# company-valuation

Estimates a public company's intrinsic value using DCF, peer multiples, and SOTP. The skill generates a WACC sensitivity grid and bull, base, or bear price targets.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3200 installs · ★ 3453
- Author: himself65
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/himself65/finance-skills/tree/HEAD/plugins/market-analysis/skills/company-valuation/SKILL.md
- Updated: 2026-10-25
- HTML: https://affpapa.org/en/nevibecode/skills/company-valuation

## What it does

Instead of relying on LLM memory, this skill executes Python models to evaluate equity value. It triangulates fair value using three approaches: a 5-year discounted cash flow with WACC discounting, peer valuation against 4–6 competitors using median EV/EBITDA, P/E, or EV/Revenue, and sum-of-the-parts for multi-segment businesses. The output includes a 5×5 sensitivity table crossing WACC against terminal growth rates alongside bull, base, and bear scenarios.

Execution relies on Python 3 with `yfinance`, `numpy`, and `pandas`. If these packages are missing, the setup flow triggers `python3 -m pip install -q yfinance numpy pandas`. It pulls live 10-year US Treasury yields for the risk-free rate, falling back to 4.5% if the ticker fetch fails.

Valuation logic adapts to sector profiles. DCF is skipped for banks, REITs, and pre-revenue companies in favor of specialized book value or revenue multiples. Without shell execution permissions, the agent cannot pull SEC filings or pricing data and will halt.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add himself65/finance-skills --skill company-valuation -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/company-valuation/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/company-valuation/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add himself65/finance-skills --skill company-valuation -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/company-valuation/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add himself65/finance-skills --skill company-valuation -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/company-valuation/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install himself65/finance-skills company-valuation
```

2. **Manually: put the files in place**
- File: `.github/skills/company-valuation/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add himself65/finance-skills --skill company-valuation -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/himself65/finance-skills --path plugins/market-analysis/skills/company-valuation
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/company-valuation/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add himself65/finance-skills --skill company-valuation -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/himself65/finance-skills/tree/HEAD/plugins/market-analysis/skills/company-valuation/SKILL.md

## When to use it
- calculate public company intrinsic value using a 5-year DCF model
- benchmark valuation against peer medians using EV/EBITDA and P/E
- break down multi-segment corporate valuation with SOTP
- generate a 5×5 WACC and terminal growth sensitivity matrix

## Example prompts
- Run a DCF valuation for AAPL and calculate implied share price
- Value MSFT using peer multiples and build bull, base, and bear scenarios
- Perform a sum-of-the-parts valuation for this conglomerate

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 298c26b2f6945919.

BEGIN THIRD-PARTY FILE 298c26b2f6945919
````text
---
name: company-valuation
description: >
  Estimate a public company's intrinsic value with DCF, relative (peer multiple), and
  sum-of-the-parts (SOTP) methods, then blend them into an implied share price with
  upside/downside vs the market price, a WACC and terminal-growth sensitivity grid,
  and bull/base/bear scenarios. Use this skill whenever the user asks what a company
  or ticker is worth: fair value, intrinsic value, implied share price, a price target
  from fundamentals, whether it is overvalued or undervalued, building a DCF (WACC,
  terminal value, discounted cash flow), EV/EBITDA or P/E based targets, peer
  comparison valuation, or SOTP and conglomerate discounts. Run the model rather than
  answering valuation questions from memory.
---

# Company Valuation

If your host has not already executed the shell probes below (for example, in Codex), run the commands inside the `!` wrappers with its shell tool before choosing a setup path. Use the actual output; if shell access is unavailable, report that limitation.

Triangulates intrinsic value via three methods, then blends them to an implied share price:

1. **DCF** — 5-year FCFF projection, discount at WACC, terminal value.
2. **Relative** — apply peer median P/E, EV/Revenue, EV/EBITDA.
3. **SOTP** — when 2+ distinct reporting segments exist, value each at pure-play peer multiples.

Always present a WACC × terminal-growth sensitivity table and Bull/Base/Bear scenarios.

**Disclaimer**: Research/educational output. Not financial advice.

---

## Step 1: Detection Flow

Detect data source and runtime deps. The skill supports 2 method paths — pick the richest one available.

**Environment status:**

```
!`python3 -c "exec('try:\n import yfinance, numpy, pandas\n print(\'YFIN_OK\')\nexcept Exception:\n print(\'YFIN_MISSING\')')"`
```

```
!`python3 -c "exec('try:\n import yfinance as yf\n t=yf.Ticker(\'^TNX\')\n p=t.fast_info.last_price\n print(f\'RF_10Y={p/100:.4f}\')\nexcept Exception:\n print(\'RF_FETCH_FAIL\')')"`
```

**Decision tree:**

| Condition | Method path |
|---|---|
| `YFIN_OK` | **Path A** (primary): yfinance for financials + peer multiples |
| `YFIN_MISSING` | **Path B**: pip-install yfinance, then Path A. `python3 -m pip install -q yfinance numpy pandas` |
| `RF_FETCH_FAIL` | Use default `rf = 0.045` and note stale risk-free rate in output |

If `RF_10Y=` printed, use that value as `rf` in Step 4d instead of the hardcoded 4.5%.

---

## Step 2: Choose Methods & Set Defaults

### Method applicability

| Company type | DCF | Relative | SOTP | Fallback |
````
END THIRD-PARTY FILE 298c26b2f6945919

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