comps-analysis

Build a comparable company analysis Excel workbook with formula-driven valuation multiples.

4|Updated May 18, 2026
One-click install
npx skills add https://github.com/ZardLi1115/zedclaw --skill comps-analysis-zardli1115
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: comps-analysis
Source: https://github.com/ZardLi1115/zedclaw/tree/main/optional-skills/finance/comps-analysis
Command: npx skills add https://github.com/ZardLi1115/zedclaw --skill comps-analysis-zardli1115

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Comparable company analysis is hard to build correctly because it requires consistent data sourcing, disciplined Excel modeling, and repeatable statistical benchmarking across a peer set.

Core Features & Use Cases

  • Institutional-grade Excel comps model: Produces a structured .xlsx with operating metrics, valuation multiples, and peer-distribution statistics.
  • Formula-first modeling: Ensures derived values are computed via Excel formulas that reference input cells (reducing silent calculation errors).
  • Data-source guardrails: Enforces an MCP-first hierarchy (S&P Kensho / FactSet / Daloopa) and avoids uncontrolled web-search as the primary source.
  • Workflow with verification steps: Guides the agent to confirm structure, inputs, margins, multiples, and sanity checks before completing the sheet.
  • Auditability requirements: Requires cell comments for all hardcoded inputs to preserve traceability and assumptions.

Quick Start

Use the comps-analysis skill to generate a headless openpyxl Excel model for comparable company valuation using MCP financial data sources when available.

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 Excel model with formula-driven valuation multiples?

Comparable company analysis Excel models can be built using openpyxl to generate structured peer benchmarking sheets with formula-driven cell dependencies and quartile statistics for operating metrics and valuation multiples.

What is quartile benchmarking and how does it apply to peer comps valuation?

Quartile benchmarking calculates the 25th, median, and 75th percentile distribution of peer valuation multiples to identify sector outliers and establish relative pricing bands for public-company valuation or M&A screening.

How do I ensure traceability of hardcoded inputs when building a comps financial model in Excel?

Traceability in comps financial modeling requires adding cell comments to all hardcoded inputs, ensuring derived values are computed via Excel formulas that reference input cells rather than embedding silent calculation errors.

Can I use MCP financial data sources like S&P Kensho or FactSet for peer comps analysis?

MCP financial data sources like S&P Kensho, FactSet, or Daloopa serve as primary data providers for peer comps analysis, enforcing a data-source hierarchy that avoids uncontrolled web-search as the primary input method.

What are the limitations of using openpyxl for investment banking comps modeling?

Openpyxl limitations for investment banking comps modeling include the need for headless execution without Excel's native solver, requiring formula-first modeling and structured verification steps to confirm margins, multiples, and sanity checks before completion.

Does the comps-analysis workflow support IPO pricing and sector outlier detection scenarios?

Comps-analysis workflows support IPO pricing, sector outlier detection, public-company valuation, and M&A screening by generating peer sets with statistical quartiles and operating metrics benchmarking.