comparable-company-analyzer

Public company comparable analysis skill with peer selection, multiple calculation, and football field visualization

509 stars

Best use case

comparable-company-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Public company comparable analysis skill with peer selection, multiple calculation, and football field visualization

Teams using comparable-company-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/comparable-company-analyzer/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/business/finance-accounting/skills/comparable-company-analyzer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/comparable-company-analyzer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How comparable-company-analyzer Compares

Feature / Agentcomparable-company-analyzerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Public company comparable analysis skill with peer selection, multiple calculation, and football field visualization

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Comparable Company Analyzer

## Overview

The Comparable Company Analyzer skill provides comprehensive public company trading analysis capabilities. It enables peer universe selection, trading multiple calculation, and valuation benchmarking through systematic comparable analysis.

## Capabilities

### Peer Universe Selection
- Industry classification screening
- Size parameter filtering
- Geographic focus alignment
- Business model similarity
- Growth profile matching
- Margin profile comparison

### Trading Multiple Calculation
- Enterprise value multiples (EV/EBITDA, EV/Revenue)
- Price multiples (P/E, P/B, PEG)
- Industry-specific metrics
- Adjusted metrics calculation
- Diluted share counts
- Net debt calculation

### LTM and NTM Metric Normalization
- Last twelve months calculation
- Next twelve months estimates
- Calendarization adjustments
- Non-recurring item adjustment
- Stock compensation normalization
- Pro forma adjustments

### Outlier Identification
- Statistical outlier detection
- Interquartile range analysis
- Company-specific explanation
- Inclusion/exclusion rationale
- Sensitivity to outliers
- Documentation requirements

### Football Field Chart Generation
- Multiple methodology ranges
- Implied valuation bands
- Median and mean markers
- Current trading position
- Historical trading context
- Presentation formatting

### Equity Value Bridge
- Enterprise to equity value
- Debt deduction
- Cash addition
- Minority interest
- Preferred stock
- Other adjustments

## Usage

### Trading Comparables Analysis
```
Input: Target company, selection criteria, market data
Process: Build peer universe, calculate multiples, determine range
Output: Comparable company analysis, implied valuation, benchmark report
```

### Relative Valuation
```
Input: Target metrics, comparable multiples, adjustments
Process: Apply multiples, bridge to equity value
Output: Valuation range, sensitivity analysis, peer positioning
```

## Integration

### Used By Processes
- Comparable Company Analysis
- M&A Financial Due Diligence
- Discounted Cash Flow (DCF) Valuation

### Tools and Libraries
- Capital IQ API
- FactSet
- Bloomberg Terminal
- Financial databases

## Best Practices

1. Document peer selection rationale
2. Use consistent data sources across peers
3. Adjust for non-recurring items consistently
4. Consider trading liquidity in weighting
5. Update analysis for market movements
6. Cross-check against transaction multiples

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