comps-analysis

## 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 Web search results can be outdated, inaccurate, or unreliable for financial analysis.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