Apply the Modigliani-Miller theorem to analyze capital structure decisions and identify when financing choices affect firm value. Use this skill when the user needs to evaluate debt-equity tradeoffs, assess the impact of leverage on firm value, understand tax shield benefits, or when they ask 'does capital structure matter', 'should we take on more debt', or 'what is the optimal leverage ratio'.
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-mm-theorem
The Modigliani-Miller theorem (1958) establishes that in perfect capital markets, firm value is independent of capital structure. This irrelevance result serves as the benchmark — every real-world reason capital structure matters is a violation of MM's assumptions.
IRON LAW: MM irrelevance holds ONLY in perfect markets — every
real-world deviation (taxes, bankruptcy costs, agency costs) makes
capital structure matter. MM is the null hypothesis, not the answer.
Key assumptions (for irrelevance):
For each deviation, assess its magnitude:
Optimal capital structure balances marginal tax shield benefit against marginal bankruptcy and agency costs.
WACC = (E/V)Re + (D/V)Rd(1-Tc). Optimal structure minimizes WACC.
## Capital Structure Analysis: [Firm]
### Current Structure
| Metric | Value |
|--------|-------|
| Debt (D) | $X |
| Equity (E) | $X |
| D/E Ratio | x.xx |
| WACC | x% |
### MM Imperfections Present
| Imperfection | Magnitude | Direction |
|-------------|-----------|-----------|
| Tax shield | [high/medium/low] | Favors debt |
| Bankruptcy costs | [high/medium/low] | Favors equity |
| Agency costs | [high/medium/low] | [depends] |
### Recommendation
- [Optimal direction of adjustment with reasoning]
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Take asgard-ai-platform/grad-mm-theorem from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.