Saas Valuation Compression

This does one thing well: it takes a SaaS company name, pulls funding data from the web, calculates how their ARR multiple changed between rounds, and explains why. It searches for valuation, ARR, and investor info for each round, then attributes compression to macro rate changes (includes a detailed timeline from 2019 ZIRP through the April 2026 meltdown), growth deceleration, AI narrative premium or lack thereof, and competitive shifts. The output is a visualization plus prose breakdown comparing the company to benchmarks like Vercel or Netlify. Use it when you need to understand why a company's valuation multiple went from 140x to 32x even though absolute valuation still grew, or to see if compression was company-specific or just the market resetting.

skills.sh installs: 3.2K
GitHub stars: 3.5K
last updated: Aug 21, 2026

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Install

$ npx -y skills add himself65/finance-skills --skill saas-valuation-compression --agent claude-code

Installs into .claude/skills of the current project.

Files

SaaS Valuation Compression Analyzer

What This Skill Does

For a given SaaS company, research its funding history and compute ARR-based valuation multiples at each round. Then explain the compression (or expansion) using a structured framework that covers macro rates, growth trajectory, narrative shifts, and comparables.

Render the output as an inline visualization (using the Visualizer tool) plus a concise prose explanation, rather than a wall of numbers.

Workflow

1. Gather Data via Web Search

Research these, running independent searches in parallel:

Each funding round of the target company — round name, date, amount raised, post-money valuation, and lead investor.
ARR at or near each round date — from press coverage, founder interviews, or investor posts; note when a figure is estimated.
Growth and retention around each round — ARR growth rate, NRR, churn, notable customers.
Narrative context — AI positioning and product launches, category leadership, competitive moves.
Private-market SaaS multiples at each round date — fall back on the dated tables in references/benchmarks.md when search is thin.