
Excel Author
FreeCreate auditable financial workbooks headless with Python.
Free · Opens the source repo
What Excel Author does
Excel Author is a Python-based skill designed to automate the generation of .xlsx financial workbooks using the openpyxl library. This skill adheres to strict banker-grade conventions, ensuring that the resulting spreadsheets are not only functional but also auditable and flexible. By following these conventions, users can create financial models that are easily reviewable by others, enhancing collaboration and reducing errors in financial reporting.
The skill emphasizes the importance of using formulas instead of hardcoded values, ensuring that all calculations are dynamic and responsive to changes in input data. This approach minimizes the risk of silent bugs that can arise from hardcoded numbers, making financial models more reliable. Additionally, the use of named ranges for cross-sheet references simplifies the management of complex models, allowing users to maintain clarity and accuracy in their calculations.
Another key feature of Excel Author is the inclusion of a balance checks tab, which automatically verifies the integrity of financial statements. This tab ensures that balance sheets and cash flows tie together correctly, providing an immediate check on the model's accuracy. Furthermore, every hardcoded input is accompanied by a comment detailing its source, promoting transparency and accountability in financial modeling.
Excel Author is ideal for financial analysts, accountants, and anyone involved in creating detailed financial models. It streamlines the process of workbook creation while adhering to best practices in financial modeling, making it a valuable tool for professionals who require precision and clarity in their work.
When to use it
Use Excel Author when you need to generate financial models programmatically while ensuring they are auditable and maintainable.
When not to use it
This skill is not suitable for simple spreadsheet tasks or for users who do not require adherence to strict financial modeling conventions.
What you can build with it
Automating Financial Reporting
Use Excel Author to automate the generation of monthly financial reports, ensuring compliance with auditing standards.
Creating Sensitivity Analyses
Generate sensitivity analysis tables quickly, allowing for dynamic adjustments to key assumptions in your financial models.
Collaborative Financial Modeling
Facilitate collaboration among team members by producing clear, auditable financial models that can be easily reviewed and modified.
How to install Excel Author
View source1. Install with the skills CLI
npx skills add nousresearch/hermes-agent/excel-author --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by nousresearchexcel-author
Produce an .xlsx file on disk using openpyxl. Follow the banker-grade conventions below so the model is auditable, flexible, and reviewable by someone other than the person who built it.
Adapted from Anthropic's xlsx-author and audit-xls skills in the anthropics/financial-services repo. The MCP / Office-JS / Cowork-specific branches of the originals are dropped — this skill assumes headless Python.
Output contract
- Write to
./out/<name>.xlsx. Create./out/if it does not exist. - Return the relative path in your final message so downstream tools can pick it up.
- One logical model per file. Do not append to an existing workbook unless explicitly asked.
Setup
pip install "openpyxl>=3.0"
Core conventions (non-negotiable)
Blue / black / green cell color
- Blue (
Font(color="0000FF")) — hardcoded input a human entered. Revenue drivers, WACC inputs, terminal growth, market data. - Black (default) — formula. Every derived cell is a live Excel formula.
- Green (
Font(color="006100")) — link to another sheet or external file.
A reviewer can then scan the sheet and immediately see what's an assumption vs. what's computed.
Formulas over hardcodes
Every calculation cell MUST be a formula string, never a number computed in Python and pasted as a value.
# WRONG — silent bug waiting to happen
ws["D20"] = revenue_prior_year * (1 + growth)
# CORRECT — flexes when the user changes the assumption
ws["D20"] = "=D19*(1+$B$8)"
The only hardcoded numbers permitted:
- Raw historical inputs (actual revenues, reported EBITDA, etc.)
- Assumption drivers the user is meant to flex (growth rates, WACC inputs, terminal g)
- Current market data (share price, debt balance) — with a cell comment documenting source + date
If you catch yourself computing a value in Python and writing the result, stop.
Named ranges for cross-sheet references
Use named ranges for any figure referenced from another sheet, a deck, or a memo.
from openpyxl.workbook.defined_name import DefinedName
wb.defined_names["WACC"] = DefinedName("WACC", attr_text="Inputs!$C$8")
# then elsewhere:
calc["D30"] = "=D29/WACC"
Balance checks tab
Include a Checks tab that ties everything and surfaces TRUE/FALSE:
- Balance sheet balances (assets = liabilities + equity)
- Cash flow ties to period-over-period cash change on the BS
- Sum-of-parts ties to consolidated totals
- No rogue hardcodes inside calc ranges
Example:
checks = wb.create_sheet("Checks")
checks["A2"] = "BS balances"
checks["B2"] = "=IS!D20-IS!D21-IS!D22"
checks["C2"] = "=ABS(B2)<0.01" # TRUE/FALSE
Cell comments on every hardcoded input
Add the comment AS you create the cell, not later.
from openpyxl.comments import Comment
ws["C2"] = 1_250_000_000
ws["C2"].font = Font(color="0000FF")
ws["C2"].comment = Comment("Source: 10-K FY2024, p.47, revenue line", "analyst")
Format: Source: [System/Document], [Date], [Reference], [URL if applicable].
Never defer sourcing. Never write TODO: add source.
Skeleton: typical financial model
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.comments import Comment
from openpyxl.utils import get_column_letter
from pathlib import Path
BLUE = Font(color="0000FF")
BLACK = Font(color="000000")
GREEN = Font(color="006100")
BOLD = Font(bold=True)
HEADER_FILL = PatternFill("solid", fgColor="1F4E79")
HEADER_FONT = Font(color="FFFFFF", bold=True)
wb = Workbook()
# --- Inputs tab ---
inp = wb.active
inp.title = "Inputs"
inp["A1"] = "MARKET DATA & KEY INPUTS"
inp["A1"].font = HEADER_FONT
inp["A1"].fill = HEADER_FILL
inp.merge_cells("A1:C1")
inp["B3"] = "Revenue FY2024"
inp["C3"] = 1_250_000_000
inp["C3"].font = BLUE
inp["C3"].comment = Comment("Source: 10-K FY2024 p.47", "model")
inp["B4"] = "Growth Rate"
inp["C4"] = 0.12
inp["C4"].font = BLUE
# --- Calc tab ---
calc = wb.create_sheet("DCF")
calc["B2"] = "Projected Revenue"
calc["C2"] = "=Inputs!C3*(1+Inputs!C4)" # formula, black
# --- Checks tab ---
chk = wb.create_sheet("Checks")
chk["A2"] = "BS balances"
chk["B2"] = "=ABS(BS!D20-BS!D21-BS!D22)<0.01"
Path("./out").mkdir(exist_ok=True)
wb.save("./out/model.xlsx")
Section headers with merged cells
openpyxl quirk: when you merge, set the value on the top-left cell and style the full range separately.
ws["A7"] = "CASH FLOW PROJECTION"
ws["A7"].font = HEADER_FONT
ws.merge_cells("A7:H7")
for col in range(1, 9): # A..H
ws.cell(row=7, column=col).fill = HEADER_FILL
Sensitivity tables
Build with loops, not hardcoded formulas per cell. Rules:
- Odd number of rows/cols (5×5 or 7×7) — guarantees a true center cell.
- Center cell = base case. The middle row/col header must equal the model's actual WACC and terminal g so the center output equals the base-case implied share price. That's the sanity check.
- Highlight the center cell with medium-blue fill (
"BDD7EE") and bold. - Populate every cell with a full recalculation formula — never an approximation.
# 5x5 WACC (rows) x terminal growth (cols) sensitivity
wacc_axis = [0.08, 0.085, 0.09, 0.095, 0.10] # center row = base 9.0%
term_axis = [0.02, 0.025, 0.03, 0.035, 0.04] # center col = base 3.0%
start_row = 40
ws.cell(row=start_row, column=1).value = "Implied Share Price ($)"
ws.cell(row=start_row, column=1).font = BOLD
for j, g in enumerate(term_axis):
ws.cell(row=start_row+1, column=2+j).value = g
ws.cell(row=start_row+1, column=2+j).font = BLUE
for i, w in enumerate(wacc_axis):
r = start_row + 2 + i
ws.cell(row=r, column=1).value = w
ws.cell(row=r, column=1).font = BLUE
for j, g in enumerate(term_axis):
c = 2 + j
# Full DCF recalc formula (simplified for illustration).
# In a real model this references the full projection block.
ws.cell(row=r, column=c).value = (
f"=SUMPRODUCT(FCF_range,1/(1+{w})^year_offset) + "
f"FCF_terminal*(1+{g})/({w}-{g})/(1+{w})^terminal_year"
)
# Highlight center cell (base case)
center = ws.cell(row=start_row+2+len(wacc_axis)//2,
column=2+len(term_axis)//2)
center.fill = PatternFill("solid", fgColor="BDD7EE")
center.font = BOLD
Recalculating before delivery
openpyxl writes formula strings but does not compute them. Excel recalculates on open, but downstream consumers (auto-check scripts, CI) need computed values.
Run LibreOffice or a dedicated recalc step before delivery:
# LibreOffice headless recalc
libreoffice --headless --calc --convert-to xlsx ./out/model.xlsx --outdir ./out/
Or use a Python recalc helper (see scripts/recalc.py in this skill).
Model layout planning
Before writing any formula:
- Define ALL section row positions
- Write ALL headers and labels
- Write ALL section dividers and blank rows
- THEN write formulas using the locked row positions
This prevents the cascading-formula-breakage pattern where inserting a header row after formulas are written shifts every downstream reference.
Verify step-by-step with the user
For large models (DCFs, 3-statement, LBO), stop and show the user intermediate artifacts before continuing. Catching a wrong margin assumption before you've built downstream sensitivity tables saves an hour.
Checkpoint pattern:
- After Inputs block → show raw inputs, confirm before projecting
- After Revenue projections → confirm top line + growth
- After FCF build → confirm the full schedule
- After WACC → confirm inputs
- After valuation → confirm the equity bridge
- THEN build sensitivity tables
When NOT to use this skill
- Users in a live Excel session with an Office MCP available — drive their live workbook instead.
- Pure tabular data export with no formulas —
csvorpandas.to_excelis simpler. - Dashboards / charts with heavy interactivity — use a real BI tool.
Attribution
Conventions (blue/black/green, formulas-over-hardcodes, named ranges, sensitivity rules) adapted from Anthropic's Claude for Financial Services plugin suite, Apache-2.0 licensed. Original: https://github.com/anthropics/financial-services/tree/main/plugins/vertical-plugins/financial-analysis/skills/xlsx-author
Frequently asked questions about Excel Author
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