Company valuation

What the skill tells Claude

This is the skill's instruction file, SKILL.md, as Claude reads it.

Overview

This skill performs end-to-end company valuation analysis. It reads references/company_valuation_models.md to understand the library of 22 models, then follows a strict five-phase workflow:

1. Parse — understand the company and the investment scenario
2. Select — choose the 3–4 models best suited to the company and scenario
3. Source — retrieve all required financial data from live connectors and filings
4. Calculate — run every selected model using the formulas in the reference
5. Report — produce a structured markdown report

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Invocation

/company-valuation-analyst <company name or ticker> — <scenario description>

Examples: - /company-valuation-analyst AAPL — long-term buy-and-hold equity investment - /company-valuation-analyst Microsoft — strategic acquisition analysis - /company-valuation-analyst a mid-market SaaS company with $50M ARR — Series C funding round - /company-valuation-analyst a US utility company — DCF for regulated asset base - /company-valuation-analyst Tesla — PE-style LBO feasibility check

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STRICT WORKFLOW (follow every phase in order)

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PHASE 1 — PARSE & CLARIFY

1. Extract the company identifier (ticker, name, or description) and the scenario from the user's input.
2. Identify the following scenario dimensions — these drive model selection: - Purpose: Buy-side equity / Sell-side M&A / PE/LBO / VC / Credit / Internal planning - Stage: Early-stage / High-growth / Mature / Distressed / Conglomerate - Sector: Technology / Financials / Industrials / Energy / Healthcare / Consumer / Real estate / Other - Cash flow status: Pre-revenue / Pre-profit / Profitable and stable / Highly levered - Dividend status: Pays dividends / Does not pay dividends
3. If the company or ticker is ambiguous, use web_search to confirm the correct entity before proceeding.

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PHASE 2 — MODEL SELECTION

Read references/company_valuation_models.md to review all 22 models. Select 3 to 4 models using the decision matrix below.

Model Selection Decision Matrix

Mandatory defaults:

• Always include DCF (FCFF) unless the company is pre-revenue/pre-profit OR a financial institution.
• Always include at least one market-based (relative) model (Trading Comps or a multiple) as a cross-check.
• Never select more than 4 models — prioritise depth over breadth.

For each selected model, document upfront:

• Why this model was chosen for this specific company and scenario
• What data inputs are required
• Which data sources will be used to obtain them

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PHASE 3 — DATA SOURCING

Source all required inputs for every selected model. Use the priority order below. Document every data point with its source — this populates the References section of the report.

Source Priority Order

1. PitchBook (only if the user has a PitchBook connector or licence available; otherwise skip to the next source) - Use for: Company financials, deal comps, transaction multiples, cap table, funding rounds, revenue estimates for private companies - Use whichever PitchBook connector or tool is available in the session; do not assume one exists

2. FactSet (only if the user has a FactSet connector or licence available; otherwise skip to the next source) - Use for: Public company financials, consensus estimates, trading multiples, peer group data, EV/EBITDA comps, cost of capital (WACC components) - Use whichever FactSet connector or tool is available in the session; do not assume one exists

3. SEC EDGAR (via /sec-edgar skill) - Use for: Public company 10-K/10-Q filings, XBRL financial statements, revenue, EBITDA, net income, capex, D&A, balance sheet items - Invoke as: use the sec-edgar skill and follow its workflow to extract the latest 10-K or 10-Q for the target ticker

4. Web Search - Use for: Beta estimates, risk-free rate (current 10-year Treasury yield), equity risk premium, analyst consensus estimates, sector EV/EBITDA multiples, dividend history, recent news - Always check publication dates; use 2026 figures where available

5. Web fetch / Manual - Use for: Specific data points from investor relations pages, proxy statements, earnings call transcripts

Data Checklist by Model

DCF (FCFF): - [ ] Revenue (last 3 years actuals + 3-5 year forecasts) - [ ] EBITDA margin (historical + projected) - [ ] D&A, Capex, change in working capital - [ ] Tax rate (effective) - [ ] WACC components: Risk-free rate, equity risk premium, beta, cost of debt, capital structure - [ ] Terminal growth rate (long-run GDP or sector growth)

Gordon Growth Model / DDM: - [ ] Annual dividend per share (most recent) - [ ] Dividend growth rate (5-year CAGR or analyst forecast) - [ ] Cost of equity (from CAPM: Rf + beta × ERP)

EV/EBITDA Multiple: - [ ] Target company LTM EBITDA - [ ] Peer group EV/EBITDA median (5–8 comparable companies) - [ ] Net debt (total debt minus cash)

Comparable Company Analysis: - [ ] Target: Revenue, EBITDA, EBIT, Net Income, EPS - [ ] Peer group: Same metrics + current market cap + net debt for each peer - [ ] Calculate: EV/Revenue, EV/EBITDA, P/E for each peer; take median

LBO Model: - [ ] Entry EV/EBITDA multiple (from precedent transactions) - [ ] LTM EBITDA - [ ] Revenue growth and EBITDA margin projections (5 years) - [ ] Leverage assumption (Debt/EBITDA at entry, typically 4–6x) - [ ] Interest rate on debt - [ ] Exit EV/EBITDA multiple assumption - [ ] Target IRR (typically 20%+)

Residual Income / P/B: - [ ] Book value of equity (most recent balance sheet) - [ ] ROE (net income / avg. book equity) - [ ] Cost of equity - [ ] Long-run growth rate

NAV: - [ ] Fair market value of all assets (use cap rates for real estate, DCF for operating assets) - [ ] Total liabilities

Sum-of-the-Parts: - [ ] Segment revenue and EBITDA - [ ] Appropriate peer multiple per segment - [ ] Consolidated net debt

VC Method: - [ ] Projected revenue or EBITDA at exit (Year 5–7) - [ ] Exit multiple (sector EV/Revenue or EV/EBITDA) - [ ] Target IRR (typically 30–50%) - [ ] Investment amount

Real Options: - [ ] PV of project/asset cash flows (S) - [ ] Investment required (K) - [ ] Time to decision (T) - [ ] Risk-free rate (r) - [ ] Asset volatility (sigma — use comparable company stock volatility or project cost volatility)

Liquidation Value: - [ ] Balance sheet: all asset categories with book values - [ ] Apply recovery rate assumptions by asset class - [ ] Total liabilities

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PHASE 4 — CALCULATION

For each selected model:

1. Read the exact formula from references/company_valuation_models.md
2. Populate all inputs with sourced data
3. Execute the calculation step by step (show arithmetic)
4. Derive the implied enterprise value and implied equity value per share (or per unit as appropriate)
5. Perform sensitivity analysis for 1–2 key inputs (e.g., for DCF: vary WACC ±1% and terminal growth ±0.5%; for multiples: use 25th/median/75th percentile of peer group)
6. State the valuation range (low / base / high) for each model

Use Python (execute_command) when calculations are complex (e.g., DCF with multi-year projections, LBO IRR, Black-Scholes for real options). Save calculation scripts to scripts/ directory during runs for transparency.

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PHASE 5 — REPORT GENERATION

Produce a single structured markdown report using the exact format below. Save it as valuation_report_<company>_<date>.md in the workspace.

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OUTPUT REPORT FORMAT (MANDATORY)

# Valuation Analysis: [Company Name] ([Ticker])
**Date:** [Date]
**Scenario:** [Investment scenario description]
**Analyst:** Company Valuation Analyst Skill

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## Executive Summary

[3–5 paragraph narrative covering:
- Company overview and why this scenario was chosen
- Which models were selected and why they are the most appropriate for this company/scenario
- Key findings: implied valuation range across all models
- Investment conclusion / key risks]

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## Valuation Summary Table

| Model | Category | Implied EV ($M) | Implied Equity Value ($M) | Implied Price / Share | Weight | Notes |
|-------|----------|:-:|:-:|:-:|:-:|-------|
| [Model 1] | [Income/Market/Asset] | $X – $Y | $A – $B | $P – $Q | [%] | [1-line note] |
| [Model 2] | ... | ... | ... | ... | ... | ... |
| [Model 3] | ... | ... | ... | ... | ... | ... |
| [Model 4 if applicable] | ... | ... | ... | ... | ... | ... |
| **Blended / Triangulated Range** | | **$X – $Y** | **$A – $B** | **$P – $Q** | 100% | |

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## Model 1: [Model Name]

### Why This Model Was Selected
[1–2 paragraphs explaining why this model is appropriate for this company and scenario,
referencing the company's stage, sector, cash flow profile, and the investor's purpose.]

### Key Inputs

| Input | Value | Source | Rationale |
|-------|-------|--------|-----------|
| [Input name] | [Value with units] | [Source: PitchBook / FactSet / SEC 10-K / Web] | [Why this value was used] |
| ... | ... | ... | ... |

### Calculation

[Show the formula, then substitute values step by step]

**Formula:** [formula from reference]

**Step-by-step:**

[arithmetic, shown clearly]

**Sensitivity Analysis:**

| [Key Input] ↓ / [Other Input] → | [Low] | [Base] | [High] |
|---|---|---|---|
| [Low] | $X | $Y | $Z |
| [Base] | ... | ... | ... |
| [High] | ... | ... | ... |

**Result:** Implied EV = $X – $Y | Implied Equity Value = $A – $B | Implied Price/Share = $P – $Q

### References
- [Source 1: full citation with URL or filing date]
- [Source 2: ...]

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## Model 2: [Model Name]

[Repeat same structure]

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## Model 3: [Model Name]

[Repeat same structure]

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## Model 4: [Model Name, if applicable]

[Repeat same structure]

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## Methodology Notes & Limitations

[Paragraph covering:
- Key assumptions that most influence results
- Data gaps or limitations encountered
- Models that were considered but excluded, and why
- Caveat about forward-looking assumptions]

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## Data Sources

| # | Source | Type | Data Retrieved | Date Accessed |
|---|--------|------|----------------|---------------|
| 1 | PitchBook | Financial data platform | [list data points] | [date] |
| 2 | FactSet | Financial data platform | [list data points] | [date] |
| 3 | SEC EDGAR (10-K/10-Q) | Regulatory filing | [list data points] | [date] |
| 4 | Web Search | Market data | [list data points] | [date] |

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Constraints & Failure Handling

• Private company — no public filings: Use PitchBook for financials; use VC Method or EV/Revenue multiples; skip DCF if no historical cash flow data available.
• No dividend history: Do not use DDM variants; substitute DCF or residual income.
• PitchBook/FactSet not available or returns no data: Fall back to SEC EDGAR + web search; note the gap in the report.
• Insufficient peer group data: Use published sector reports from Damodaran (NYU Stern) or industry research; cite clearly.
• Calculation fails or data missing: Show partial calculation, clearly flag the missing input, and state the range is indicative only.
• Maximum 4 models: If a 5th model would add meaningful perspective, mention it briefly in Methodology Notes instead.

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Reference Files

• references/company_valuation_models.md — Full library of 22 models with formulas, complexity ratings, and usage guidance
• scripts/model_selector.py — Model selection logic helper
• scripts/valuation_engine.py — Calculation functions for all models
• scripts/report_generator.py — Report assembly and formatting helper

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Example Run

Input: /company-valuation-analyst MSFT — strategic acquisition analysis by a technology acquirer

Phase 1 parse: - Company: Microsoft Corporation (MSFT), large-cap technology - Scenario: M&A acquisition → purpose is to determine fair acquisition price - Stage: Mature, highly profitable - Sector: Technology (enterprise software) - Cash flow: Strongly FCF-positive - Dividends: Yes (growing)

Phase 2 model selection: 1. DCF (FCFF) — intrinsic value anchor; MSFT has highly predictable FCF 2. Comparable Company Analysis (EV/EBITDA, P/E) — market reference; large peer group exists 3. Precedent Transaction Analysis — what large-cap software acquisitions have traded at 4. Two-Stage DDM — MSFT pays dividends with consistent growth; cross-check on equity value

Phase 3 data sourcing: FactSet for peer multiples and financials → SEC EDGAR 10-K for FCFF inputs → web search for current WACC components → PitchBook for any M&A transaction comps

Phase 4 calculation: Run each model, produce EV range

Phase 5 report: Deliver full markdown report with all sections

Shared as is, with no warranty. Not investment, legal or tax advice. See Disclosures.