tam-sam-som-calculator
Skill6 starsChanged 3 months agoApache-2.0View on GitHub
Calculate TAM, SAM, and SOM with explicit assumptions, methods, and caveats. Use when sizing a market for a product idea, business case, or executive review.
We split this skill into the rules it gives an agent
13 rules. 10 are common, 0 are distinctive, and 3 are very specific to this project.
10
Common
Most of the 4 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 54
Before defining the problem (market sizing requires clear problem space)
- Very specific: line 61
Use workshop-facilitation as the default interaction protocol for this skill.
- Very specific: line 75
Use template.md for the full fill-in structure.
- Common: line 85
Before we begin, it's helpful to have product context.
- Very specific: line 110
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 …
- Common: line 132
Tip: If you provided website copy or marketing materials, the agent can extract the problem space from phrases like:
- Common: line 158
If user selected B2B SaaS (Question 1, Option 1) → Emphasize US/EU markets (mature SaaS adoption)
- Common: line 159
If user selected Consumer fintech (Question 1, Option 2) → Mention emerging markets (higher mobile adoption)
- Common: line 181
If Question 1 = Consumer fintech, offer consumer segments (e.g., "Gen Z 18-25," "Millennials 25-40")
- Common: line 182
If Question 1 = Healthcare, offer segments (e.g., "Primary care physicians," "Therapists/counselors")
- Common: line 207
After collecting responses, the agent generates a structured analysis:
- Common: line 339
Symptom: Only dollar amounts, no customer counts
- Common: line 343
Fix: Always include population (e.g., "1.2M businesses" or "60K customers in Year 1").
“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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.
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%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):
- B2B SaaS productivity — E.g., "Workflow automation for small business operations" (like Zapier, Integromat)
- Consumer fintech — E.g., "Personal budgeting app for Gen Z users" (like Mint, YNAB)
- Healthcare/telehealth — E.g., "Mental health support for remote workers" (like BetterHelp, Talkspace)
- E-commerce enablement — E.g., "Payment processing for online sellers" (like Stripe, Square)
Or write your own problem space description based on the marketing materials you shared.
Tip: If you provided website copy or marketing materials, the agent can extract the problem space from phrases like:
- "We help [target] solve [problem]"
- "The #1 solution for [use case]"
- Customer pain points in testimonials or case studies
User response: [Selection or custom description]
Question 2: Geographic Region
Agent asks: "What geographic region are you targeting?"
Offer 4 enumerated options (adapted based on problem space):
- United States — Best for detailed Census Bureau data, BLS stats, robust industry reports
- European Union — Use Eurostat, local statistical agencies; note GDPR/compliance considerations
- Global — World Bank, IMF data; broader but less granular
- Specific country/region — E.g., "Canada," "Southeast Asia," "Latin America"
Or specify your own region.
User response: [Selection or custom]
Adaptation logic:
- If user selected B2B SaaS (Question 1, Option 1) → Emphasize US/EU markets (mature SaaS adoption)
- If user selected Consumer fintech (Question 1, Option 2) → Mention emerging markets (higher mobile adoption)
Question 3: Industry/Market Segments
Agent asks: "What specific industry or market segments does this problem space relate to?"
Offer 4 enumerated options (adapted based on problem space + geography):
Example (if Question 1 = B2B SaaS, Question 2 = US):
- SMB services sector — 5.4M businesses, $1.2T revenue (US Census, 2023)
- Professional services (legal, accounting) — 1.1M firms, $850B revenue (IBISWorld, 2023)
- Healthcare providers — 900K practices, $4T industry (BLS, 2023)
- Tech/software companies — 500K firms, $1.8T revenue (Statista, 2023)
Or describe your own industry segment.
User response: [Selection or custom]
Adaptation logic:
- If Question 1 = Consumer fintech, offer consumer segments (e.g., "Gen Z 18-25," "Millennials 25-40")
- If Question 1 = Healthcare, offer segments (e.g., "Primary care physicians," "Therapists/counselors")
Question 4: Potential Customers (Demographics/Firmographics)
Agent asks: "Who are the potential customers affected by this problem?"
Offer 4 enumerated options (adapted based on previous answers):
Example (if Question 1 = B2B SaaS, Question 3 = SMB services sector):
- SMBs with 10-50 employees — 1.2M businesses, $400B revenue (Census Bureau, 2023)
- SMBs with 50-250 employees — 600K businesses, $800B revenue (Census Bureau, 2023)
- Solo entrepreneurs/freelancers — 3.5M self-employed, $200B revenue (BLS, 2023)
- Service businesses with online presence — 2M businesses, $600B e-commerce (Statista, 2023)
Or describe your own customer segment (firmographics, demographics, income, etc.).
User response: [Selection or custom]
Output: Generate TAM/SAM/SOM Analysis
After collecting responses, the agent generates a structured analysis:
# TAM/SAM/SOM Analysis
**Problem Space:** [User's input from Question 1]
**Geographic Region:** [User's input from Question 2]
**Industry/Market Segments:** [User's input from Question 3]
**Potential Customers:** [User's input from Question 4]
---
## Total Addressable Market (TAM)
**Definition:** The total market demand if you captured 100% of potential customers in the problem space.
**Population Estimate:** [Calculated from data sources]
- **Source:** [Citation, e.g., "US Census Bureau, 2023"]
- **Calculation:** [Show math, e.g., "5.4M SMBs × $1.2T revenue = $1.2T TAM"]
**Market Size Estimate:** $[X] billion/million
- **Source:** [Industry report citation]
- **URL:** [Clickable link to source]
---
## Serviceable Available Market (SAM)
**Definition:** The segment of TAM you can realistically target with your product (narrowed by geography, firmographics, product fit).
**Segment of TAM:** [User's narrowed segment from Question 4]
**Population Estimate:** [Calculated]
- **Source:** [Citation]
- **Calculation:** [Show math, e.g., "1.2M SMBs with 10-50 employees"]
**Market Size Estimate:** $[X] billion/million
- **Source:** [Citation]
- **URL:** [Link]
**Assumptions:**
- [List key assumptions, e.g., "Assumes 50% of SMBs have budget for automation tools"]
---
## Serviceable Obtainable Market (SOM)
**Definition:** The portion of SAM you can realistically capture in the next 1-3 years, accounting for competition and market constraints.
**Realistically Capturable Market:** [Agent's estimation based on market maturity, competition]
**Population Estimate:** [Calculated]
- **Source:** [Citation]
- **Calculation:** [Show math, e.g., "1.2M SMBs × 5% market share (Year 1) = 60K customers"]
**Market Size Estimate:** $[X] million
- **Assumptions:**
- [Competition assumption, e.g., "5 major competitors, market leader has 15% share"]
- [GTM assumption, e.g., "Sales capacity: 50 customers/month in Year 1"]
- [Conversion assumption, e.g., "10% trial-to-paid conversion"]
**Year 1-3 Projections:**
- **Year 1:** [X]K customers, $[X]M revenue (5% of SAM)
- **Year 2:** [X]K customers, $[X]M revenue (10% of SAM)
- **Year 3:** [X]K customers, $[X]M revenue (15% of SAM)
---
## Data Sources & Citations
- [Source 1: e.g., "US Census Bureau (2023). County Business Patterns. URL: census.gov"]
- [Source 2: e.g., "IBISWorld (2023). Professional Services Industry Report. URL: ibisworld.com"]
- [Source 3: e.g., "Statista (2023). SMB Software Market Size. URL: statista.com"]
- [Add all sources used]
---
## Validation Questions
1. **Does TAM align with industry reports?** [Compare to 3rd-party market research]
2. **Is SAM realistically serviceable?** [Can your GTM motion reach this segment?]
3. **Is SOM achievable given competition?** [Is 5-15% market share realistic in 3 years?]
---
## Next Steps
1. **Validate with customer interviews:** Does the problem resonate with target segment?
2. **Benchmark against competitors:** What market share do incumbents have?
3. **Refine SOM based on GTM capacity:** Can sales/marketing support this growth?
4. **Update annually:** Markets shift—reassess TAM/SAM/SOM yearly
---
**Would you like to refine any assumptions or explore a different segment?**Examples
See examples/sample.md for a full TAM/SAM/SOM analysis example.
Mini example excerpt:
**TAM:** 5.4M SMBs × $2,000 ARPA = $10.8B
**SAM:** 1.2M SMBs × $2,000 ARPA = $2.4B
**SOM:** 5% of SAM = $120MCommon Pitfalls
Pitfall 1: TAM Without Citations
Symptom: "The market is $50B" (no source)
Consequence: Can't defend the number to investors or execs.
Fix: Cite industry reports (Gartner, IBISWorld, Statista) with URLs.
Pitfall 2: SOM Equals SAM
Symptom: "SAM is $5B, SOM is $5B" (assuming 100% capture)
Consequence: Unrealistic projection—no market has zero competition.
Fix: SOM should be 1-20% of SAM in Year 1-3, accounting for competition.
Pitfall 3: No Population Estimates
Symptom: Only dollar amounts, no customer counts
Consequence: Can't build sales/marketing plans without knowing customer volume.
Fix: Always include population (e.g., "1.2M businesses" or "60K customers in Year 1").
Pitfall 4: Static Assumptions
Symptom: TAM/SAM/SOM calculated once, never updated
Consequence: Stale data as markets shift.
Fix: Reassess annually. Markets grow/shrink, competition changes, new data emerges.
Pitfall 5: Ignoring GTM Constraints
Symptom: "SOM is 50% of SAM in Year 1" (but no sales team)
Consequence: SOM isn't realistic given GTM capacity.
Fix: Ground SOM in GTM constraints (sales capacity, marketing budget, conversion rates).
References
Related Skills
skills/positioning-statement/SKILL.md— TAM/SAM/SOM informs "For [target]" segment sizeskills/problem-statement/SKILL.md— Problem space defines the marketskills/recommendation-canvas/SKILL.md— Market sizing informs business outcome projections
Optional Helpers
skills/tam-sam-som-calculator/scripts/market-sizing.py— Deterministic TAM/SAM/SOM calculator (no network access)
External Frameworks
- Steve Blank, The Four Steps to the Epiphany (2005) — Market sizing for startups
- Lean Startup methodology — Validate market size with experiments, not just desk research
Data Sources (For Citations)
- US: US Census Bureau, Bureau of Labor Statistics, IBISWorld, Statista
- Europe: Eurostat, local statistical agencies
- Global: World Bank, IMF, Gartner, Forrester
Dean's Work
- TAM/SAM/SOM Prompt Generator (multi-turn adaptive market sizing)
Skill type: Interactive
Suggested filename: tam-sam-som-calculator.md
Suggested placement: /skills/interactive/
Dependencies: None (standalone interactive skill)
Other versions6 projects use a version of this.
Word for word · 1
- vijaysuperpositionist/skills★ 0100% same
Edited · 4
- deanpeters/Product-Manager-Skills★ 7.7k76% same
- DIAL-Studio/pm-agent-harness-kit★ 1294% same
- agrivisionai/claude-skills★ 194% same
- ian-lawrence423/Claude-skills★ 099% same
Wrote their own version · 1
- nandanosql/all_in_one_pm_skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
Used alongsideProjects with it also use skill-authoring-workflow.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
ian-lawrence423Skillskill-authoring-workflow
Create or update repo skills with compliant metadata, dependency contracts, package sync, marketplace descriptions, and validation workflow.
Also in 3 projects that use this
In this repository281 other files it gives its agents.
- AGENTS.mdAGENTS.md
- skills/founder-productivity/salim/AGENTS.mdAGENTS.md
- skills/investments/ada/AGENTS.mdAGENTS.md
- skills/investments/khalil/AGENTS.mdAGENTS.md
- skills/product/vercel-composition-patterns/AGENTS.mdAGENTS.md
- skills/product/vercel-react-best-practices/AGENTS.mdAGENTS.md
- skills/product/vercel-react-native-skills/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- .cursor/rules/ceramic-design-system.mdcCursor rule
- agent-persona-builderSkill
- agent-prdSkill
- alignment-values-north-starSkill
- defuddleSkill
- draft-ndaSkill
- executive-onboarding-playbookSkill
- find-skillsSkill
- full-output-enforcementSkill
- grammar-checkSkill
- json-canvasSkill
- last30daysSkill
- market-segmentsSkill
- market-sizingSkill
- obsidian-basesSkill
- obsidian-cliSkill
- obsidian-markdownSkill
- pestel-analysisSkill
- porters-five-forcesSkill
- prioritization-frameworksSkill
- prioritizationSkill
- privacy-policySkill
- proofSkill
- review-resumeSkill
- role-switchSkill
- salimSkill
- skillshareSkill
- stakeholder-mapSkill
- swot-analysisSkill
- workshop-facilitationSkill
- 3-statement-modelSkill
- adaSkill
- adverse-selection-priorSkill
- auction-first-price-shadingSkill
- auction-winners-curse-haircutSkill
- audit-xlsSkill
- bayesian-reasoning-calibrationSkill
- bond-futures-basisSkill
- bond-relative-valueSkill
- capital-structure-optimizerSkill
- causal-inference-root-causeSkill
- clean-data-xlsSkill
- competitive-analysisSkill
- comps-analysisSkill
- cost-of-capital-estimatorSkill
- datapack-builderSkill
- dcf-modelSkill
- decision-matrixSkill
- deck-refreshSkill
- deliberation-debate-red-teamingSkill
- design-of-experimentsSkill
- The first 60 shown.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

