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Market Sizing — TAM/SAM/SOM Calculator

Name: Market Sizing — TAM/SAM/SOM Calculator
Author: 1kalin

作者 1kalin · GitHub ↗ · v1.0.0

cross-platform ✓ 安全检测通过

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在 OpenClaw 中安装

/install afrexai-market-sizing

功能描述

Calculate defensible TAM, SAM, and SOM market sizes using top-down and bottom-up methods with source citations for pitches and business cases.

使用说明 (SKILL.md)

Market Sizing — TAM/SAM/SOM Calculator

Build defensible market sizing for any product, pitch deck, or business case. Top-down and bottom-up methodologies combined.

What You Get

TAM (Total Addressable Market) — entire market if you had 100% share
SAM (Serviceable Addressable Market) — segment you can actually reach
SOM (Serviceable Obtainable Market) — realistic capture in 12-36 months
Bottom-up validation — unit economics × reachable customers
Source citations — government data, industry reports, public filings

How to Use

Tell me your product/service and target customer. I'll build the full sizing.

Example prompts:

"Size the market for AI-powered contract review for mid-market law firms in the US"
"TAM/SAM/SOM for a SaaS helpdesk targeting e-commerce brands doing $1M-$50M revenue"
"Market size for automated bookkeeping for UK SMBs"

Methodology

Top-Down

Start with total industry revenue (cite source)
Filter by geography, segment, company size
Apply technology adoption rates
Result = SAM

Bottom-Up

Count reachable customers (databases, directories, LinkedIn)
Multiply by realistic ACV (annual contract value)
Apply conversion rates at each funnel stage
Result = SOM

Triangulation

Compare top-down and bottom-up. If they're within 2-3x of each other, the sizing holds. If wildly different, investigate assumptions.

Output Format

## Market Sizing: [Product/Service]

### TAM — $X.XB
[Total market calculation with sources]

### SAM — $XXM
[Filtered by geography + segment + tech adoption]

### SOM (12-month) — $X.XM
[Bottom-up: customers × ACV × conversion]

### Key Assumptions
- [Assumption 1 + source]
- [Assumption 2 + source]

### Risks to Sizing
- [What could make this smaller]
- [What could make this bigger]

When to Use This

Pitch decks and investor presentations
Go-to-market strategy planning
New product feasibility analysis
Board presentations and business cases
Competitive positioning

Pro Tip

Most founders oversize their TAM and undersize their SOM. Investors see through inflated numbers instantly. A tight, well-sourced $50M SAM beats a hand-wavy $10B TAM every time.

Need the full business context pack for your industry? Browse AfrexAI Context Packs — pre-built agent configs for Fintech, Healthcare, Legal, SaaS, and 6 more verticals ($47 each).

Calculate what AI automation could save your business: AI Revenue Leak Calculator

安全使用建议

This is an instruction-only market-sizing skill that appears coherent with its stated purpose. Because it may recommend using external datasets (e.g., directories or LinkedIn), do not grant your agent access to private company databases, personal accounts, or API keys unless you explicitly trust the downstream tool and understand what data will be accessed. Verify all cited sources and assumptions before using numbers in investor materials. Note the README contains promotional links to paid "context packs" — those are external commercial resources, not required for the skill to work.

功能分析

Type: OpenClaw Skill Name: afrexai-market-sizing Version: 1.0.0 The skill bundle is benign. The `SKILL.md` provides clear, task-oriented instructions for an AI agent to perform market sizing, including methodology and output format, without any attempts at prompt injection, unauthorized command execution, or data exfiltration. The `README.md` and `_meta.json` files contain standard metadata and installation instructions. External links in both `SKILL.md` and `README.md` point to `afrexai-cto.github.io` for related products, which appear to be legitimate marketing links and not instructions for the agent to execute malicious actions.

能力评估

✓ Purpose & Capability

Name and SKILL.md describe TAM/SAM/SOM calculations and triangulation; the instructions and examples align with that purpose and do not request unrelated capabilities.

ℹ Instruction Scope

Instructions are scoped to market-sizing methodology (top-down, bottom-up, triangulation, and citation of public sources). One caution: the bottom-up step mentions counting reachable customers via "databases, directories, LinkedIn," which implies the agent may ask to access external datasets or accounts — the skill itself does not supply credentials or code to do so. If you grant the agent access to private company databases or a LinkedIn session, that would be outside this skill and deserves separate review.

✓ Install Mechanism

No install spec and no code files — instruction-only. Nothing is downloaded or written to disk by the skill itself.

✓ Credentials

The skill declares no environment variables, credentials, or config paths. There are no disproportionate or unexplained secret requests.

✓ Persistence & Privilege

always is false and default autonomy settings apply. The skill does not request persistent system changes or elevated privileges.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install afrexai-market-sizing
安装完成后，直接呼叫该 Skill 的名称或使用 /afrexai-market-sizing 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.0

- Initial release of the Market Sizing — TAM/SAM/SOM Calculator skill. - Supports both top-down and bottom-up market sizing methodologies. - Calculates TAM, SAM, and SOM with clear definitions and outputs. - Provides source citations from government data, industry reports, and public filings. - Offers a step-by-step methodology and prompt examples for easy use. - Includes best practices and common pitfalls for defensible market sizing.

元数据

Slug afrexai-market-sizing

版本 1.0.0

许可证 —

累计安装 1

当前安装数 1

历史版本数 1

常见问题