Comps Analysis
Produce institutional-style comparable company analyses with operating metrics and valuation multiples in Excel for investment or M&A decisions.
Overview
comps-analysis is an agent skill for the Validate phase that builds institutional comparable company analyses with multiples and peer benchmarking in spreadsheet form.
Install
npx skills add https://github.com/anthropics/financial-services-plugins --skill comps-analysisWhat is this skill?
- Institutional-grade comps with operating metrics, valuation multiples, and statistical benchmarking
- Excel/spreadsheet output suited to IC decks and sector overview reports
- Mandatory MCP-first data hierarchy (S&P Kensho, FactSet, Daloopa) before web search
- Explicit not-ideal cases: pre-revenue startups, distressed names, unique models without peers
- Supports M&A, IPO pricing, peer performance benchmarking, and outlier identification
- MCP-first hierarchy: Kensho, FactSet, Daloopa before web search
Adoption & trust: 806 installs on skills.sh; 30.5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You need to price a round, IPO, or M&A view but lack a disciplined peer set with verified multiples and operating metrics.
Who is it for?
Public-company peer sets when Kensho, FactSet, or Daloopa MCPs are available and you need IC-ready benchmarking.
Skip if: Private companies without public peers, highly diversified conglomerates, distressed/bankrupt names, pre-revenue startups, or unique models with no comp universe.
When should I use this skill?
Public company valuation, peer benchmarking, IPO or funding round pricing, sector reports, or outlier identification when institutional MCP data is available.
What do I get? / Deliverables
You receive an Excel-oriented comps workbook narrative with peer benchmarks, valuation multiples, and sourcing aligned to MCP-first institutional data rules.
- Comparable company analysis in Excel/spreadsheet format
- Operating metrics and valuation multiple tables with statistical benchmarking
Recommended Skills
Journey fit
Validate is where pricing, funding, and valuation hypotheses are tested before committing capital or narrative to the market. Pricing subphase fits IPO/funding round pricing, peer multiples, and identifying over/under-valued benchmarks versus ad-hoc spreadsheets.
How it compares
Institutional comps generator with MCP data discipline—not a generic web-scrape valuation calculator.
Common Questions / FAQ
Who is comps-analysis for?
Builders and analysts doing public-company valuation, IPO pricing, M&A support, or sector benchmarking who need spreadsheet-grade comps.
When should I use comps-analysis?
During Validate pricing when you have identifiable public peers and MCP financial data—not for pre-revenue startups or distressed names called out as poor fits.
Is comps-analysis safe to install?
Review the Security Audits panel on this page; the skill may invoke financial MCPs and must not treat web search as primary institutional data.
SKILL.md
READMESKILL.md - Comps Analysis
# 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 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:** 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 Ove