes. ## Common Workflow 1. **Choose tool**: pandas for data, openpyxl for formulas/formatting 2. **Create/Load**: Create new workbook or load existing file 3. **Modify**: Add/edit data, formulas, and formatting 4. **Save**: Write to file 5. **Recalculate formulas (MANDATORY IF USING FORMULAS)**: Use the scripts/recalc.py script ```bash python scripts/recalc.py output.xlsx ``` 6. **Verify and fix any errors**: - The script returns JSON with error details - If `status` is `errors_found`, check `error_summary` for specific error types and locations - Fix the identified errors and recalculate again ### Creating new Excel files ```python from openpyxl import Workbook from openpyxl.styles import Font, PatternFill, Alignment wb = Workbook() sheet = wb.active # Add data sheet['A1'] = 'Hello' sheet['B1'] = 'World' sheet.append(['Row', 'of', 'data']) # Add formula sheet['B2'] = '=SUM(A1:A10)' # Formatting sheet['A1'].font = Font(bold=True, color='FF0000') sheet['A1'].fill = PatternFill('solid', start_color='FFFF00') sheet['A1'].alignment = Alignment(horizontal='center') # Column width sheet.column_dimensions['A'].width = 20 wb.save('output.xlsx') ``` ### Editing existing Excel files ```python from openpyxl import load_workbook wb = load_workbook('existing.xlsx') sheet = wb.active # or wb['SheetName'] for specific sheet # Working with multiple sheets for sheet_name in wb.sheetnames: sheet = wb[sheet_name] print(f"Sheet: {sheet_name}") # Modify cells sheet['A1'] = 'New Value' sheet.insert_rows(2) # Insert row at position 2 sheet.delete_cols(3) # Delete column 3 # Add new sheet new_sheet = wb.create_sheet('NewSheet') new_sheet['A1'] = 'Data' wb.save('modified.xlsx') ``` ## Recalculating formulas Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided `scripts/recalc.py` script to recalculate formulas: ```bash python scripts/recalc.py <excel_file> [timeout_seconds] ``` Example: ```bash python scripts/recalc.py output.xlsx 30 ``` The script: - Automatically sets up LibreOffice macro on first run - Recalculates all formulas in all sheets - Scans ALL cells for Excel errors (#REF!, #DIV/0!, etc.) - Returns JSON with detailed error locations and counts - Works on Windows, Linux, and macOS ## Formula Verification Checklist ### Essential Verification - [ ] **Test 2-3 sample references**: Verify they pull correct values before building full model - [ ] **Column mapping**: Confirm Excel columns match (e.g., column 64 = BL, not BK) - [ ] **Row offset**: Remember Excel rows are 1-indexed (DataFrame row 5 = Excel row 6) ### Common Pitfalls - [ ] **NaN handling**: Check for null values with `pd.notna()` - [ ] **Far-right columns**: FY data often in columns 50+ - [ ] **Multiple matches**: Search all occurrences, not just first - [ ] **Division by zero**: Check denominators before using `/` in formulas (#DIV/0!) - [ ] **Wrong references**: Verify all cell references point to intended cells (#REF!) - [ ] **Cross-sheet references**: Use correct format (Sheet1!A1) for linking sheets ## Best Practices ### Library Selection - **pandas**: Best for data analysis, bulk operations, and simple data export - **openpyxl**: Best for complex formatting, formulas, and Excel-specific features ### Working with openpyxl - Cell indices are 1-based (row=1, column=1 refers to cell A1) - Use `data_only=True` to read calculated values: `load_workbook('file.xlsx', data_only=True)` - **Warning**: If opened with `data_only=True` and saved, formulas are replaced with values and permanently lost - For large files: Use `read_only=True` for reading or `write_only=True` for writing ### Working with pandas - Specify data types to avoid inference issues: `pd.read_excel('file.xlsx', dtype={'id': str})` - For large files, read specific columns: `pd.read_excel('file.xlsx', usecols=['A', 'C', 'E'])` - Handle dates properly: `pd.read_excel('file.xlsx', parse_dates=['date_column'])` ## Code Style Guidelines When generating Python code for Excel operations: - Write minimal, concise Python code without unnecessary comments - Avoid verbose variable names and redundant operations - Avoid unnecessary print statements ## Dependencies Required Python packages: - `openpyxl` — reading/writing .xlsx files with formula and formatting support - `pandas` — data analysis and bulk operations - `xlrd` — reading older .xls files (optional) External dependency: - **LibreOffice** — required for formula recalculation via `scripts/recalc.py` - Download: https://www.libreoffice.org/download/download-libreoffice/ - Or set `SOFFICE_PATH` environment variable to your soffice.exe location ## Localization Notes (Windows) This skill was localized from the Anthropic official xlsx skill (https://github.com/anthropics/skills). Key adaptations: - Removed Linux-specific AF_UNIX socket shim (LD_PRELOAD) from `soffice.py` - Added Windows LibreOffice path detection (`C:\Program Files\LibreOffice\program\soffice.exe`) - Added `SOFFICE_PATH` environment variable support - Currency defaults changed to ¥ (CNY) for Chinese market context - Macro directory uses `%APPDATA%\LibreOffice\4\user\basic\Standard` on Windows


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (HIGH)