# Comps Analysis

> Build comparable company analysis in Excel — operating metrics, valuation multiples, statistical benchmarking vs peer sets. Pairs with excel-author. Use for public-company valuation, IPO pricing, sector benchmarking, or outlier detection.

- **Type:** Skill
- **Install:** `agentstack add skill-wesleysimplicio-simplicio-agent-comps-analysis`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [wesleysimplicio](https://agentstack.voostack.com/s/wesleysimplicio)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [wesleysimplicio](https://github.com/wesleysimplicio)
- **Source:** https://github.com/wesleysimplicio/simplicio-agent/tree/main/optional-skills/finance/comps-analysis

## Install

```sh
agentstack add skill-wesleysimplicio-simplicio-agent-comps-analysis
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

## Environment

This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.

# Comparable Company Analysis

## ⚠️ CRITICAL: Data Source Priority (READ FIRST)

**ALWAYS follow this data source hierarchy:**

1. **FIRST: Check for MCP data sources** - If S&P Kensho MCP, FactSet MCP, or Daloopa MCP are available, use them exclusively for financial and trading information
2. **DO NOT use web search** if the above MCP data sources are available
3. **ONLY if MCPs are unavailable:** Then use Bloomberg Terminal, SEC EDGAR filings, or other institutional sources
4. **NEVER use web search as a primary data source** - it lacks the accuracy, audit trails, and reliability required for institutional-grade analysis

**Why this matters:** MCP sources provide verified, institutional-grade data with proper citations. Web search results can be outdated, inaccurate, or unreliable for financial analysis.

---

## Overview
This skill teaches the agent to build institutional-grade comparable company analyses that combine operating metrics, valuation multiples, and statistical benchmarking. The output is a structured Excel/spreadsheet that enables informed investment decisions through peer comparison.

**Reference Material & Contextualization:**

An example comparable company analysis is provided in `examples/comps_example.xlsx`. When using this or other example files in this skill directory, use them intelligently:

**DO use examples for:**
- Understanding structural hierarchy (how sections flow)
- Grasping the level of rigor expected (statistical depth, documentation standards)
- Learning principles (clear headers, transparent formulas, audit trails)

**DO NOT use examples for:**
- Exact reproduction of format or metrics
- Copying layout without considering context
- Applying the same visual style regardless of audience

**ALWAYS ask yourself first:**
1. **"Do you have a preferred format or should I adapt the template style?"**
2. **"Who is the audience?"** (Investment committee, board presentation, quick reference, detailed memo)
3. **"What's the key question?"** (Valuation, growth analysis, competitive positioning, efficiency)
4. **"What's the context?"** (M&A evaluation, investment decision, sector benchmarking, performance review)

**Adapt based on specifics:**
- **Industry context**: Big tech mega-caps need different metrics than emerging SaaS startups
- **Sector-specific needs**: Add relevant metrics early (e.g., cloud ARR, enterprise customers, developer ecosystem for tech)
- **Company familiarity**: Well-known companies may need less background, more focus on delta analysis
- **Decision type**: M&A requires different emphasis than ongoing portfolio monitoring

**Core principle:** Use template principles (clear structure, statistical rigor, transparent formulas) but vary execution based on context. The goal is institutional-quality analysis, not institutional-looking templates.

User-provided examples and explicit preferences always take precedence over defaults.

## Core Philosophy
**"Build the right structure first, then let the data tell the story."**

Start with headers that force strategic thinking about what matters, input clean data, build transparent formulas, and let statistics emerge automatically. A good comp should be immediately readable by someone who didn't build it.

---

## ⚠️ CRITICAL: Formulas Over Hardcodes + Step-by-Step Verification

**Formulas, not hardcodes:**
- Every derived value (margin, multiple, statistic) MUST be an Excel formula referencing input cells — never a pre-computed number pasted in
- When using Python/openpyxl to build the sheet: write `cell.value = "=E7/C7"` (formula string), NOT `cell.value = 0.687` (computed result)
- The only hardcoded values should be raw input data (revenue, EBITDA, share price, etc.) — and every one of those gets a cell comment with its source
- Why: the model must update automatically when an input changes. A hardcoded margin is a silent bug waiting to happen.

**Verify step-by-step with the user:**
- After setting up the structure → show the user the header layout before filling data
- After entering raw inputs → show the user the input block and confirm sources/periods before building formulas
- After building operating metrics formulas → show the calculated margins and sanity-check with the user before moving to valuation
- After building valuation multiples → show the multiples and confirm they look reasonable before adding statistics
- Do NOT build the entire sheet end-to-end and then present it — catch errors early by confirming each section

---

## Section 1: Document Structure & Setup

### Header Block (Rows 1-3)
```
Row 1: [ANALYSIS TITLE] - COMPARABLE COMPANY ANALYSIS
Row 2: [List of Companies with Tickers] • [Company 1 (TICK1)] • [Company 2 (TICK2)] • [Company 3 (TICK3)]
Row 3: As of [Period] | All figures in [USD Millions/Billions] except per-share amounts and ratios
```

**Why this matters:** Establishes context immediately. Anyone opening this file knows what they're looking at, when it was created, and how to interpret the numbers.

### Visual Convention Standards (OPTIONAL - User preferences and uploaded templates always override)

**IMPORTANT: These are suggested defaults only. Always prioritize:**
1. User's explicit formatting preferences
2. Formatting from any uploaded template files
3. Company/team style guides
4. These defaults (only if no other guidance provided)

**Suggested Font & Typography:**
- **Font family**: Times New Roman (professional, readable, industry standard)
- **Font size**: 11pt for data cells, 12pt for headers
- **Bold text**: Section headers, company names, statistic labels

**Default Color & Shading — Professional Blue/Grey Palette (minimal is better):**
- **Keep it restrained** — only blues and greys. Do NOT introduce greens, oranges, reds, or multiple accent colors. A clean comps sheet uses 3-4 colors total.
- **Section headers** (e.g., "OPERATING STATISTICS & FINANCIAL METRICS"):
  - Dark blue background (`#1F4E79` or `#17365D` navy)
  - White bold text
  - Full row shading across all columns
- **Column headers** (e.g., "Company", "Revenue", "Margin"):
  - Light blue background (`#D9E1F2` or similar pale blue)
  - Black bold text
  - Centered alignment
- **Data rows**:
  - White background for company data
  - Black text for formulas; blue text for hardcoded inputs
- **Statistics rows** (Maximum, 75th Percentile, etc.):
  - Light grey background (`#F2F2F2`)
  - Black text, left-aligned labels
- **That's the whole palette**: dark blue + light blue + light grey + white. Nothing else unless the user's template says otherwise.

**Suggested Formatting Conventions:**
- **Decimal precision**:
  - Percentages: 1 decimal (12.3%)
  - Multiples: 1 decimal (13.5x)
  - Dollar amounts: No decimals, thousands separator (69,632)
  - Margins shown as percentages: 1 decimal (68.7%)
- **Borders**: No borders (clean, minimal appearance)
- **Alignment**: All metrics center-aligned for clean, uniform appearance
- **Cell dimensions**: All column widths should be uniform/even, all row heights should be consistent (creates clean, professional grid)

**Note:** If the user provides a template file or specifies different formatting, use that instead.

---

## Section 2: Operating Statistics & Financial Metrics

### Core Columns (Start with these)
1. **Company** - Names with consistent formatting
2. **Revenue** - Size metric (can be LTM, quarterly, or annual depending on context)
3. **Revenue Growth** - Year-over-year percentage change
4. **Gross Profit** - Revenue minus cost of goods sold
5. **Gross Margin** - GP/Revenue (fundamental profitability)
6. **EBITDA** - Earnings before interest, tax, depreciation, amortization
7. **EBITDA Margin** - EBITDA/Revenue (operating efficiency)

### Optional Additions (Choose based on industry/purpose)
- **Quarterly vs LTM** - Include both if seasonality matters
- **Free Cash Flow** - For capital-intensive or SaaS businesses
- **FCF Margin** - FCF/Revenue (cash generation efficiency)
- **Net Income** - For mature, profitable companies
- **Operating Income** - For businesses with varying D&A
- **CapEx metrics** - For asset-heavy industries
- **Rule of 40** - Specifically for SaaS (Growth % + Margin %)
- **FCF Conversion** - For quality of earnings analysis (advanced)

### Formula Examples (Using Row 7 as example)
```excel
// Core ratios - these are always calculated
Gross Margin (F7): =E7/C7
EBITDA Margin (H7): =G7/C7

// Optional ratios - include if relevant
FCF Margin: =[FCF]/[Revenue]
Net Margin: =[Net Income]/[Revenue]
Rule of 40: =[Growth %]+[FCF Margin %]
```

**Golden Rule:** Every ratio should be [Something] / [Revenue] or [Something] / [Something from this sheet]. Keep it simple.

### Statistics Block (After company data)

**CRITICAL: Add statistics formulas for all comparable metrics (ratios, margins, growth rates, multiples).**

```
[Leave one blank row for visual separation]
- Maximum: =MAX(B7:B9)
- 75th Percentile: =QUARTILE(B7:B9,3)
- Median: =MEDIAN(B7:B9)
- 25th Percentile: =QUARTILE(B7:B9,1)
- Minimum: =MIN(B7:B9)
```

**Columns that NEED statistics (comparable metrics):**
- Revenue Growth %, Gross Margin %, EBITDA Margin %, EPS
- EV/Revenue, EV/EBITDA, P/E, Dividend Yield %, Beta

**Columns that DON'T need statistics (size metrics):**
- Revenue, EBITDA, Net Income (absolute size varies by company scale)
- Market Cap, Enterprise Value (not comparable across different-sized companies)

**Note:** Add one blank row between company data and statistics rows for visual separation. Do NOT add a "SECTOR STATISTICS" or "VALUATION STATISTICS" header row.

**Why quartiles matter:** They show distribution, not just average. A 75th percentile multiple tells you what "premium" companies trade at.

---

## Section 3: Valuation Multiples & Investment Metrics

### Core Valuation Columns (Start with these)
1. **Company** - Same order as operating section
2. **Market Cap** - Current market valuation
3. **Enterprise Value** - Market Cap ± Net Debt/Cash
4. **EV/Revenue** - How much market pays per dollar of sales
5. **EV/EBITDA** - How much market pays per dollar of earnings
6. **P/E Ratio** - Price relative to net earnings

### Optional Valuation Metrics (Choose based on context)
- **FCF Yield** - FCF/Market Cap (for cash-focused analysis)
- **PEG Ratio** - P/E/Growth Rate (for growth companies)
- **Price/Book** - Market value vs. book value (for asset-heavy businesses)
- **ROE/ROA** - Return metrics (for profitability comparison)
- **Revenue/EBITDA CAGR** - Historical growth rates (for trend analysis)
- **Asset Turnover** - Revenue/Assets (for operational efficiency)
- **Debt/Equity** - Leverage (for capital structure analysis)

**Key Principle:** Include 3-5 core multiples that matter for your industry. Don't include every possible metric just because you can.

### Formula Examples
```excel
// Core multiples - always include these
EV/Revenue: =[Enterprise Value]/[LTM Revenue]
EV/EBITDA: =[Enterprise Value]/[LTM EBITDA]
P/E Ratio: =[Market Cap]/[Net Income]

// Optional multiples - include if data available
FCF Yield: =[LTM FCF]/[Market Cap]
PEG Ratio: =[P/E]/[Growth Rate %]
```

### Cross-Reference Rule
**CRITICAL:** Valuation multiples MUST reference the operating metrics section. Never input the same raw data twice. If revenue is in C7, then EV/Revenue formula should reference C7.

### Statistics Block
Same structure as operating section: Max, 75th, Median, 25th, Min for every metric. Add one blank row for visual separation between company data and statistics. Do NOT add a "VALUATION STATISTICS" header row.

---

## Section 4: Notes & Methodology Documentation

### Required Components

**Data Sources & Quality:**
- Where did the data come from? (S&P Kensho MCP, FactSet MCP, Daloopa MCP, Bloomberg, SEC filings)
- What period does it cover? (Q4 2024, audited figures)
- How was it verified? (Cross-checked against 10-K/10-Q)
- Note: Prioritize MCP data sources (S&P Kensho, FactSet, Daloopa) if available for better accuracy and traceability

**Key Definitions:**
- EBITDA calculation method (Gross Profit + D&A, or Operating Income + D&A)
- Free Cash Flow formula (Operating CF - CapEx)
- Special metrics explained (Rule of 40, FCF Conversion)
- Time period definitions (LTM, CAGR calculation periods)

**Valuation Methodology:**
- How was Enterprise Value calculated? (Market Cap + Net Debt)
- What growth rates were used? (Historical CAGR, forward estimates)
- Any adjustments made? (One-time items excluded, normalized margins)

**Analysis Framework:**
- What's the investment thesis? (Cloud/SaaS efficiency)
- What metrics matter most? (Cash generation, capital efficiency)
- How should readers interpret the statistics? (Quartiles provide context)

---

## Section 5: Choosing the Right Metrics (Decision Framework)

### Start with "What question am I answering?"

**"Which company is undervalued?"**
→ Focus on: EV/Revenue, EV/EBITDA, P/E, Market Cap
→ Skip: Operational details, growth metrics

**"Which company is most efficient?"**
→ Focus on: Gross Margin, EBITDA Margin, FCF Margin, Asset Turnover
→ Skip: Size metrics, absolute dollar amounts

**"Which company is growing fastest?"**
→ Focus on: Revenue Growth %, EBITDA CAGR, User/Customer Growth
→ Skip: Margin metrics, leverage ratios

**"Which is the best cash generator?"**
→ Focus on: FCF, FCF Margin, FCF Conversion, CapEx intensity
→ Skip: EBITDA, P/E ratios

### Industry-Specific Metric Selection

**Software/SaaS:**
Must have: Revenue Growth, Gross Margin, Rule of 40
Optional: ARR, Net Dollar Retention, CAC Payback
Skip: Asset Turnover, Inventory metrics

**Manufacturing/Industrials:**
Must have: EBITDA Margin, Asset Turnover, CapEx/Revenue
Optional: ROA, Inventory Turns, Backlog
Skip: Rule of 40, SaaS metrics

**Financial Services:**
Must have: ROE, ROA, Efficiency Ratio, P/E
Optional: Net Interest Margin, Loan Loss Reserves
Skip: Gross Margin, EBITDA (not meaningful for banks)

**Retail/E-commerce:**
Must have: Revenue Growth, Gross Margin, Inventory Turnover
Optional: Same-Store Sales, Customer Acquisition Cost
Skip: Heavy R&D or CapEx metrics

### The "5-10 Rule"

**5 operating metrics** - Revenue, Growth, 2-3 margins/efficiency metrics
**5 valuation metrics** - Market Cap, EV, 3 multiples
**= 10 total columns** - Enough to tell the story, not so many you lose the thread

If you have more than 15 metrics, you're probably including noise. Edit ruthlessly.

---

## Section 6: Best Practices & Quality Checks

### Before You Start
1. **Define the peer group** - Companies must be truly comparable (similar business model, scale, geography)
2. **Choose the right period** - LTM smooths seasonality; quarterly shows trends
3. **Standardize units upfront** - Millions vs. billions decision affects everything
4. **Map data sources** - Know where each number comes from

### As You Build
1. **Input all raw data first** - Complete the blue text before writing formulas
2. **Add cell comments to ALL hard-coded inputs** - Right-click cell → Insert Comment → Document source OR assumption

   **For sourced data, cite exactly where it came from:**
   - Example: "Bloomberg Terminal - MSFT Equity DES, accessed 2024-10-02"
   - Example: "Q4 2024 10-K filing, page 42, line item 'Total Revenue'"
   - Example: "FactSet consensus estimate as of 2024-10-02"
   - **Include hyperlinks when possible**: Right-click cell → Link → paste URL to SEC filing, data source, or report

   **For assumptions, explain the reasoning:**
   - Example: "Assumed 15% EBITDA margin based on peer median, company does not disclose"
   - Example: "Estimated Enterprise Value as Market Cap + $50M net debt (from Q3 balance sheet, Q4 not yet available)"
   - Example: "Forward

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [wesleysimplicio](https://github.com/wesleysimplicio)
- **Source:** [wesleysimplicio/simplicio-agent](https://github.com/wesleysimplicio/simplicio-agent)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-wesleysimplicio-simplicio-agent-comps-analysis
- Seller: https://agentstack.voostack.com/s/wesleysimplicio
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.