# Comps Analysis

Comparable company analysis: peer set, operating metrics, valuation multiples...

- Repository: https://github.com/ginlix-ai/LangAlpha (2232 stars)
- Category: Engineering
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add ginlix-ai/LangAlpha --skill comps-analysis -g -a claude-code -y`
Codex: `npx skills add ginlix-ai/LangAlpha --skill comps-analysis -g -a codex -y`
Cursor: `npx skills add ginlix-ai/LangAlpha --skill comps-analysis -g -a cursor -y`
Gemini CLI: `npx skills add ginlix-ai/LangAlpha --skill comps-analysis -g -a gemini-cli -y`

## Description

Comparable company analysis: peer set, operating metrics, valuation multiples, statistics and an implied value. Triggers on comps, trading comparables, how does it trade against peers, peer benchmarking, what multiple should it get.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Comparable Company Analysis

A comps table is arithmetic anyone can do and data discipline almost nobody does. The multiples are the easy half: the work is in which companies belong in the set, whether each number measures the same thing over the same period, and how old it is. The steps below build the table in the order those questions have to be answered.

Evidence labels, source tiers, staleness, the readiness posture and the intake limits: `.agents/skills/research-conventions/SKILL.md`, read before the first figure enters the table.

Period labels, per-company LTM windows, NTM as four quarterly estimates, and one common base date for every comparative return are binding here: `.agents/skills/research-conventions/references/market-data-rules.md`, read before the first pull. Step 3 adds no rule of its own and says only which of those period cases a comps table selects.

## Step 1: Frame the table

Four questions decide what gets built. How many to ask, in what shape, and what to do when no answer comes back: `.agents/skills/research-conventions/references/intake.md`.

1. **Format**: their template, or ours
2. **Audience**: investment committee, board, quick reference, detailed memo
3. **The key question**: valuation, growth, competitive position, efficiency
4. **The context**: M&A, an investment decision, sector benchmarking, performance review

The answers change the table, not just its wrapper. A relative-value question needs multiples and quartiles; an efficiency question needs margins and turns and can drop the multiples entirely. Big-cap incumbents and emerging names in the same sector do not take the same metrics. User-provided examples and stated preferences outrank every default in this file.

**Done when** the question the table answers is written down in one sentence and the metric list follows from it.

## Step 2: Build the peer set, with roles

The rest: https://github.com/ginlix-ai/LangAlpha/blob/HEAD/plugins/langalpha_research/skills/comps-analysis/SKILL.md