> ## Documentation Index
> Fetch the complete documentation index at: https://docs.startupsuperpowers.io/llms.txt
> Use this file to discover all available pages before exploring further.
# market-research
> A grounded, one-page picture of the market you're entering — real, named, and priced, or not
`/market-research` answers "what's the market opportunity?" with evidence instead of a hand-wavy TAM slide: is the market real and named, who's in it, how buyers decide, and what people actually pay.
## When to reach for it
* "How big is this market — is it even real?"
* "Who are the buyers and how do they make decisions?"
* "What do people typically pay for something like this?"
* "What are the tailwinds and headwinds in this space?"
* "The research is getting stale — refresh it"
## First-time research
The initial run frames its output up front — a one-page market brief you could show an advisor or investor — and asks one scope question (segments, geographies, or pricing models to focus on or skip; "just go broad" is a fine answer). It detects whether your idea is **B2B or B2C** from your project definition and researches accordingly:
* **B2B** leans on analyst coverage, G2/Capterra category data, and buying-process signals — who evaluates, who signs, what the criteria are.
* **B2C** leans on communities first — Reddit, app stores, social — where community size and activity are themselves market signals.
Four dimensions get covered either way: market reality & size, buyer behavior, pricing norms, and trends. Findings come back as a short narrative, not a data dump, with an explicit "does this match your intuition?" beat — your pushback isn't discarded; it's logged as Open Questions to test later.
The goal is deliberately not a precise TAM figure. "The category has analyst coverage and established players" and "no formal sizing exists, but an active community suggests demand" are both honest, useful answers — and the file tells you which kind you got.
## Working with existing research
Once the file exists, the skill handles the rest inline:
* **Answer questions** from what's already researched — and offer a targeted top-up if a dimension is thin
* **Update sections** as you learn (propose → confirm → write, with `last_updated` bumped)
* **Refresh** when things feel stale — full or targeted re-run
* **Connect to hypotheses** — surface which findings support or challenge your current bets
## What it writes
| File | What's in it |
| ---------------------------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `startup/market-research.md` | The full living file: Market Overview, Customer Segments, Buying Behavior, Pricing Landscape, Trends, Key Sources, Open Questions |
| `startup/market-brief.md` | The condensed one-pager: The Market / Who's In It / What They Pay / Tailwinds — advisor/investor-ready |
| `startup/research/…` | Raw research output, kept for reference |
## Good to know
* Pricing data is sometimes inferred from competitor pricing rather than explicit category data — the file flags which kind you're looking at.
* **Open Questions** is where your contradicting intuition lands. It's not a failure bucket — it's your future interview questions.
* Findings feed forward deliberately: pricing norms shape willingness-to-pay hypotheses, buyer behavior shapes interview scripts, and a sharper segment triggers an offer to update your core Audience field.
## Related
The named players inside this market.
Where market findings become testable bets.