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Yige 8f7953948f fix: 补齐市场静态事实并保持旧客户端兼容 (#184 )

## 中文

补齐已有 owner 与已锁定上游内容可证明的许可证、技能类型和收录版本。保留 Anthropic Source Available
和其他附加条件，不把所有内容统一标为 MIT，不提升 listing-only 的安装准入。

README 保留最新的双语结构及以 manifest 为准的数量口径，增加静态目录不存访问量、下载量、评分或 Stars 的边界。

发布兼容性：**本 PR 不包含**两支团队新增的本地化字段，也不包含微信支付 `latestVersion:
1.0`；旧客户端会拒绝这些新字段，另以待发布 PR 保留，等匹配客户端发布后再合并。

验证：47 项目录校验测试、19 项 i18n 测试、5 项合集生成测试通过；完整目录/i18n/翻译新鲜度检查通过，6
个固定来源合集的生成检查通过，2 个浮动来源按既有规则明确跳过。发现并修复了生成器在 Windows 使用反斜杠造成的误报：输出改为 POSIX
路径，比较规则不放宽、不重写合集数据。

## English

Fill only license, type and listed-release facts supported by existing
owners or pinned upstream content. Preserve custom license restrictions
and acquisition eligibility. Keep the current bilingual README and
manifest-derived inventory; do not author usage analytics.

This PR deliberately excludes localized team fields and the two-part
WeChat Pay version that older clients reject. Those changes remain
pending a compatible client release.

Validation passes: 47 catalog-validator tests, 19 i18n tests, five
collection-generator tests, complete catalog and translation checks, and
generated inventory checks for six pinned collections. Two mutable
sources remain explicitly skipped. Fix Windows-generated path separators
without weakening comparison or changing declared children.

2026-09-22 01:09:03 +08:00

18 KiB

Raw Blame History

license, name, description, version, type, risk_level, status, disable-model-invocation, tags, metadata, market

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xlsx Skill

L0: One-line Summary

Create, edit, and analyze Excel spreadsheets (.xlsx), with support for formulas, formatting, data analysis, and visualization.

L1: Overview and Use Cases

Capability

xlsx is a Procedural Skill that provides full processing capabilities for Excel spreadsheets. Built on Python libraries (openpyxl, pandas), it supports creating new spreadsheets, editing existing files, data analysis, formula calculation, formatting, and chart generation, and uses LibreOffice for formula recalculation and error detection.

Use Cases

The user needs to create a new Excel spreadsheet (financial models, data reports, etc.)
The user needs to edit or modify an existing .xlsx file
The user needs to perform data analysis, cleaning, or format conversion (CSV/TSV → XLSX)
The user needs to add formulas, charts, or formatting to an existing spreadsheet

L2: Detailed Specification

Script Path Rule (mandatory)

The Python scripts bundled with this skill live inside the skill installation directory. You MUST use full paths when invoking them — never use bare relative paths.

The skill directory is provided by the <skill-dir> tag in the context. Prefix all scripts/ commands accordingly:

python "<skill-dir>/scripts/recalc.py" output.xlsx
python "<skill-dir>/scripts/recalc.py" output.xlsx 30

NEVER run python scripts/recalc.py directly — that relative path does not exist in the user's working directory.

Prerequisites

Python 3 (required)

Before performing any Python operation, first check whether Python is available:

python3 --version 2>/dev/null || python --version 2>/dev/null

If the command fails (Python is unavailable), you must stop and instruct the user to install Python 3:

macOS: brew install python3 or download from https://www.python.org/downloads/
Windows: winget install Python.Python.3 or download from python.org (check "Add Python to PATH" during installation)
Linux (Debian/Ubuntu): sudo apt install python3 python3-pip
Linux (Fedora/RHEL): sudo dnf install python3 python3-pip

For more detailed environment setup help: for Python-related issues load the python-runtime Skill; for everything else (LibreOffice / containers / WSL), load the dev-environment-setup Skill.

Python Package Dependencies

This Skill depends on the following Python packages (checked on demand):

openpyxl — Excel file creation and editing (formulas, formatting)
pandas — data analysis and read/write

Detection method:

python3 -c "import openpyxl; import pandas" 2>/dev/null || echo "MISSING"

If missing, instruct the user to install: pip install openpyxl pandas

Requirements for Outputs

Output Rule

When you create or modify a .xlsx file, you MUST tell the user the absolute path of the output file in your response. Example: "File saved to: /path/to/output.xlsx"

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

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

# 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"]
    }
  }
}

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'])

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