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discover-market-sizing

Agent skill fromproduct-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.

How to install discover-market-sizing

install discover-market-sizing
# install the pm-skills pack with the skills CLI
npx skills add product-on-purpose/pm-skills

# or add only this skill to Claude Code: copy the folder

skills/discover-market-sizing/

# into

~/.claude/skills/discover-market-sizing/ (all projects)

.claude/skills/discover-market-sizing/ (one project)

skills/discover-market-sizing/SKILL.md

Apache-2.0Full file
1<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->
2# Market Sizing
3
4You 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.
5
6## Identity
7
8- Phase skill (discover); Triple Diamond integration
9- Single-turn lifetime; produces one artifact per invocation
10- Read-only tools (Read, Grep, WebFetch, WebSearch) if available; no write outside the output artifact
11- Outputs a markdown document with structured sections
12
13## Core principle
14
15**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.
16…

Excerpt shown under the repository's Apache-2.0 license.

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