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Capital structure optimizer

Evaluates the optimal debt-to-equity mix for a company by modeling how leverage changes affect WACC, EPS, credit rating, and financial flexibility, then recommends a target capital structure range with a clear trade-off…

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Purpose

Determine the debt/equity mix that minimizes the cost of capital while preserving the financial flexibility and credit profile the company actually needs — and translate that into a defensible target leverage range with quantified trade-offs. Replaces gut-feel leverage decisions and one-number "optimal WACC" answers with a range that accounts for rating thresholds, EPS sensitivity, and downside scenarios.

When to Use

Invoke this skill when the user asks to:

  • Decide how much debt to take on or pay down
  • Evaluate a recapitalization, dividend recap, or share buyback funded by debt
  • Pressure-test the balance sheet before an acquisition or major capex program
  • Prepare for a credit rating review or refinancing and assess headroom
  • Compare leverage against peers and decide on a target leverage ratio
  • Quantify the WACC and EPS impact of shifting the debt/equity mix

Inputs Required

Before starting, collect:

  • Current capital structure: Total debt, cash, market cap (or equity value), and any preferred/hybrid instruments
  • Income statement basics: Revenue, EBITDA, EBIT, net income, interest expense, diluted share count, effective tax rate
  • Credit metrics: Current credit rating (or implied), key covenants, and debt maturity profile
  • Cost inputs: Current cost of debt (blended interest rate), equity beta or estimated cost of equity, and risk-free rate if available
  • Strategic objective: What the company is solving for — lower WACC, fund growth, return capital, defend a rating, or maximize flexibility
  • Cash flow profile: Free cash flow trend, cyclicality, and capex intensity
  • Constraints: Target rating to maintain, maximum acceptable leverage, dividend commitments, or board-set guardrails

If cost of equity is not provided, estimate it via CAPM using a reasonable equity risk premium (5–6%) and note the assumption. If rating is unknown, infer an implied rating from leverage and coverage metrics.

Instructions

Step 1: Baseline the Current Structure

Establish the starting point before modeling any change:

  • Compute current net debt / EBITDA, EBITDA / interest (coverage), and debt / total capital (book and market)
  • Calculate current WACC: WACC = (E/V × Ke) + (D/V × Kd × (1 − tax rate))
  • Record current diluted EPS and the implied or stated credit rating
  • Flag where the company sits relative to its stated constraints and target rating

Step 2: Define the Leverage Scenarios

Model a structured set of scenarios rather than a single point:

  • Build at least 5 leverage scenarios spanning conservative to aggressive — e.g., net debt/EBITDA at roughly 0.5x, 1.5x, 2.5x, 3.5x, and 4.5x (calibrate the range to the company's sector norms)
  • For each scenario, derive the resulting debt and equity weights, incremental debt raised or repaid, and the assumed use of proceeds (buyback, debt paydown, growth)

Step 3: Model the Cost of Debt Curve

Cost of debt is not constant across leverage — step it up as risk rises:

  • Map each leverage scenario to a likely credit rating band using standard rating thresholds (e.g., investment grade typically below ~3.0–3.5x net debt/EBITDA with coverage above ~4x)
  • Assign a cost of debt to each rating band, widening the spread as leverage increases and the rating deteriorates
  • Note the rating cliff — the leverage level where the company crosses from investment grade to sub-investment grade, which materially repriced both debt and access to capital

Step 4: Model the Cost of Equity Response

As leverage rises, equity gets riskier:

  • Re-lever beta for each scenario using the Hamada relationship: βL = βU × (1 + (1 − tax) × D/E)
  • Recompute cost of equity (Ke) at each scenario via CAPM
  • This ensures WACC is not artificially minimized by ignoring the equity risk premium that leverage adds

Step 5: Calculate WACC, EPS, and Credit Impact Per Scenario

For each leverage scenario, produce:

  • WACC with the re-levered Ke and the rating-adjusted Kd
  • Diluted EPS — reflecting new interest expense (after tax) and any change in share count from buybacks or issuance
  • Projected credit rating and key metrics (net debt/EBITDA, interest coverage)
  • Financial flexibility score — qualitative High/Medium/Low based on covenant headroom, maturity concentration, and FCF coverage of debt service

Step 6: Identify the Optimal Range

Find the structure that balances the objective against risk:

  • Locate the WACC-minimizing scenario — but do not automatically recommend it if it crosses the rating cliff or eliminates flexibility
  • Define a target range (not a single number), typically a band of net debt/EBITDA where WACC is near its minimum, the desired rating holds, and FCF comfortably covers debt service through a downside case
  • Stress the recommended range against a downside scenario (e.g., EBITDA −20%) to confirm covenants and coverage still hold

Step 7: Build the Trade-Off Summary

Make the tension explicit. Summarize what is gained and given up moving across the range:

  • Lower WACC and EPS accretion (benefit of more debt) vs. rating risk, refinancing risk, and lost flexibility (cost of more debt)
  • State the single recommended target leverage range and the rationale, written for a board or finance committee

Output Format

  • Structure: Narrative diagnosis, a scenario comparison table, and a final recommendation with trade-off summary
  • Length: 900–1,300 words
  • Format: Prose for analysis and recommendation; a scenario table comparing all leverage cases across WACC, EPS, rating, coverage, and flexibility
  • Sections: Current Structure Baseline → Scenario Comparison Table → WACC & Rating Analysis → EPS Impact → Recommended Target Range → Trade-Off Summary → Key Assumptions
  • Tone: CFO-to-board — quantitative, decisive, and explicit about assumptions and downside risk

Quality Checklist

Before delivering output, verify:

  • [ ] At least 5 leverage scenarios are modeled with distinct net debt/EBITDA levels
  • [ ] Cost of debt rises across scenarios and the investment-grade rating cliff is explicitly identified
  • [ ] Cost of equity is re-levered per scenario (beta does not stay constant as leverage changes)
  • [ ] The recommendation is a range, not a single point, and is justified against a downside EBITDA case
  • [ ] The scenario table reports WACC, EPS, rating, coverage, and flexibility for every case
  • [ ] Every non-provided input (cost of equity, ERP, implied rating) is flagged as an assumption

Notes

  • The WACC-minimizing structure is rarely the right recommendation on its own — flexibility and rating headroom routinely justify carrying less debt than the math suggests, especially for cyclical businesses.
  • The rating cliff between investment grade and high yield is the most consequential threshold; the cost-of-capital and market-access penalty for crossing it usually outweighs marginal WACC gains.
  • Anchor the leverage range to sector norms — what is conservative for a regulated utility is aggressive for a software company.
  • If the strategic objective is funding growth or an acquisition, weight financial flexibility higher; if it is returning capital in a stable business, a tighter, more leveraged range is defensible.

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

  • ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
  • <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills