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comps-analysis

Generate Excel comparable company analysis with formula-based metrics and valuation multiples.

19|4|Updated Apr 22, 2026
Search Tags:#openpyxl#valuation#comps#investment banking#financial benchmarking#excel modeling#financial modeling governance
One-click install
npx skills add https://github.com/carterwayneskhizeine/hermes-agent-windows-R --skill comps-analysis-carterwayneskhizeine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: comps-analysis
Source: https://github.com/carterwayneskhizeine/hermes-agent-windows-R/tree/main/optional-skills/finance/comps-analysis
Command: npx skills add https://github.com/carterwayneskhizeine/hermes-agent-windows-R --skill comps-analysis-carterwayneskhizeine

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns peer-company inputs into a comparable company analysis spreadsheet with operating metrics, valuation multiples, and statistical benchmarking. It helps users move from raw financial and market data to a consistent model that can be audited, updated, and reused for valuation and sector comparisons.

Core Features & Use Cases

  • Comparable company analysis spreadsheet generation: Creates an Excel-style layout for operating metrics, valuation multiples, and peer statistics (quartiles, median, min/max).
  • Formula-first modeling: Ensures derived metrics (margins, growth, multiples, statistics) are written as Excel formulas referencing input cells rather than hardcoded computed values.
  • Data-source governance: Applies an MCP-first hierarchy for financial/trading data (S&P Kensho MCP, FactSet MCP, Daloopa MCP) with explicit fallbacks to institutional sources and a rule to avoid web search when MCPs are available.
  • Audit-ready documentation: Requires notes on data sources, verification approach, definitions (EBITDA, FCF), time period assumptions, and valuation methodology.
  • Use cases: Public-company valuation, IPO pricing support, sector benchmarking, outlier detection, and investment committee-ready peer comparison.

Quick Start

Use the comps-analysis skill to generate an Excel comparable-company analysis for your selected peer set using the provided financials and valuation inputs.

Frequently Asked Questions about comps-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a comparable company analysis in Excel with live formulas?▼

Build a comparable company analysis in Excel by inputting raw peer financials and market data to generate operating metrics, valuation multiples, and statistical benchmarks. All derived metrics are written as live Excel formulas referencing input cells rather than hardcoded computed values.

What is the best way to benchmark valuation multiples for an IPO peer set?▼

Benchmarking valuation multiples for an IPO peer set involves calculating quartiles, medians, and min/max statistics across comparable public companies. This process ensures consistent time periods and units, applying the model to public-company valuation, IPO pricing, and sector benchmarking workflows.

Can I use MCP financial data sources for peer group financial modeling?▼

You can use MCP financial data sources for peer group financial modeling by following a governance hierarchy that prioritizes S&P Kensho, FactSet, and Daloopa MCPs. This rule explicitly avoids web search when MCPs are available, ensuring institutional-grade data for your valuation analysis.

Does comps analysis require hardcoded values for operating metrics and statistics?▼

Comparable company analysis does not require hardcoded values for operating metrics and statistics. The formula-first modeling approach writes all derived margins, growth rates, multiples, and peer statistics as Excel formulas directly referencing the input cells.

How do I document data sources and methodology for an investment committee peer comparison?▼

Document data sources and methodology for an investment committee peer comparison by embedding audit-ready notes directly in the spreadsheet. This includes definitions for metrics like EBITDA and FCF, time period assumptions, verification approaches, and the valuation methodology applied.

When do I need consistent time periods for sector benchmarking and outlier detection?▼

Consistent time periods for sector benchmarking and outlier detection are needed when comparing operating metrics and valuation multiples across a public-company peer set. This approach ensures that statistical benchmarks accurately identify outliers and support reliable valuation workflows.

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