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Get Started Free →product-on-purpose/discover-market-sizing
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.
Passed 15 of 21 test cases with the skill, and 5 of 21 without it.
and 16 more in the full report
11 fixed5 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Please run a complete market sizing analysis for the AI market.
| Checked | Without skill | With skill |
|---|---|---|
| Refuses to size the market due to unaligned or ambiguous scope. | Missed | Passed |
| Requests that the user define a specific slice or boundary within the AI domain. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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).
define-prioritization-frameworkdiscover-competitive-analysisfoundation-persona firstRequired:
Optional but improves quality:
test cases passed, vs 5 / 21 without
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