Excel Author logo

Excel Author

OrganizationPopular
NousResearch
excel-author

Build auditable financial workbooks headless via openpyxl.

Overview

PublisherNousResearch
Repositoryhermes-agent
Skill nameexcel-author
Stars
250.4K
Forks
53.5K
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NousResearch on GitHub. Read the source before you install it.

Installation

Install the Excel Author AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NousResearch/hermes-agent/tree/main/optional-skills/finance/excel-author
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NousResearch/hermes-agent.git /tmp/hermes-agent
mkdir -p .claude/skills
cp -r /tmp/hermes-agent/optional-skills/finance/excel-author .claude/skills/excel-author
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Excel Author in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Excel Author on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Excel Author is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

excel-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

bash
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.

python
# 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:

  1. Raw historical inputs (actual revenues, reported EBITDA, etc.)
  2. Assumption drivers the user is meant to flex (growth rates, WACC inputs, terminal g)
  3. 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.

python
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:

python
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.

python
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

python
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.

python
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.
python
# 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:

bash
# 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:

  1. Define ALL section row positions
  2. Write ALL headers and labels
  3. Write ALL section dividers and blank rows
  4. 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 — csv or pandas.to_excel is 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Excel Author AI skill do?

Build auditable financial workbooks headless via openpyxl.

Why use Excel Author on TypingMind?

Because you install it once and use it with any model. Excel Author is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Excel Author in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NousResearch/hermes-agent/tree/main/optional-skills/finance/excel-author. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Excel Author?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Excel Author?

As many as you like. As long as a model supports skills, you can use Excel Author with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Excel Author AI skill free?

Yes. It is published on GitHub by NousResearch under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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