Tencent SkillHub · Data Analysis

Xlsx Cn

Excel 表格处理 | Excel Spreadsheet Processing. 创建、读取、编辑 Excel 文件 | Create, read, edit Excel files. 支持公式、图表、数据分析 | Supports formulas, charts, data analysis. 触发词：E...

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: LICENSE.txt, SKILL.md, _meta.json, scripts/office/helpers/__init__.py, scripts/office/helpers/merge_runs.py, scripts/office/helpers/simplify_redlines.py

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.1

Provenance

Publisher: guohongbin-git
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 25 sections Open source page

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

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 $#,##0 format; ALWAYS specify units in headers ("Revenue ($mm)") 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: FactSet, 8/20/2025, Consensus Estimates Screen"

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, including in sandboxed environments where Unix sockets are restricted (handled by scripts/office/soffice.py)

Data analysis with pandas

For data analysis, visualization, and basic operations, use pandas which provides powerful data manipulation capabilities: 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)

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

# Bad: Calculating in Python and hardcoding result total = df['Sales'].sum() sheet['B10'] = total # Hardcodes 5000 # Bad: Computing growth rate in Python growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue'] sheet['C5'] = growth # Hardcodes 0.15 # Bad: Python calculation for average avg = sum(values) / len(values) sheet['D20'] = avg # Hardcodes 42.5

✅ CORRECT - Using Excel Formulas

# Good: Let Excel calculate the sum sheet['B10'] = '=SUM(B2:B9)' # Good: Growth rate as Excel formula sheet['C5'] = '=(C4-C2)/C2' # Good: Average using Excel function sheet['D20'] = '=AVERAGE(D2:D19)' This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes.

Common Workflow

Choose tool: pandas for data, openpyxl for formulas/formatting Create/Load: Create new workbook or load existing file Modify: Add/edit data, formulas, and formatting Save: Write to file Recalculate formulas (MANDATORY IF USING FORMULAS): Use the scripts/recalc.py script python scripts/recalc.py output.xlsx 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 Common errors to fix: #REF!: Invalid cell references #DIV/0!: Division by zero #VALUE!: Wrong data type in formula #NAME?: Unrecognized formula name

Creating new Excel files

# Using openpyxl for formulas and formatting 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

# Using openpyxl to preserve formulas and formatting from openpyxl import load_workbook # Load existing file 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: python scripts/recalc.py <excel_file> [timeout_seconds] Example: 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 both Linux and macOS

Formula Verification Checklist

Quick checks to ensure formulas work correctly:

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

Formula Testing Strategy

Start small: Test formulas on 2-3 cells before applying broadly Verify dependencies: Check all cells referenced in formulas exist Test edge cases: Include zero, negative, and very large values

Interpreting scripts/recalc.py Output

The script returns JSON with error details: { "status": "success", // or "errors_found" "total_errors": 0, // Total error count "total_formulas": 42, // Number of formulas in file "error_summary": { // Only present if errors found "#REF!": { "count": 2, "locations": ["Sheet1!B5", "Sheet1!C10"] } } }

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 Formulas are preserved but not evaluated - use scripts/recalc.py to update values

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

IMPORTANT: 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 For Excel files themselves: Add comments to cells with complex formulas or important assumptions Document data sources for hardcoded values Include notes for key calculations and model sections

Category context

Data access, storage, extraction, analysis, reporting, and insight generation.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

3 Scripts1 Docs1 Config1 Files

SKILL.md Primary doc
scripts/office/helpers/__init__.py Scripts
scripts/office/helpers/merge_runs.py Scripts
scripts/office/helpers/simplify_redlines.py Scripts
_meta.json Config
LICENSE.txt Files