Build integrated IS/BS/CF financial workbooks in Excel.
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Build auditable financial workbooks headless via openpyxl.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
./out/<name>.xlsx. Create ./out/ if it does not exist.pip install "openpyxl>=3.0"
Font(color="0000FF")) — hardcoded input a human entered. Revenue drivers, WACC inputs, terminal growth, market data.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.
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$8)"
The only hardcoded numbers permitted:
If you catch yourself computing a value in Python and writing the result, stop.
Use named ranges for any figure referenced from another sheet, a deck, or a memo.
from openpyxl.workbook.defined_name import DefinedName
wb.defined_names["WACC"] = DefinedName("WACC", attr_text="Inputs!$C$8")
# then elsewhere:
calc["D30"] = "=D29/WACC"
Include a Checks tab that ties everything and surfaces TRUE/FALSE:
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
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.
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")
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
Build with loops, not hardcoded formulas per cell. Rules:
"BDD7EE") and bold.# 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
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).
Before writing any formula:
This prevents the cascading-formula-breakage pattern where inserting a header row after formulas are written shifts every downstream reference.
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:
csv or pandas.to_excel is simpler.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
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Build integrated IS/BS/CF financial workbooks in Excel.
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Run and grow the OpenAmer Agent-to-Agent swarm: identity, trust, node-to-node ask, signed skill/insight sharing, and the autonomous self-learning loop.
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Use for A/B experiments on OpenAmer configs and skills.
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Roleplay a hostile user to find and triage UX pain points.
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Agent mesh: master/worker nodes, HTTP delegation, heartbeat.
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Use for agentic commerce payments tasks. Candidate synthesized from trend radar; awaiting trial promotion.
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