comparable-company-analyzer
Public company comparable analysis skill with peer selection, multiple calculation, and football field visualization
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/comparable-company-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How comparable-company-analyzer Compares
| Feature / Agent | comparable-company-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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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