Agent skill

Discover Market Sizing

by product-on-purpose in product-on-purpose/pm-skills

Estimate market opportunity (TAM, SAM, SOM) using multiple sizing frameworks (top-down, bottom-up, comparable company, analogous market).

Apache-2.0Auto-check passedProduct & Project Management

Install Discover Market Sizing

skills CLI
$ npx skills add product-on-purpose/pm-skills --skill discover-market-sizing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install product-on-purpose/pm-skills discover-market-sizing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/product-on-purpose/pm-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/discover-market-sizing .claude/skills/discover-market-sizing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
discover-market-sizing
GitHub stars
716
Token cost
~3.3k tokens
SKILL.md length
1,662 words
Files
5 (incl. references)
Skills in repo
68
Repo updated
First seen
Licence
Apache-2.0

At a glance

Estimate market opportunity (TAM, SAM, SOM) using multiple sizing frameworks (top-down, bottom-up, comparable company, analogous market).

  • Works in 9 steps: Executive summary (3-5 sentences) → Market definition → Top-down sizing → …
  • Tasks that involve Market sizing
  • SKILL.md covers Identity, Core principle, When NOT to Use and Inputs, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Discover Market Sizing is an agent skill from product-on-purpose/pm-skills. 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.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `HISTORY.md`, `evals/trigger-fixtures.json` and `references/EXAMPLE.md`).

It sits in Product & Project Management, covering Market sizing and Startup and business strategy. The repository describes itself as: 68 plug-and-play, best-practice product management skills for AI agents: 30 Triple Diamond phase + 11 foundation + 12 utility + 15 tool (Foundation Sprint + Design Sprint). Plus… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Market sizing
  • Tasks that involve Startup and business strategy

Example prompts

  • “/discover-market-sizing”

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Executive summary (3-5 sentences)
  2. Market definition
  3. Top-down sizing
  4. Bottom-up sizing (when data permits)
  5. Multi-framework synthesis
  6. Sensitivity analysis
  7. Key assumptions (explicit)
  8. Confidence and limitations
  9. Next steps (recommendations)

What it can do on your machine

Read from SKILL.md and the folder at commit 1cef1a9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Discover Market Sizing loads about 3.3k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 121 tokens; SKILL.md has 1,662 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~121
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from product-on-purpose/pm-skills at commit 1cef1a9, republished under its Apache-2.0 licence (© product-on-purpose). 1,662 words, ~3,275 tokens.

Download SKILL.mdSave it as .claude/skills/discover-market-sizing/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
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
metadata.version
1.1.0
metadata.updated
2026-07-04
metadata.category
strategy
metadata.frameworks
triple-diamond, business-strategy
metadata.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:

LayerNumberMethodSource / AssumptionConfidence
TAM$XIndustry report YSource Z, page NHigh / Medium / Low
SAM$XFilter on TAMCustomer-fit % * geographic-fit %Medium
SOM$XMarket share assumptionZ% of SAM in 3 yearsMedium / 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# CustomersRevenue / CustomerSub-totalMethodSource
Segment AX$Y$X*YBottom-upSource / 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 variedLowMidHigh
Market growth rate5% (TAM = $X)10% (TAM = $Y)15% (TAM = $Z)
Market share captured1% (SOM = $A)5% (SOM = $B)10% (SOM = $C)
7. Key assumptions (explicit)

List every assumption used, with:

  • The assumption text
  • The source or rationale
  • Confidence (high / medium / low)
  • What changes if it is wrong (sensitivity link)
8. Confidence and limitations
  • Where is the analysis most/least confident?
  • What would improve confidence (specific research that could be done)?
  • What is the analysis NOT addressing (e.g., competition, time-to-market, regulatory)?
9. Next steps (recommendations)
  • If proceeding with this opportunity, what is the next discovery work?
  • What threshold of conviction is needed to justify investment?
  • What research would close the largest remaining unknown?

Refusal protocols

You refuse to produce numbers without bounded sources. Specifically:

  1. Unbounded fabrication. If the user provides no inputs and no constraints, you refuse: "I cannot size this market without source data or explicit assumptions. Please provide either (a) an industry report or market figure to anchor the analysis, (b) bottom-up unit-economic inputs (target customer count + revenue per customer), or (c) explicit assumptions you want me to use with sensitivity ranges."

  2. Missing scope definition. If the market definition is ambiguous (e.g., "the AI market"), you refuse: "The market needs a precise boundary. 'The AI market' could mean training infrastructure ($X), AI-powered SaaS ($Y), AI-augmented services ($Z), or all of the above. Please specify which slice you want sized."

  3. Implausible confidence requests. If the user asks for a "definitive" or "single" number, you refuse the framing: "Market sizing is inherently a range, not a point estimate. I can produce a range with confidence labels, but stating a single 'definitive' number would misrepresent the certainty. Want me to produce a central estimate with low/high bounds instead?"

  4. Compliance with hand-wavy sources. If the user provides a source that is actually a tweet, a blog post without citations, or "I heard at a conference", you flag it: "The source you provided does not support the figure cited. I will use it as an assumption but flag it as Low confidence. If you have a primary source, share it."

  5. Misuse of TAM as the sales-projection number. If the user expects TAM to be a revenue projection, you flag: "TAM is total addressable demand if 100 percent of customers bought, which is unrealistic. Revenue projections should be derived from SOM and grow over time. TAM is the upper bound of the opportunity, not the projection."

Show full SKILL.md (564 more words)Show less

Sources and references

When sizing claims rest on external data:

  • Cite the source publication name, year, page number where possible
  • For consultancy reports (Gartner, McKinsey, Forrester, IDC), note publication date and methodology if known
  • For company financial filings (10-K, earnings calls), cite the report and section
  • For statistical agencies (BLS, Eurostat, etc.), cite the dataset and methodology
  • For surveys, note sample size, methodology, and the entity that conducted the survey

Source-calibrated confidence: assign confidence based on source quality, not blanket-label all web-fetched figures as Low:

  • High: government statistical agencies, company financial filings (10-K, earnings), established industry bodies with primary methodology
  • Medium: established research firms (Gartner, IDC, Forrester) with dated reports; industry associations
  • Low: secondary aggregator sites, blog posts with uncited figures, undated estimates

Proactive fetch recommendation: before proceeding, evaluate what the user has provided. If the inputs would produce Low-confidence results throughout, recommend whether fetching additional sources would materially improve the output and suggest a specific approach (e.g., "your SAM estimate would improve significantly with a public market report on this category; want me to search for one?"). You may use web search if available to verify or supplement source data. You may NOT invent sources.

Common patterns

B2B SaaS sizing
  • TAM: total addressable spend (e.g., total enterprise IT spend on the relevant category)
  • SAM: filter by target company size, industry, geography
  • SOM: market share assumption, often 1-10 percent of SAM in 3 years
  • Bottom-up: target customer count (e.g., 50,000 mid-market companies) x ACV (e.g., $50K/year)
Consumer subscription sizing
  • TAM: total addressable consumers x annual spending
  • SAM: filter by demographic, geography, market readiness
  • SOM: market share assumption, often 0.1-5 percent depending on category maturity
  • Bottom-up: addressable user count x ARPU (or LTV / churn-adjusted)
Marketplace / two-sided sizing
  • TAM: total GMV (gross merchandise volume) in the addressable market
  • SAM: filter by category, geography, transaction type
  • SOM: take rate x GMV captured
  • Bottom-up: buyer count x average order value x order frequency
Quick estimate mode

When the user needs a directional TAM/SAM/SOM for a board slide or early investment case and does not have primary sources, use quick-estimate mode:

  • Accept explicit assumptions instead of cited sources
  • Label every figure Low or Medium confidence
  • Widen all sensitivity bands
  • Front-load the output: "This is a quick estimate based on stated assumptions. For investment-case use, replace assumptions with cited sources."

Quick-estimate mode still refuses unbounded fabrication. The difference is it accepts user-stated rough assumptions rather than demanding primary-source citations.

Cross-skill composition

  • Output of this skill feeds into: develop-solution-brief and deliver-prd (sizing informs scope and the investment case)
  • Inputs to this skill often come from: discover-competitive-analysis (market and competitor context) and discover-interview-synthesis (qualitative signal that informs sizing assumptions)
  • Adversarial review via: utility-pm-critic (use proactively to challenge assumptions, source quality, and confidence labels)

Output Format

Use the template in references/TEMPLATE.md to structure the output. See references/EXAMPLE.md for a complete worked example showing multi-framework synthesis.

Quality Checklist

Before finalizing, verify:

  • Market definition states an explicit boundary (what is in, what is out)
  • At least two sizing frameworks were run (top-down + bottom-up where data permits)
  • Multi-framework synthesis explains convergence and divergence, not just an average
  • Every dollar figure traces to a cited source, a stated assumption, or a sensitivity range
  • Confidence labels are source-calibrated, not blanket Low
  • Sensitivity analysis shows how the estimate moves under key assumptions
  • TAM is not presented as a revenue projection

Cross-references

  • Template: references/TEMPLATE.md
  • Examples: references/EXAMPLE.md + library samples in library/skill-output-samples/discover-market-sizing/

© product-on-purpose, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in skills/discover-market-sizing of product-on-purpose/pm-skills.

  • SKILL.md
  • HISTORY.md
  • evals/trigger-fixtures.json
  • references/EXAMPLE.md
  • references/TEMPLATE.md

Open the folder on GitHubat commit 1cef1a9

Compare with similar skills

Discover Market Sizing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Discover Market Sizing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Discover Market Sizing this skillproduct-on-purpose/pm-skills716—~3.3kAutomated safety check: PassApache-2.0
TAM SAM SOM Calculatordeanpeters/Product-Manager-Skills7.2k1 repos~4.8kAutomated safety check: PassCustom licence
Market Sizing Analysisnicepkg/auto-company19511 repos~3.1kAutomated safety check: PassNone
Market Sizing Frameworksslgoodrich/agents139—~3.3kAutomated safety check: PassCustom licence
Market Sizing Analysiswshobson/agents40k1 repos~620Automated safety check: PassMIT
Startup Business Analyst Market Opportunityaiskillstore/marketplace4337 repos~1.8kAutomated safety check: NotesNone

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Questions about Discover Market Sizing

What does Discover Market Sizing do?

Estimate market opportunity (TAM, SAM, SOM) using multiple sizing frameworks (top-down, bottom-up, comparable company, analogous market). Discover Market Sizing is an agent skill from product-on-purpose/pm-skills. Estimate market opportunity (TAM, SAM, SOM) using multiple sizing frameworks (top-down, bottom-up, comparable company, analogous market).

When should I use Discover Market Sizing?

Discover Market Sizing fits situations like: tasks that involve Market sizing; tasks that involve Startup and business strategy.

How do I install Discover Market Sizing in Claude Code?

Run `npx skills add product-on-purpose/pm-skills --skill discover-market-sizing -a claude-code`. Or copy the skill folder (skills/discover-market-sizing in product-on-purpose/pm-skills) into .claude/skills/discover-market-sizing in your project. Claude Code loads it when a task matches its description.

How do I install Discover Market Sizing in Codex?

Run `npx skills add product-on-purpose/pm-skills --skill discover-market-sizing -a codex`. Or copy the skill folder (skills/discover-market-sizing in product-on-purpose/pm-skills) into .agents/skills/discover-market-sizing in your project. Codex loads it when a task matches its description.

Can I use Discover Market Sizing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add product-on-purpose/pm-skills --skill discover-market-sizing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/discover-market-sizing, .gemini/skills/discover-market-sizing, .github/skills/discover-market-sizing and .opencode/skills/discover-market-sizing in your project.

What does Discover Market Sizing need to run?

SKILL.md names no scripts, command-line tools or credentials: Discover Market Sizing is instructions for the agent only.

Does Discover Market Sizing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Discover Market Sizing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Discover Market Sizing use?

Discover Market Sizing is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Discover Market Sizing use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.

What are the alternatives to Discover Market Sizing?

Skills that share tags, products or a category with Discover Market Sizing: TAM SAM SOM Calculator (deanpeters/Product-Manager-Skills, 7.2k stars), Market Sizing Analysis (nicepkg/auto-company, 195 stars), Market Sizing Frameworks (slgoodrich/agents, 139 stars) and Market Sizing Analysis (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Discover Market Sizing?

product-on-purpose (a GitHub organization) maintains it in product-on-purpose/pm-skills, which has 716 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on October 8, 2026.

Source: product-on-purpose/pm-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.