xlsx

分类: 开发与编程 | 上传者: sizzlecar | 下载: 0 | 版本: v1.0（最新）

当电子表格文件是主要输入或输出时，请使用此技能。这意味着用户想要执行的任何任务：打开、读取、编辑或修复现有 .xlsx、.xlsm、.csv 或 .tsv 文件（例如，添加列、计算公式、格式化、图表、清理混乱数据）；从头开始或从其他数据源创建新的电子表格；或在表格文件格式之间进行转换。特别是当用户通过名称或路径引用电子表格文件时（甚至是随意引用（例如“我下载的 xlsx”））并希望对其执行某些操作或从中生成某些内容时，尤其会触发。还可以触发清理或重组混乱的表格数据文件（格式错误的行、错误的标题、垃圾数据）到正确的电子表格中。可交付成果必须是电子表格文件。当主要交付成果是 Word 文档、HTML 报告、独立 Python 脚本、数据库管道或 Google Sheets API 集成时，即使涉及表格数据，也不要触发。

更新日志: Source: GitHub https://github.com/sizzlecar/orchestral

目录结构

当前层级: tree/main/.claude/skills/xlsx/

📁 scripts/
- 📁 office/
  - 📁 helpers/
    
    📄 __init__.py 0 B
    
    📄 merge_runs.py 5.4 KB
    
    📄 simplify_redlines.py 5.6 KB
  - 📁 schemas/
    
    📁 ecma/
    
    📁 fouth-edition/
    
    📄 opc-contentTypes.xsd 1.9 KB
    
    📄 opc-coreProperties.xsd 2.5 KB
    
    📄 opc-digSig.xsd 2.8 KB
    
    📄 opc-relationships.xsd 1.3 KB
    
    📁 ISO-IEC29500-4_2016/
    
    📄 dml-chart.xsd 73.2 KB
    
    📄 dml-chartDrawing.xsd 6.8 KB
    
    📄 dml-diagram.xsd 50.1 KB
    
    📄 dml-lockedCanvas.xsd 624 B
    
    📄 dml-main.xsd 148.5 KB
    
    📄 dml-picture.xsd 1.2 KB
    
    📄 dml-spreadsheetDrawing.xsd 8.7 KB
    
    📄 dml-wordprocessingDrawing.xsd 14.4 KB
    
    📄 pml.xsd 81.7 KB
    
    📄 shared-additionalCharacteristics.xsd 1.2 KB
    
    📄 shared-bibliography.xsd 7.2 KB
    
    📄 shared-commonSimpleTypes.xsd 6.2 KB
    
    📄 shared-customXmlDataProperties.xsd 1.2 KB
    
    📄 shared-customXmlSchemaProperties.xsd 880 B
    
    📄 shared-documentPropertiesCustom.xsd 2.5 KB
    
    📄 shared-documentPropertiesExtended.xsd 3.4 KB
    
    📄 shared-documentPropertiesVariantTypes.xsd 7.3 KB
    
    📄 shared-math.xsd 22.8 KB
    
    📄 shared-relationshipReference.xsd 1.3 KB
    
    📄 sml.xsd 236.6 KB
    
    📄 vml-main.xsd 25.5 KB
    
    📄 vml-officeDrawing.xsd 24.7 KB
    
    📄 vml-presentationDrawing.xsd 535 B
    
    📄 vml-spreadsheetDrawing.xsd 5.6 KB
    
    📄 vml-wordprocessingDrawing.xsd 3.9 KB
    
    📄 wml.xsd 167.4 KB
    
    📄 xml.xsd 4.5 KB
    
    📁 mce/
    
    📄 mc.xsd 3.1 KB
    
    📁 microsoft/
    
    📄 wml-2010.xsd 25.9 KB
    
    📄 wml-2012.xsd 3.7 KB
    
    📄 wml-2018.xsd 901 B
    
    📄 wml-cex-2018.xsd 1.7 KB
    
    📄 wml-cid-2016.xsd 1002 B
    
    📄 wml-sdtdatahash-2020.xsd 600 B
    
    📄 wml-symex-2015.xsd 745 B
  - 📁 validators/
    
    📄 __init__.py 336 B
    
    📄 base.py 31.9 KB
    
    📄 docx.py 16.0 KB
    
    📄 pptx.py 9.6 KB
    
    📄 redlining.py 8.7 KB
  - 📄 pack.py 4.9 KB
  - 📄 soffice.py 5.2 KB
  - 📄 unpack.py 4.0 KB
  - 📄 validate.py 3.6 KB
- 📄 recalc.py 6.0 KB
📄 LICENSE.txt 1.4 KB
📄 SKILL.md 11.9 KB

SKILL.md

---
name: xlsx
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 — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved."
license: Proprietary. LICENSE.txt has complete terms
---

# 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
- For fill/complete tasks, treat placeholder values such as `N/A`, `NA`, `TBD`, `-`, or `待填写` as missing data candidates unless surrounding context clearly marks them as intentional final values
- In tabular forms, if one row has multiple adjacent placeholder cells under user-entered headers, fill all clearly required fields rather than only one notes/remarks cell

## 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 $#,##0 format; ALWAYS specify units in headers ("Revenue ($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"

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

## 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

# 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
```python
# 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
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 OR CHANGING FORMULAS)**: Use the scripts/recalc.py script
   ```bash
   python scripts/recalc.py output.xlsx
   ```
   - For plain template filling or value-only edits, do **not** run `scripts/recalc.py` unless you changed formulas or must refresh dependent formula outputs as part of verification.
   - For mutating scripts, prefer printing one JSON object with the updated path and changed-cell count so downstream verification can reason about the result deterministically.
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
   - 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

```python
# 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

```python
# 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:

```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 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:
```json
{
  "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"]
    }
  }
}
```

## 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
- 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

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xlsx

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SKILL.md

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