Comparable Company Analysis
⚠️ CRITICAL: Data Source Priority (READ FIRST)
**ALWAYS follow this data source hierarchy:**
- **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
- **DO NOT use web search** if the above MCP data sources are available
- **ONLY if MCPs are unavailable:** Then use Bloomberg Terminal, SEC EDGAR filings, or other institutional sources
- **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 Web search results can be outdated, inaccurate, or unreliable for financial analysis.
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Overview
This skill teaches Claude 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:**
- **"Do you have a preferred format or should I adapt the template style?"**
- **"Who is the audience?"** (Investment committee, board presentation, quick reference, detailed memo)
- **"What's the key question?"** (Valuation, growth analysis, competitive positioning, efficiency)
- **"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.
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⚠️ CRITICAL: Formulas Ove
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