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Update docx, pptx, and xlsx skills (#1447)

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Add support for template formats (.dotx, .potx, .xltx) across validation
and the helper scripts.

Consolidate the shared office helpers into a single module, replacing the
pack/unpack pipeline with explicit zip/unzip steps. Extraction now rejects
symlink and path-traversal archive entries. Move run merging to a
standalone docx script, since it only ever applied to Word documents.

Fix the redlining validator so it compares against the original even when
a document has no tracked changes, which is when an untracked edit would
otherwise go unreported.

Provision a LibreOffice user profile per invocation so conversions work in
sandboxed environments.

Trim the skill docs to the guidance that earns its place.
This commit is contained in:
Peter Lai
2026-07-16 19:47:37 -07:00
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---
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."
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, .xltx, .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
# XLSX creation, editing, and analysis
## All Excel files
| Task | Approach |
|---|---|
| **Create** or **edit** with formulas/formatting | `openpyxl` — see gotchas below |
| **Bulk data** in or out | `pandas` (`read_excel`, `to_excel`) |
| **Quick look** at a sheet | `markitdown file.xlsx` — `## SheetName` per sheet; reads `.xlsm` too. No cell coordinates, so don't plan edits from it |
| **Read** a model (formulas *and* values) | two `load_workbook` passes — see gotchas |
### Professional Font
- Use a consistent, professional font (e.g., Arial, Times New Roman) for all deliverables unless otherwise instructed by the user
> `openpyxl`, `pandas`, and `markitdown` are preinstalled — do not run `pip install` first; write the script and import directly. Only if an import fails (or the `markitdown` command is missing): `pip install` the missing package.
### Zero Formula Errors
- Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)
> Script paths below are relative to this skill's directory.
### 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
## Requirements for every output
- **Professional font** (Arial, Times New Roman) throughout, unless the user says otherwise.
- **Zero formula errors.** Never ship while `recalc.py` reports `errors_found`. If you think an error predates you, prove it: load the *original* with `data_only=True` and look at that cell. An error you introduced looks exactly like one you inherited.
- **Use formulas, never hardcoded results.** Write `sheet['B10'] = '=SUM(B2:B9)'`, not the Python-computed total. The sheet must recalculate when its inputs change.
- **Follow the user's spec literally.** Exact tab names, exact column headers, and the formula they spelled out. A redesign that computes something else fails, however elegant.
- **Document every assumption and hardcoded number** where the reader will see it — a cell comment, or an adjacent cell at a table's end. Cite a real source when one exists (`Source: Company 10-K, FY2024, Page 45, Revenue Note, [SEC EDGAR URL]`); when the number came from the user, say so plainly.
- **A workbook *you create* for someone to fill in** needs a short legend naming which cells to edit, and one example row of realistic values showing the expected format. Never add such a row to a file you were asked to edit.
- **Editing an existing file: match its conventions exactly.** They override every guideline here. Find its designated input cells first — a distinct font color, fill, or shading marks them — write only there, and leave every existing formula untouched.
## Recalculate (mandatory whenever the file contains formulas)
openpyxl writes formulas as strings with **no cached values**. Until you recalculate, every
formula cell reads back as `None` to anything reading cached values — `pandas`,
`load_workbook(data_only=True)`, and most previewers.
```bash
python scripts/recalc.py output.xlsx [timeout_seconds] # default 30
```
LibreOffice computes every formula, the file is **rewritten in place**, and you get JSON:
`status` (`success` | `errors_found`), `total_formulas`, `total_errors`, and an
`error_summary` naming up to 100 cells per error type (`locations_truncated` says how many it
withheld — trust `total_errors`, not the length of the list). Fix what it names and run it
again. **JSON with an `error` key instead of a `status` means nothing was recalculated**, and
only that case exits non-zero — `errors_found` exits 0, so never treat a clean exit as a clean
workbook.
**A green recalc proves your formulas *evaluate*, not that they are *right*.** An off-by-one
range or a reference to the wrong row yields a clean, error-free file with wrong numbers.
Write 2–3 formulas first and check they pull the values you expect, before building out a grid.
**A workbook that links to another file loses those links** if you re-save it with openpyxl and
then recalculate. Such a formula reads `='[1]Returns Analysis'!$B$2` — the `[1]` is an index
into the workbook's external-reference list, naming a *separate file on disk*, not a sheet.
That file is rarely present here, so the cell's cached value is the only thing holding its
data. openpyxl strips that value on save; LibreOffice then has to resolve the reference for
real, fails, writes `#NAME?`, and deletes every link. `recalc.py` refuses to run in that state
— copy those cells' values out of the original before you save over them (`--force` overrides,
and accepts the loss).
## Choosing formulas that survive verification
LibreOffice implements fewer functions than Excel, and one it cannot evaluate becomes a
literal `#NAME?` baked into the file you deliver.
- **Prefer Excel-2007-era functions** — `SUMIFS`, `INDEX`, `MATCH`, `IFERROR`, `SUMPRODUCT` — which need no prefix.
- **Six post-2007 functions work, but only with an `_xlfn.` prefix**, because openpyxl writes your formula into the XML verbatim and Excel stores post-2007 names prefixed (its UI hides the prefix): `_xlfn.TEXTJOIN`, `_xlfn.CONCAT`, `_xlfn.IFS`, `_xlfn.SWITCH`, `_xlfn.MAXIFS`, `_xlfn.MINIFS`. Written bare, each yields `#NAME?`.
- **Never use `XLOOKUP`, `XMATCH`, `SORT`, `FILTER`, `UNIQUE`, or `SEQUENCE`.** The runtime's LibreOffice cannot evaluate them under *any* prefix. Newer builds do evaluate them, but they are spilling array functions and an openpyxl-written file has no spill metadata, so only the top-left cell of the range gets a value — and `recalc.py` reports `total_errors: 0` on the truncated result. Use `INDEX`/`MATCH` for lookups, and sort, filter, and de-duplicate in Python before writing the cells.
- A formula LibreOffice could not parse is written back **lowercased** — a quick tell beside a `#NAME?`.
## openpyxl gotchas
- **Reading a model takes two loads.** `data_only=True` yields cached values with the formulas gone; the default yields formula strings with no values. One pass cannot give you both.
- **`data_only=True` is destructive if you save.** That workbook has no formulas left, so saving replaces every one with a literal — permanently.
- **`data_only=True` on a file openpyxl just wrote returns `None` everywhere** — run `recalc.py` first. (A formula whose result is `""` also reads back as `None`.)
- **Merged cells: write the top-left anchor only.** Every other cell in the range is a `MergedCell` whose `.value` is read-only.
- **`.xlsm` loses its macros unless you pass `keep_vba=True`** to `load_workbook`.
- **A sheet name containing a space must be quoted** in a cross-sheet reference: `='Assumptions Inputs'!$B$5`. Unquoted, it evaluates to `#VALUE!`.
## Financial models
### Color Coding Standards
Unless otherwise stated by the user or existing template
Unless the user says otherwise, or the existing file already does something else.
#### Industry-Standard Color Conventions
- **Blue text (RGB: 0,0,255)**: Hardcoded inputs, and numbers users will change for scenarios
- **Black text (RGB: 0,0,0)**: ALL formulas and calculations
- **Green text (RGB: 0,128,0)**: Links pulling from other worksheets within same workbook
- **Red text (RGB: 255,0,0)**: External links to other files
- **Yellow background (RGB: 255,255,0)**: Key assumptions needing attention or cells that need to be updated
**Color:** blue text (`0,0,255`) for hardcoded inputs and scenario levers · black for formulas ·
green (`0,128,0`) for links to another sheet · red (`255,0,0`) for links to another file ·
yellow fill (`255,255,0`) for key assumptions and cells the user should fill in.
### Number Formatting Standards
**Numbers:** currency `$#,##0`, with the unit named in the header (`Revenue ($mm)`) · zeros
render as `-`, including in percentages (`$#,##0;($#,##0);-`) · negatives in parentheses ·
percentages `0.0%`, **stored as fractions** (`0.15` renders `15.0%`; storing `15` renders
`1500.0%`) · valuation multiples `0.0x` · years as text (`"2024"`, never `2,024`).
#### 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
**Structure:** every assumption in its own labeled cell, referenced by the formulas that use it
(`=B5*(1+$B$6)`, never `=B5*1.05`) · formulas consistent across every projection period, since a
lone edited cell mid-row is the commonest silent error · guard denominators that can be zero.
### Formula Construction Rules
## Dependencies
#### Assumptions Placement
- Place ALL assumptions (growth rates, margins, multiples, etc.) in separate assumption cells
- Use cell references instead of hardcoded values in formulas
- Example: Use =B5*(1+$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 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
- 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
`openpyxl`, `pandas`, `markitdown` (pip, preinstalled — install only if an import fails or the command is missing) · LibreOffice (`soffice`, auto-configured for sandboxed environments via `scripts/office/soffice.py`)