tam-sam-som-calculator skill

Purpose

Guide product managers through calculating Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) for a product idea by asking adaptive, contextually relevant questions. Use this to build defensible market size estimates backed by real-world citations, economic projections, and population data—essential for pitching to investors, securing budget, or validating product-market fit.

This is not a back-of-napkin guess—it's a structured, citation-backed analysis that withstands scrutiny.

Input

Works best with: The product or idea being sized, and any market constraints you already know (geography, vertical, customer type). Also useful: Pricing assumptions, comparable companies, and the audience for the numbers (investors, execs, business case).

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

Arriving empty-handed? That works too. The calculator opens by asking what you're sizing and for whom, then works through method and assumptions.

Three entry modes — pick whichever matches where your evidence lives:

1. Bring your own numbers — you have population and ARPU figures; jump to the math (the helper script makes this deterministic)
2. Guided interview — up to 4 adaptive questions build the inputs with you (the default flow below)
3. Autonomous research — the agent builds a bottom-up, citation-backed estimate from government statistics and filings while you review the evidence (see Mode 3 in Application)

Example invocation: Size the market: AI scheduling assistant for independent dental practices, US only, $99/mo price point. — or for research mode: Research-mode market sizing: field-service dispatch software, DACH region; I have no numbers yet.

Key Concepts

TAM/SAM/SOM Framework

The three-tier market sizing model:

Total Addressable Market (TAM):

• The total market demand for a product or service
• "If we captured 100% of the market, what's the revenue?"
• Broadest possible market (no constraints)

Serviceable Available Market (SAM):

• The segment of TAM your company can realistically target
• Narrowed by geography, firmographics, demographics, or product constraints
• "Who can we actually reach with our product?"

Serviceable Obtainable Market (SOM):

• The portion of SAM you can realistically capture
• Accounts for competition, market constraints, go-to-market capacity
• "What can we capture in the next 1-3 years?"

Why This Works

• Top-down validation: TAM → SAM → SOM ensures estimates are grounded in reality
• Investor-friendly: Standard framework VCs and execs understand
• Citation-backed: Real data sources (Census, Statista, World Bank) add credibility
• Adaptive: Questions adjust based on context (B2B vs. B2C, US vs. global, etc.)

Anti-Patterns (What This Is NOT)

• Not a single-number guess: "The market is $10B" without supporting data
• Not static: Markets evolve—reassess annually
• Not a substitute for customer validation: Market size ≠ product-market fit

When to Use This

• Pitching to investors or execs (need market size in deck)
• Validating product ideas (is the market big enough?)
• Prioritizing product lines (which has bigger opportunity?)
• Setting growth targets (what's realistic to capture?)

When NOT to Use This

• For internal tools with captive users (no external market)
• Before defining the problem (market sizing requires clear problem space)
• As the only validation (pair with customer research)

Facilitation Source of Truth

Use workshop-facilitation as the default interaction protocol for this skill.

It defines:

• session heads-up + entry mode (Guided, Context dump, Best guess)
• one-question turns with plain-language prompts
• progress labels (for example, Context Qx/8 and Scoring Qx/5)
• interruption handling and pause/resume behavior
• numbered recommendations at decision points
• quick-select numbered response options for regular questions (include Other (specify) when useful)

This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.

Application

Use template.md for the full fill-in structure.

This interactive skill asks up to 4 adaptive questions, offering enumerated context-aware options at each step. The agent adapts questions based on previous responses.

Step 0: Gather Context (Before Questions)

Agent suggests:

Before we begin, it's helpful to have product context. If available, please share:

For Your Own Product:

• Website copy (homepage, product pages, value prop statements)
• Marketing emails or landing pages
• Product descriptions or positioning statements
• Case studies or customer testimonials
• Sales deck or pitch materials

If You Don't Have a Product Yet:

• Find a similar or adjacent product (competitor or analog)
• Copy their website homepage, product description, or landing page
• We'll use this as a reference point for market sizing

You can paste this content directly, or we can proceed with a brief description.

Why this helps:

• Marketing materials already contain target audience, pain points, and value props
• Analyzing real content (yours or competitors') grounds the analysis in reality
• You can benchmark against similar products' market positioning

Optional Helper Script (Deterministic Math)

If you already have population and ARPU numbers (or a TAM estimate), you can run a deterministic helper to compute TAM/SAM/SOM and generate a Markdown table. This script does not fetch data or write files.

python3 scripts/market-sizing.py --population 5400000 --arpu 1000 --sam-share 30% --som-share 10%

Mode 3: Autonomous Research (when the user has no numbers)

When the user picks research mode — or arrives with a market and a decision but no data — run the sizing as an investigation under the autonomous-investigation protocol: ask at most the questions below that remain unanswered, show a 3-bullet search plan, then build the estimate bottom-up with every figure labeled Fact / Inference / Assumption and cited.

The bottom-up recipe (from the GEOINT/DEMOINT discipline in intelligence-collection-disciplines):

~~~ TAM: Establishment counts for the market (Census/NAICS, Eurostat/NACE, or the national equivalent for the geography) × employment/spend benchmarks (BLS, Eurostat, trade associations) (validate against two independent analyst reports; if they disagree by 3x, say so)

SAM: TAM filtered by your actual constraints: geography, segment, compliance requirements, tech prerequisites (technographics: who can buy you), vendor-registration eligibility where applicable

SOM: SAM × realistic capture rate derived from competitor public filings (their revenue ÷ their claimed customer count = deal size reality check) ~~~

Why bottom-up wins scrutiny: a top-down number ("2% of a $50B market") borrows someone else's denominator and hides every assumption inside it. Establishment counts × benchmarks shows the math, so a skeptical CFO can attack one assumption at a time instead of dismissing the whole slide. The capture-rate trick grounds SOM in what incumbents actually achieve rather than hope.

Research-mode output uses the same analysis structure below, with two additions: an evidence label on every figure, and an Assumptions to Validate list. When done, offer the standard next steps plus a re-run option — sizing built from statistics releases rots slowly but really; re-run annually per the fusion cadence.

Question 1: Problem Space

Agent asks: "Based on the context you've provided (or will describe), what problem space are you exploring for market sizing?"

Offer 4 enumerated examples (user can select by number or write custom):

1. B2B SaaS productivity — E.g., "Workflow automation for small business operations" (like Zapier, Integromat)
2. Consumer fintech — E.g., "Personal budgeting app for Gen Z users" (like Mint, YNAB)
3. Healthcare/telehealth — E.g., "Mental health support for remote workers" (like BetterHelp, Talkspace)
4. E-commerce enablement — E.g., "Payment processing for online sellers" (like Stripe, Square)