Build leveraged buyout workbooks with IRR/MOIC in Excel. An agent skill from Luciole-Studio/Misaka-Agent.

Apache-2.0Auto-check passedDocuments & Office

Install Lbo Model

skills CLI
$ npx skills add Luciole-Studio/Misaka-Agent --skill lbo-model -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Luciole-Studio/Misaka-Agent lbo-model --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Luciole-Studio/Misaka-Agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/misaka/core/skills/assets/optional/finance/lbo-model .claude/skills/lbo-model && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
lbo-model
GitHub stars
158
Used in
2 other repos
Token cost
~4.2k tokens
SKILL.md length
2,165 words
Files
1
Skills in repo
77
Repo updated
First seen
Licence
Apache-2.0

At a glance

Build leveraged buyout workbooks with IRR/MOIC in Excel. An agent skill from Luciole-Studio/Misaka-Agent.

  • Works in 3 steps: Check the Template → Check the User's Instructions → Apply Standard Practice
  • Tasks that involve Excel spreadsheets
  • SKILL.md covers Environment, TEMPLATE REQUIREMENT, CRITICAL INSTRUCTIONS — READ… and TEMPLATE ANALYSIS PHASE - DO…, plus 3 more sections
  • Calls python; reaches sec.gov

What it does

Lbo Model is an agent skill from Luciole-Studio/Misaka-Agent. Build leveraged buyout workbooks with IRR/MOIC in Excel.

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Documents & Office, covering Excel spreadsheets. It works with Microsoft Excel, Python and openpyxl. The repository describes itself as: A multi-agent research system for the humanities and social sciences. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Excel spreadsheets

Example prompts

  • “/lbo-model”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Check the Template
  2. Check the User's Instructions
  3. Apply Standard Practice

What it can do on your machine

Read from SKILL.md and the folder at commit 3bcf7a3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • sec.gov

    Also links to:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Lbo Model loads about 4.2k tokens when it runs. Until then it costs about 17 tokens; SKILL.md has 2,165 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~17
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Luciole-Studio/Misaka-Agent at commit 3bcf7a3, republished under its Apache-2.0 licence (© Luciole-Studio). 2,165 words, ~4,210 tokens.

Download SKILL.mdSave it as .claude/skills/lbo-model/SKILL.md (or your agent's skills folder).
name
lbo-model
description
Build leveraged buyout workbooks with IRR/MOIC in Excel.
version
1.0.0
author
Anthropic (adapted by Nous Research)
license
Apache-2.0
platforms
linux, macos, windows

Environment

This skill assumes headless openpyxl — you are producing an .xlsx file on disk. Follow the excel-author skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables. Recalculate before delivery: python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx.


TEMPLATE REQUIREMENT

This skill uses templates for LBO models. Always check for an attached template file first.

Before starting any LBO model:

  1. If a template file is attached/provided: Use that template's structure exactly - copy it and populate with the user's data
  2. If no template is attached: Ask the user: "Do you have a specific LBO template you'd like me to use? If not, I can use the standard template which includes Sources & Uses, Operating Model, Debt Schedule, and Returns Analysis."
  3. If using the standard template: Copy examples/LBO_Model.xlsx as your starting point and populate it with the user's assumptions

IMPORTANT: When a file like LBO_Model.xlsx is attached, you MUST use it as your template - do not build from scratch. Even if the template seems complex or has more features than needed, copy it and adapt it to the user's requirements. Never decide to "build from scratch" when a template is provided.


CRITICAL INSTRUCTIONS — READ FIRST

Use Python/openpyxl. Write formula strings (ws["D20"] = "=B5*B6"), then run the excel-author skill's recalc.py helper before delivery.

Core Principles
  • Every calculation must be an Excel formula - NEVER compute values in Python and hardcode results into cells. When using openpyxl, write cell.value = "=B5*B6" (formula string), NOT cell.value = 1250 (computed result). The model must be dynamic and update when inputs change.
  • Use the template structure - Follow the organization in examples/LBO_Model.xlsx or the user's provided template. Do not invent your own layout.
  • Use proper cell references - All formulas should reference the appropriate cells. Never type numbers that should come from other cells.
  • Maintain sign convention consistency - Follow whatever sign convention the template uses (some use negative for outflows, some use positive). Be consistent throughout.
  • Work section by section, verify with user at each step - Complete one section fully, show the user what was built, run the section's verification checks, and get confirmation BEFORE moving to the next section. Do NOT build the entire model end-to-end and then present it — later sections depend on earlier ones, so catching a mistake in Sources & Uses after the returns are already built means rework everywhere.
Formula Color Conventions
  • Blue (0000FF): Hardcoded inputs - typed numbers that don't reference other cells
  • Black (000000): Formulas with calculations - any formula using operators or functions (=B4*B5, =SUM(), =-MAX(0,B4))
  • Purple (800080): Links to cells on the same tab - direct references with no calculation (=B9, =B45)
  • Green (008000): Links to cells on different tabs - cross-sheet references (=Assumptions!B5, ='Operating Model'!C10)
Fill Color Palette — Professional Blues & Greys (Default unless user/template specifies otherwise)
  • Keep it minimal — only use blues and greys for cell fills. Do NOT introduce greens, yellows, reds, or multiple accents. A professional LBO model uses restraint.
  • Default fill palette:
    • Section headers (Sources & Uses, Operating Model, etc.): Dark blue #1F4E79 with white bold text
    • Column headers (Year 1, Year 2, etc.): Light blue #D9E1F2 with black bold text
    • Input cells: Light grey #F2F2F2 (or just white) — the blue font is the signal, fill is secondary
    • Formula/calculated cells: White, no fill
    • Key outputs (IRR, MOIC, Exit Equity): Medium blue #BDD7EE with black bold text
  • That's the whole palette. 3 blues + 1 grey + white. If the template uses its own colors, follow the template instead.
  • Note: The blue/black/purple/green font colors above are for distinguishing inputs vs formulas vs links. Those are separate from the fill palette here — both work together.
Number Formatting Standards
  • Currency: $#,##0;($#,##0);"-" or $#,##0.0 depending on template
  • Percentages: 0.0% (one decimal)
  • Multiples: 0.0"x" (one decimal)
  • MOIC/Detailed Ratios: 0.00"x" (two decimals for precision)
  • All numeric cells: Right-aligned

Clarify Requirements First

Before filling any formulas:

  • Examine the template structure - Identify all sections, understand the timeline (which columns are which periods), note any existing formulas
  • Ask the user if anything is unclear - If the template structure, calculation methods, or requirements are ambiguous, ask before proceeding
  • Confirm key assumptions - Any key inputs, calculation preferences, or specific requirements
  • ONLY AFTER understanding the template, proceed to fill in formulas

TEMPLATE ANALYSIS PHASE - DO THIS FIRST

Before filling any formulas, examine the template thoroughly:

  1. Map the structure - Identify where each section lives and how they relate to each other. Note which sections feed into others.

  2. Understand the timeline - Which columns represent which periods? Is there a "Closing" or "Pro Forma" column? Where does the projection period start?

  3. Identify input vs formula cells - Templates often use color coding, borders, or shading to indicate which cells need inputs vs formulas. Respect these conventions.

  4. Read existing labels carefully - The row labels tell you exactly what calculation is expected. Don't assume - read what the template is asking for.

  5. Check for existing formulas - Some templates come partially filled. Don't overwrite working formulas unless specifically asked.

  6. Note template-specific conventions - Sign conventions, subtotal structures, how sections are organized, whether there are separate tabs for different components, etc.


FILLING FORMULAS - GENERAL APPROACH

For each cell that needs a formula, follow this hierarchy:

Step 1: Check the Template
  • Does the cell already have a formula? If yes, verify it's correct and move on.
  • Is there a comment or note indicating the expected calculation?
  • Does the row/column label make the calculation obvious?
  • Do neighboring cells show a pattern you should follow?
Step 2: Check the User's Instructions
  • Did the user specify a particular calculation method?
  • Are there stated assumptions that affect this formula?
  • Any special requirements mentioned?
Step 3: Apply Standard Practice
  • If neither template nor user specifies, use standard LBO modeling conventions
  • Document any assumptions you make
  • If genuinely uncertain, ask the user

COMMON PROBLEM AREAS

The following calculation patterns frequently cause issues across LBO models. Pay special attention when you encounter these:

Balancing Sections
  • When two sections must equal (e.g., Sources = Uses), one item is typically the "plug" (balancing figure)
  • Identify which item is the plug and calculate it as the difference
Tax Calculations
  • Tax formulas should only reference the relevant income line and tax rate
  • Should NOT reference unrelated sections (e.g., debt schedules)
  • Consider whether losses create tax shields or are simply ignored
Interest and Circular References
  • Interest calculations can create circularity if they reference balances affected by cash flows
  • Use Beginning Balance (not average or ending) to break circular references
  • Pattern: Interest → Cash Flow → Paydown → Ending Balance (if interest uses ending balance, this circles back)
Debt Paydown / Cash Sweeps
  • When multiple debt tranches exist, there's usually a priority order
  • Cash sweep should respect the priority waterfall
  • Balances cannot go negative - use MAX or MIN functions appropriately
Returns Calculations (IRR/MOIC)
  • Cash flows must have correct signs: Investment = negative, Proceeds = positive
  • If using XIRR, need corresponding dates
  • If using IRR, cash flows should be in consecutive periods
  • MOIC = Total Proceeds / Total Investment
Sensitivity Tables
  • Use ODD dimensions (5×5 or 7×7) — never 4×4 or 6×6. Odd dimensions guarantee a true center cell.
  • Center cell = base case. Build the row and column axis values symmetrically around the model's actual assumptions (e.g., if base entry multiple = 10.0x, axis = [8.0x, 9.0x, 10.0x, 11.0x, 12.0x]). The center cell's IRR/MOIC MUST then equal the model's actual IRR/MOIC output — this is the proof the table is wired correctly.
  • Highlight the center cell — medium-blue fill (#BDD7EE) + bold font so the base case is visually anchored.
  • Excel's DATA TABLE function may not work with openpyxl — instead write explicit formulas that reference row/column headers
  • Each cell should show a DIFFERENT value — if all same, formulas aren't varying correctly
  • Use mixed references (e.g., $A5 for row input, B$4 for column input)

VERIFICATION CHECKLIST - RUN AFTER COMPLETION

Run Formula Validation
bash
python /path/to/excel-author/scripts/recalc.py model.xlsx

Must return success with zero errors.

Section Balancing
  • Any sections that must balance (Sources/Uses, Assets/Liabilities) balance exactly
  • Plug items are calculated correctly as the balancing figure
  • Amounts that should match across sections are consistent
Show full SKILL.md (866 more words)Show less
Income/Operating Projections
  • Revenue/top-line builds correctly from drivers or growth rates
  • All cost and expense items calculated appropriately
  • Subtotals and totals sum correctly
  • Margins and ratios are reasonable
  • Links to assumptions are correct
Balance Sheet (if applicable)
  • Assets = Liabilities + Equity (must balance)
  • All items link to appropriate schedules or roll-forwards
  • Beginning balances = prior period ending balances
  • Check row included and shows zero
Cash Flow (if applicable)
  • Starts with correct income figure
  • Non-cash items added/subtracted appropriately
  • Working capital changes have correct signs
  • Ending Cash = Beginning Cash + Net Cash Flow
  • Cash balances are consistent across statements
Supporting Schedules
  • Roll-forward schedules balance (Beginning + Changes = Ending)
  • Schedules link correctly to main statements
  • Calculated items use appropriate drivers
  • All periods are calculated consistently
Debt/Financing Schedules (if applicable)
  • Beginning balances tie to sources or prior period
  • Interest calculated on appropriate balance (typically beginning)
  • Paydowns respect cash availability and priority
  • Ending balances cannot be negative
  • Totals sum tranches correctly
Returns/Output Analysis
  • Exit/terminal values calculated correctly
  • All relevant adjustments included
  • Cash flow signs are correct (negative for investment, positive for proceeds)
  • IRR/MOIC formulas reference complete ranges
  • Results are reasonable for the scenario
Sensitivity Tables (if applicable)
  • Grid dimensions are ODD (5×5 or 7×7) — there is a true center cell
  • Row and column axis values are symmetric around the base case ([base-2Δ, base-Δ, base, base+Δ, base+2Δ])
  • Center cell output equals the model's actual IRR/MOIC — confirms the table is wired correctly
  • Center cell is highlighted (medium-blue fill #BDD7EE, bold font)
  • Row and column headers contain appropriate input values
  • Each data cell contains a formula (not hardcoded)
  • Each data cell shows a DIFFERENT value
  • Values move in expected directions (higher exit multiple → higher IRR, etc.)
Formatting
  • Hardcoded inputs are blue (0000FF)
  • Calculated formulas are black (000000)
  • Same-tab links are purple (800080)
  • Cross-tab links are green (008000)
  • All numbers are right-aligned
  • Appropriate number formats applied throughout
  • No cells show error values (#REF!, #DIV/0!, #VALUE!, #NAME?)
Logical Sanity Checks
  • Numbers are reasonable order of magnitude
  • Trends make sense (growth, decline, stabilization as expected)
  • No obviously wrong values (negative where should be positive, impossible percentages, etc.)
  • Key outputs are within reasonable ranges for the type of analysis

COMMON ERRORS TO AVOID

ErrorWhat Goes WrongHow to Fix
Hardcoding calculated valuesModel doesn't update when inputs changeAlways use formulas that reference source cells
Wrong cell references after copyingFormulas point to wrong cellsVerify all links, use appropriate $ anchoring
Circular reference errorsModel can't calculateUse beginning balances for interest-type calcs, break the circle
Sections don't balanceTotals that should match don'tEnsure one item is the plug (calculated as difference)
Negative balances where impossiblePaying/using more than availableUse MAX(0, ...) or MIN functions appropriately
IRR/return errorsWrong signs or incomplete rangesCheck cash flow signs and ensure formula covers all periods
Sensitivity table shows same valueFormula not varying with inputsCheck cell references - need mixed references ($A5, B$4)
Roll-forwards don't tieBeginning ≠ prior endingVerify links between periods
Inconsistent sign conventionsAdditions become subtractions or vice versaFollow template's convention consistently throughout

WORKING WITH THE USER — SECTION-BY-SECTION CHECKPOINTS

  • If the template structure is unclear, ask before proceeding
  • If the user's requirements conflict with the template, confirm their preference
  • After completing each major section, STOP and verify with the user before continuing:
    • After Sources & Uses → show the balanced table, confirm the plug is correct, get sign-off before building the operating model
    • After Operating Model / Projections → show the projected P&L, confirm growth rates and margins look right, get sign-off before the debt schedule
    • After Debt Schedule → show beginning/ending balances and interest, confirm the waterfall logic, get sign-off before returns
    • After Returns (IRR/MOIC) → show the cash flow series and outputs, confirm signs and ranges, get sign-off before sensitivity tables
    • After Sensitivity Tables → show that each cell varies, confirm the base case lands where expected
  • If errors are found during verification, fix them before moving to the next section
  • Show your work - explain key formulas or assumptions when helpful
  • Never present a completed model without having checked in at each section — it's faster to catch a wrong cell reference at the source than to trace it backwards from a broken IRR

This skill produces investment banking-quality LBO models by filling templates with correct formulas, proper formatting, and validated calculations. The skill adapts to any template structure while ensuring financial accuracy and professional presentation standards.

Data sources — MCP first, web fallback

Many passages below say "use the S&P Kensho MCP / Daloopa MCP / FactSet MCP". Those are commercial financial-data MCPs from the original Cowork plugin context. In Hermes:

  • If you have any structured financial-data MCP configured (Hermes supports MCP — see native-mcp skill), prefer it for point-in-time comps, precedent transactions, and filings.
  • Otherwise, fall back to:
    • web_search / web_extract against SEC EDGAR (https://www.sec.gov/cgi-bin/browse-edgar) for US filings
    • Company IR pages for press releases, earnings decks
    • browser_navigate for interactive data portals
    • User-provided data (explicitly ask when the context doesn't have it)
  • Never fabricate. If a multiple, precedent, or filing number can't be sourced, flag the cell as [UNSOURCED] and surface it to the user.

Attribution

This skill is adapted from Anthropic's Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the excel-author skill's conventions. Original: https://github.com/anthropics/financial-services

© Luciole-Studio, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in misaka/core/skills/assets/optional/finance/lbo-model of Luciole-Studio/Misaka-Agent.

Open the folder on GitHubat commit 3bcf7a3

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in Luciole-Studio/Misaka-Agent, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Lbo Model next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Kimi XLSXthvroyal/kimi-skills238—~9.5kAutomated safety check: PassNone
Office XLSXopen-octo/octo-agent125—~1.8kAutomated safety check: NotesMIT
Cc Streaming Export Safetydoccker/cc-use-exp1.1k—~2.2kAutomated safety check: PassCustom licence
XLSXnexus-research-lab/nexus151—~501Automated safety check: PassApache-2.0

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Questions about Lbo Model

What does Lbo Model do?

Build leveraged buyout workbooks with IRR/MOIC in Excel. An agent skill from Luciole-Studio/Misaka-Agent. Lbo Model is an agent skill from Luciole-Studio/Misaka-Agent. Build leveraged buyout workbooks with IRR/MOIC in Excel.

When should I use Lbo Model?

Lbo Model fits situations like: tasks that involve Excel spreadsheets.

How do I install Lbo Model in Claude Code?

Run `npx skills add Luciole-Studio/Misaka-Agent --skill lbo-model -a claude-code`. Or copy the skill folder (misaka/core/skills/assets/optional/finance/lbo-model in Luciole-Studio/Misaka-Agent) into .claude/skills/lbo-model in your project. Claude Code loads it when a task matches its description.

How do I install Lbo Model in Codex?

Run `npx skills add Luciole-Studio/Misaka-Agent --skill lbo-model -a codex`. Or copy the skill folder (misaka/core/skills/assets/optional/finance/lbo-model in Luciole-Studio/Misaka-Agent) into .agents/skills/lbo-model in your project. Codex loads it when a task matches its description.

Can I use Lbo Model in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Luciole-Studio/Misaka-Agent --skill lbo-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lbo-model, .gemini/skills/lbo-model, .github/skills/lbo-model and .opencode/skills/lbo-model in your project.

What does Lbo Model need to run?

Going by SKILL.md and its folder, Lbo Model needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Lbo Model access the network?

SKILL.md names 2 domains. In commands or code: sec.gov; the agent is likely to contact it when it follows the instructions. As links in the text: github.com. This is read from the text; nothing was executed.

Is Lbo Model safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Lbo Model use?

Lbo Model is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Lbo Model use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Lbo Model?

Skills that share tags, products or a category with Lbo Model: Excel Workbook Editor (TokenRhythm/opensquilla, 7.1k stars), Kimi XLSX (thvroyal/kimi-skills, 238 stars), Office XLSX (open-octo/octo-agent, 125 stars) and Cc Streaming Export Safety (doccker/cc-use-exp, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Lbo Model?

Luciole-Studio (a GitHub organization) maintains it in Luciole-Studio/Misaka-Agent, which has 158 GitHub stars. The repository holds 77 skills in this directory. The repository was last updated on October 8, 2026.

Source: Luciole-Studio/Misaka-Agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.