comps-analysis

SKILL.md preview

---
name: comps-analysis
description: 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.
version: 1.0.0
author: Anthropic (adapted by Nous Research)
license: Apache-2.0
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [finance, valuation, comps, excel, openpyxl, modeling, investment-banking]
    related_skills: [excel-author, pptx-author, dcf-model, lbo-model]
---

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 Web search results can be outdated, inaccurate, or unreliable for financial analysis.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

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