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Returns Analysis

OrganizationPopular
anthropics
returns-analysis

Build quick IRR/MOIC sensitivity tables for PE deal evaluation. Models returns across entry multiple, leverage, exit multiple, growth, and hold period scenarios. Use when sizing up a deal, stress-testing assumptions, or preparing IC returns exhibits. Triggers on "returns analysis", "IRR sensitivity", "MOIC table", "what's the return at", "model the returns", or "back of the envelope".

Overview

Publisheranthropics
Repositoryfinancial-services
Skill namereturns-analysis
Stars
38.4K
Forks
5.5K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Returns Analysis 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/anthropics/financial-services/tree/main/plugins/agent-plugins/valuation-reviewer/skills/returns-analysis
  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/anthropics/financial-services.git /tmp/financial-services
mkdir -p .claude/skills
cp -r /tmp/financial-services/plugins/agent-plugins/valuation-reviewer/skills/returns-analysis .claude/skills/returns-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Returns Analysis 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 Returns Analysis 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 Returns Analysis 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.

Returns Analysis

Workflow

Step 1: Gather Deal Inputs

Ask for (or extract from prior analysis):

Entry:

  • Entry EBITDA (LTM or NTM)
  • Entry multiple (EV / EBITDA)
  • Enterprise value
  • Net debt at close
  • Equity check size
  • Transaction fees & expenses

Financing:

  • Senior debt (x EBITDA, rate, amortization)
  • Subordinated debt / mezzanine (if any)
  • Total leverage at entry (x EBITDA)
  • Equity contribution

Operating Assumptions:

  • Revenue growth rate (annual)
  • EBITDA margin trajectory
  • Capex as % of revenue
  • Working capital changes
  • Debt paydown schedule

Exit:

  • Hold period (years)
  • Exit multiple (EV / EBITDA)
  • Exit EBITDA (calculated from growth assumptions)

Step 2: Base Case Returns

Calculate:

MetricValue
Entry EV
Equity invested
Exit EBITDA
Exit EV
Net debt at exit
Exit equity value
MOIC
IRR
Cash-on-cash

Show the returns waterfall:

  • EBITDA growth contribution
  • Multiple expansion/contraction contribution
  • Debt paydown contribution
  • Fee/expense drag

Step 3: Sensitivity Tables

Build 2-way sensitivity matrices:

Entry Multiple vs. Exit Multiple

Exit 6xExit 7xExit 8xExit 9xExit 10x
Entry 7x
Entry 8x
Entry 9x
Entry 10x

EBITDA Growth vs. Exit Multiple (at fixed entry)

Leverage vs. Exit Multiple (at fixed entry and growth)

Hold Period vs. Exit Multiple

Show both IRR and MOIC in each cell (IRR / MOIC format).

Step 4: Scenario Analysis

Build 3 scenarios:

BullBaseBear
Revenue CAGR
Exit EBITDA margin
Exit multiple
Exit EBITDA
MOIC
IRR

Step 5: Output

  • Excel workbook with:
    • Assumptions tab
    • Returns calculation
    • Sensitivity tables (formatted with conditional coloring)
    • Scenario summary
  • One-page returns summary suitable for IC deck

Key Formulas

  • MOIC = Exit Equity Value / Equity Invested
  • IRR = solve for r: Equity Invested × (1 + r)^n = Exit Equity Value (adjust for interim cash flows)
  • Returns attribution:
    • Growth: (Exit EBITDA - Entry EBITDA) × Exit Multiple / Equity
    • Multiple: (Exit Multiple - Entry Multiple) × Entry EBITDA / Equity
    • Leverage: Debt paydown over hold period / Equity

Important Notes

  • Always show returns both gross and net of fees/carry where applicable
  • Management rollover and co-invest change the equity check — ask if relevant
  • Dividend recaps or interim distributions affect IRR significantly — include if planned
  • Don't forget transaction costs (typically 2-4% of EV) — they reduce Day 1 equity value
  • Tax considerations (asset vs. stock deal, 338(h)(10) election) can materially affect after-tax returns

Frequently asked questions

What does the Returns Analysis AI skill do?

Build quick IRR/MOIC sensitivity tables for PE deal evaluation. Models returns across entry multiple, leverage, exit multiple, growth, and hold period scenarios. Use when sizing up a deal, stress-testing assumptions, or preparing IC returns exhibits. Triggers on "returns analysis", "IRR sensitivity", "MOIC table", "what's the return at", "model the returns", or "back of the envelope".

Why use Returns Analysis on TypingMind?

Because you install it once and use it with any model. Returns Analysis 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 Returns Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/financial-services/tree/main/plugins/agent-plugins/valuation-reviewer/skills/returns-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Returns Analysis?

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 Returns Analysis?

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

Is the Returns Analysis AI skill free?

Yes. It is published on GitHub by anthropics 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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