# Fin Saas Valuation Compression

> Research a SaaS company's funding rounds, compute ARR-based valuation multiples per round, and explain the multiple compression or expansion with a structured framework. Use when the user asks for saas valuation compression analyzer work, or mentions fin, saas, valuation.

- Skill: `criptogus/fin-saas-valuation-compression` (Agent Skill)
- Install (CLI): `npx skillmds@latest add criptogus/fin-saas-valuation-compression`
- Raw SKILL.md: https://api.skillmd.com/api/skills/criptogus/fin-saas-valuation-compression/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- License: MIT
- Author: criptogus (https://skillmd.com/u/criptogus)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/criptogus/fin-saas-valuation-compression

---


# SaaS Valuation Compression Analyzer

Use this skill when a user wants to understand how a SaaS company's valuation multiple has changed
across funding rounds: how the ARR multiple compressed (or expanded) round to round and why. It
researches funding history and ARR via web search, computes multiples and round-over-round
compression, and attributes the change to causes (macro/rate environment, growth deceleration,
narrative shift, AI premium, competition, investor supply/demand).

Output is an inline visualization (metric cards, multiple-over-time line, decomposition bars, peer
comparison) plus a concise prose summary with a cause-attribution table and confidence flag. It uses
pre-loaded private-market multiple benchmarks (including the April 2026 software meltdown) when search
is thin. Research/educational only, not financial advice.

## Instructions

You are a SaaS valuation analyst explaining multiple compression across funding rounds.
Step 1 - Gather data via web search (in parallel): funding rounds, amounts, post-money valuations,
ARR at each round date, lead investors, plus macro and narrative context. Estimate ARR with heuristics
if not public and flag it as estimated.
Step 2 - Build a data model per round (round, date, amount, post-money, ARR, ARR multiple = valuation/ARR, lead).
Step 3 - Compute per consecutive pair: multiple_compression_pct, valuation_growth_pct, arr_growth_pct.
Key identity: valuation_growth ~= arr_growth + multiple_change (ARR can outgrow compression so absolute value rises).
Step 4 - Attribute compression to causes (Primary/Contributing/N/A): macro/rate environment (ZIRP
2020-21 premium, 2022-23 hikes, April 2026 software meltdown), growth deceleration / NRR drop, narrative
shift, AI premium/discount, competition, investor supply/demand. Use the pre-loaded private-market median
multiple benchmark table when search is thin.
Step 5 - Render an inline visualization (metric cards, multiple-over-time vs macro median line, growth-vs-
multiple decomposition bars, peer comparison) followed by a 5-8 sentence prose summary: one-sentence verdict,
primary cause, narrative premium/discount, comparable context, forward implication. Flag data confidence if ARR estimated.
Research/educational only, not financial advice.

## Always

- Research funding and ARR via web search and flag any estimated ARR.
- Compute and decompose compression (multiple, valuation, ARR growth) per round pair.
- Render a visualization plus prose, and state research-only, not financial advice.

## Never

- Present a target valuation as investment advice or a recommendation.
- Report multiples as precise when ARR was estimated, without a confidence flag.

## Examples

### Round-over-round compression

Input:

```
Analyze how Figma's valuation multiple compressed across its funding rounds
```

Expected output:

```
Builds a per-round ARR-multiple model, computes compression and growth decomposition, attributes
the change to macro/narrative causes, and renders metric cards plus a verdict. Research-only.
```

### Thin-data case

Input:

```
Why did this private SaaS company's ARR multiple drop between Series B and C?
```

Expected output:

```
Uses search plus the pre-loaded benchmark table, estimates ARR (flagged), decomposes the move, and
names the primary cause (e.g. growth deceleration vs macro reset), with forward implications.
```

## Trust & telemetry

This skill is graded on the Super Agent Skill network: format, substance and adversarial
(prompt-injection) testing produce a public Trust Score.

- Trust Score & evidence: https://superagentskill.com/marketplace/trust/fin-saas-valuation-compression
- Skill page: https://superagentskill.com/marketplace/fin-saas-valuation-compression
- Live version (always current) via MCP: https://superagentskill.com/api/mcp

Reinstall or update with `npx skills update`, or pull the live graded version with
`npx super-agent install fin-saas-valuation-compression`.