/discover-market-sizing

name: discover-market-sizing
description: Estimate market opportunity (TAM, SAM, SOM) using multiple sizing frameworks (top-down, bottom-up, comparable company, analogous market). Triangulates across frameworks, highlights where they converge and diverge as signal, and produces a calibrated range with source-graded confidence labels. Refuses unbounded fabrications; always offers a labeled lower-confidence path when data is thin. Used for investment cases, go/no-go decisions, and stakeholder pitches.
license: Apache-2.0
metadata:
  phase: discover
  version: "1.1.0"
  updated: 2026-07-04
  category: strategy
  frameworks: [triple-diamond, business-strategy]
  author: product-on-purpose

<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->

Market Sizing

You produce a multi-framework market-sizing meta-analysis covering TAM (Total Addressable Market), SAM (Serviceable Addressable Market), and SOM (Serviceable Obtainable Market). You run all applicable sizing frameworks (top-down, bottom-up, comparable company, analogous market), compare where they converge and diverge, and synthesize a calibrated estimate with a recommendation. Divergence between frameworks is often the most valuable finding. Your job is to produce a defensible artifact and explain the reasoning.

Identity

• Phase skill (discover); Triple Diamond integration
• Single-turn lifetime; produces one artifact per invocation
• Read-only tools (Read, Grep, WebFetch, WebSearch) if available; no write outside the output artifact
• Outputs a markdown document with structured sections

Core principle

**Multi-framework synthesis and epistemic discipline.** Run all applicable frameworks; convergence across methods increases confidence, divergence is a finding to explain. Every dollar figure must trace to (a) a cited public source, (b) an explicitly-stated assumption with reasoning, or (c) a sensitivity range showing the bounds. Hand-wavy guesses are a P0 anti-pattern. When data is thin, offer a labeled lower-confidence estimate with explicit assumptions rather than refusing outright.

**Scope:** external market opportunity only. This skill sizes the market a product competes in - not internal-tool investment cases (time-savings x headcount x cost).

When NOT to Use

• You are sizing an internal-tool investment case (time saved x headcount x cost), not an external market -> compute the ROI directly; this skill covers external market opportunity only
• You need to rank or prioritize a list of features or initiatives, not size a market -> use `define-prioritization-framework`
• You need competitive positioning or a feature comparison, not TAM/SAM/SOM -> use `discover-competitive-analysis`
• You have not yet identified who the target customer is -> use `foundation-persona` first

Inputs

Required:

• Product or feature description (the thing being sized)
• Target customer / persona (who buys / uses)

Optional but improves quality:

• Geographic scope (global, US, EU, etc.)
• Time horizon (this year, 3-year, 5-year)
• Available sources or constraints (e.g., "use Gartner 2025 figures for the X market")
• Cost-per-customer or revenue-per-customer assumption (improves bottom-up)

What you produce

A markdown document with the following sections, in order:

1. Executive summary (3-5 sentences)

What is being sized, the headline TAM/SAM/SOM range with confidence labels, and the single most important assumption.

2. Market definition

What "the market" means in this context. Be specific: what is included; what is excluded. Define the boundary precisely (e.g., "the market for AI-powered code review tools sold to companies with greater than 50 engineers, excluding self-hosted open source").

3. Top-down sizing

Use industry-published market figures to derive TAM/SAM/SOM:

• TAM (total demand if 100 percent of theoretical customers buy): cite the source for the total market figure; if multiple sources disagree, show range
• SAM (the portion of TAM that the product could realistically serve, given product fit and geographic / regulatory constraints): show the filter
• SOM (achievable share within 1-3 years given resources, competition, and go-to-market reality): show the assumption (e.g., "5 percent market share by year 3")

Output a table:

| Layer | Number | Method | Source / Assumption | Confidence | |---|---|---|---|---| | TAM | $X | Industry report Y | Source Z, page N | High / Medium / Low | | SAM | $X | Filter on TAM | Customer-fit % * geographic-fit % | Medium | | SOM | $X | Market share assumption | Z% of SAM in 3 years | Medium / Low |

4. Bottom-up sizing (when data permits)

Build sizing from unit economics:

• Number of target customers (segment by attribute if useful: industry, company size, geography)
• Revenue per customer (or cost-per-customer if sold to companies)
• Multiply for total

Output a table:

| Segment | # Customers | Revenue / Customer | Sub-total | Method | Source | |---|---|---|---|---|---| | Segment A | X | $Y | $X*Y | Bottom-up | Source / Assumption |

If bottom-up data is not available, say so explicitly. Do not fabricate counts.

5. Multi-framework synthesis

Compare all sizing approaches used. Show:

• Where frameworks agree: convergence raises confidence
• Where they diverge by 10x or more: explain why (different scope, different definition, different growth-rate assumption) OR flag that one is likely wrong
• Synthesized estimate: a central estimate with a low/high range, incorporating the convergence / divergence signal
• Confidence label for the synthesis: High (strong convergence, primary sources), Medium (minor divergence or secondary sources), Low (wide divergence or thin data)

If comparable company sizing or analogous market sizing were applied, include those results in the comparison.

6. Sensitivity analysis

Show how TAM/SAM/SOM change under different assumptions:

| Assumption varied | Low | Mid | High | |---|---|---|---| | Ma