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Market Sizing

Community
Mehdibargach
market-sizing

TAM/SAM/SOM estimation for any product or market. Takes a product/market description and returns structured market sizing with sources, assumptions, and confidence ranges.

Overview

PublisherMehdibargach
Repositoryclaude-code-pm-skills
Skill namemarket-sizing
Stars
97
Forks
38
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Mehdibargach on GitHub. Read the source before you install it.

Installation

Install the Market Sizing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Mehdibargach/claude-code-pm-skills/tree/main/skills/market-sizing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Mehdibargach/claude-code-pm-skills.git /tmp/claude-code-pm-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-pm-skills/skills/market-sizing .claude/skills/market-sizing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Market Sizing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Market Sizing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Market Sizing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Market Sizing

Produce a rigorous TAM/SAM/SOM estimate that a PM can use in a pitch deck, strategy doc, or investment memo. The goal is defensible numbers with transparent assumptions — not inflated vanity metrics.

Process

  1. Define the market — Restate the input as a clear market definition: what product category, who is the buyer, what problem is being solved. Distinguish between the broad market and the specific segment.
  2. Research data points — Use WebSearch to find industry reports, public filings, analyst estimates, census data, and any credible source. Prioritize recent data (last 2 years). Note every source.
  3. Calculate TAM (Total Addressable Market) — Use top-down (industry report size) AND bottom-up (number of potential customers x average revenue per customer) if possible. If both methods yield different numbers, show both and explain the gap.
  4. Calculate SAM (Serviceable Addressable Market) — Apply geographic, segment, and capability filters. Be specific about what's excluded and why.
  5. Calculate SOM (Serviceable Obtainable Market) — Estimate realistic market capture in the first 1-3 years. Factor in competition, go-to-market approach, and growth benchmarks from comparable companies.
  6. Stress-test assumptions — Provide a range (conservative / base / optimistic) for at least SOM.

Output Format

Market Definition

One paragraph: what market, who buys, what problem, geographic scope.

TAM (Total Addressable Market)

Top-down: $X based on [source]. Calculation: [show the math]. Bottom-up: $X based on [number of customers] x [ARPU]. Calculation: [show the math]. Selected TAM: $X — [explain which estimate you trust more and why].

SAM (Serviceable Addressable Market)

Filters applied:

  • Geographic: [filter]
  • Segment: [filter]
  • Other: [filter]

SAM: $X ([X%] of TAM). Calculation: [show the math].

SOM (Serviceable Obtainable Market)

ScenarioSOMAssumptions
Conservative$X...
Base$X...
Optimistic$X...

Methodology: [explain how you estimated capture rate — comparable company benchmarks, market share logic, etc.]

Key Assumptions

#AssumptionImpact if WrongConfidence
1......High/Med/Low
2......High/Med/Low

Sources

Numbered list of every source used, with URLs when available.

Confidence Level

One paragraph: overall confidence in these numbers (High / Medium / Low) and what would increase confidence (e.g., "primary research with 20 target buyers would validate the ARPU assumption").

Rules

  • Always show the math. Every number must trace back to a source or a calculation.
  • Never use a single data point without cross-referencing. If only one source exists, flag it as low confidence.
  • Bottom-up estimates are generally more trustworthy than top-down for niche markets. Say so when relevant.
  • SOM should be grounded in reality: comparable company growth rates, typical market penetration for the GTM model, etc. Do not just pick an arbitrary percentage of SAM.
  • Round numbers appropriately. Do not write "$4,237,891" — write "$4.2M". False precision erodes credibility.
  • Write in English.
  • Keep the entire output under 2 pages. Investors and execs skim — make every line count.
  • Be opinionated: if the market is too small to build a venture-scale business, say so. If the sizing depends on a shaky assumption, call it out.
  • If you cannot find reliable data for a critical input, flag it and provide a placeholder range rather than guessing.

Frequently asked questions

What does the Market Sizing AI skill do?

TAM/SAM/SOM estimation for any product or market. Takes a product/market description and returns structured market sizing with sources, assumptions, and confidence ranges.

Why use Market Sizing on TypingMind?

Because you install it once and use it with any model. Market Sizing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Market Sizing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mehdibargach/claude-code-pm-skills/tree/main/skills/market-sizing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Market Sizing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Market Sizing?

As many as you like. As long as a model supports skills, you can use Market Sizing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Market Sizing AI skill free?

It is published on GitHub by Mehdibargach. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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