Skill Market

merger model

Build accretion/dilution (merger) models in Excel — pro-forma P&L, synergies, financing mix, EPS impact. Pairs with excel-author. Use for M&A pitches, board materials, or deal evaluation.

GitHub
githubcommunityaiai-agentai-agentsanthropicchatgptclaude
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Skill Introduction

Overview
Build accretion/dilution (merger) models in Excel — pro-forma P&L, synergies, financing mix, EPS impact. Pairs with excel-author. Use for M&A pitches, board materials, or deal evaluation.

Core value

Turns reusable Development know-how into an installable skill, helping users complete github, community, ai, ai-agent work faster.

Target users

  • Developers, testers, and maintainers who handle Development tasks in Focus Code.
  • Teams that already trust workflows or content from NousResearch.
  • Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

  • Read the skill content first to confirm required inputs, expected outputs, and dependencies.
  • Try it on a small task before relying on it for critical work.
  • Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
  • For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

  • Tasks related to github, community, ai, ai-agent that need a reusable execution flow.
  • Converting a community repo, team convention, or personal workflow into day-to-day assistance.
  • Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

  • Results depend on the quality of the original skill content and may need human correction.
  • It does not replace code review, tests, security review, or professional judgment.
  • External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

  • Structured around Development, making it easier to discover and reuse than loose prompt snippets.
  • Marked as GitHub, which helps users judge trust and maintenance expectations.
  • Keeps the original source link available for repository, documentation, or discussion follow-up.
  • Tagged with github, community, ai, ai-agent, so it can be filtered by concrete task intent.

Install and use

Install
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focus install merger-model-940beb
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SKILL.md

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name: merger-model description: Build accretion/dilution (merger) models in Excel — pro-forma P&L, synergies, financing mix, EPS impact. Pairs with excel-author. Use for M&A pitches, board materials, or deal evaluation. version: 1.0.0 author: Anthropic (adapted by Nous Research) license: Apache-2.0 platforms: [linux, macos, windows] metadata: hermes: tags: [finance, m-and-a, merger, accretion-dilution, excel, openpyxl, modeling, investment-banking] related_skills: [excel-author, pptx-author, dcf-model, 3-statement-model]

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.

Merger Model

Build accretion/dilution analysis for M&A transactions. Models pro forma EPS impact, synergy sensitivities, and purchase price allocation. Use when evaluating a potential acquisition, preparing merger consequences analysis for a pitch, or advising on deal terms.

Workflow

Step 1: Gather Inputs

Acquirer:

  • Company name, current share price, shares outstanding
  • LTM and NTM EPS (GAAP and adjusted)
  • P/E multiple
  • Pre-tax cost of debt, tax rate
  • Cash on balance sheet, existing debt

Target:

  • Company name, current share price, shares outstanding (if public)
  • LTM and NTM EPS or net income
  • Enterprise value or equity value

Deal Terms:

  • Offer price per share (or premium to current)
  • Consideration mix: % cash vs. % stock
  • New debt raised to fund cash portion
  • Expected synergies (revenue and cost) and phase-in timeline
  • Transaction fees and financing costs
  • Expected close date

Step 2: Purchase Price Analysis

ItemValue
Offer price per share
Premium to current
Equity value
Plus: net debt assumed
Enterprise value
EV / EBITDA implied
P/E implied

Step 3: Sources & Uses

Sources$Uses$
New debtEquity purchase price
Cash on handRefinance target debt
New equity issuedTransaction fees
Financing fees
TotalTotal

Step 4: Pro Forma EPS (Accretion / Dilution)

Calculate year-by-year (Year 1-3):

StandalonePro FormaAccretion/(Dilution)
Acquirer net income
Target net income
Synergies (after tax)
Foregone interest on cash (after tax)
New debt interest (after tax)
Intangible amortization (after tax)
Pro forma net income
Pro forma shares
Pro forma EPS
Accretion / (Dilution) %

Step 5: Sensitivity Analysis

Accretion/Dilution vs. Synergies and Offer Premium:

$0M syn$25M syn$50M syn$75M syn$100M syn
15% premium
20% premium
25% premium
30% premium

Accretion/Dilution vs. Cash/Stock Mix:

100% cash75/2550/5025/75100% stock
Year 1
Year 2

Step 6: Breakeven Synergies

Calculate the minimum synergies needed for the deal to be EPS-neutral in Year 1.

Step 7: Output

  • Excel workbook with:
    • Assumptions tab
    • Sources & uses
    • Pro forma income statement
    • Accretion/dilution summary
    • Sensitivity tables
    • Breakeven analysis
  • One-page merger consequences summary for pitch book

Important Notes

  • Always show both GAAP and adjusted (cash) EPS where relevant
  • Stock deals: use acquirer's current price for exchange ratio, note dilution from new shares
  • Include purchase price allocation — goodwill and intangible amortization matter for GAAP EPS
  • Synergy phase-in is critical — Year 1 is often only 25-50% of run-rate synergies
  • Don't forget foregone interest income on cash used and new interest expense on debt raised
  • Tax rate on synergies and interest adjustments should match the acquirer's marginal rate

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

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