| name | fa-product-discovery |
| description | Provides Stage 1 product-discovery guidance for consumer one-off / in-app-purchase products - trend scanning (Sensor Tower, AppMagic, data.ai, store charts, Steam tags/wishlists, TikTok/Reddit/Product Hunt), opportunity sourcing, demand-signal harvesting as the cheapest kill gate, competitor teardowns, persona + JTBD + WTP-by-segment, and bottoms-up TAM/SAM/SOM sized net-of-fee. Use to find one buildable, monetizable bet and exit with an Opportunity Thesis, unit-economics model v0, and a GO/NO-GO/PIVOT call. |
| license | MIT |
Gaia Product Discovery
Scope and when to use
Use this skill to scan the market and surface ONE buildable, monetizable
consumer bet. Scope is one-off + IAP/IAG monetization; B2B seat-based SaaS is out
of scope and auto-renewing passes are red-lined unless an explicit in-scope
decision is recorded.
Use when:
- a consumer-product goal needs trend, demand, and competitor evidence
- an opportunity must be sized net-of-fee before any concept work begins
- the coordinator needs a GO / NO-GO / PIVOT call backed by money signals
Do not use when:
- a concept is already selected and only needs shaping (use ideation)
- the product is B2B seat-based SaaS
Required inputs
- product goal, constraints, monetization lane, and success bar
- access to market-intel tools and store/charts data (or named proxies)
- prior-title priors when this is a sequel/follow-on (payer base, CRM, tooling)
Owned outputs
- an Opportunity Thesis: who, what job, why now, why pay, why us
- unit-economics model v0 (all inputs flagged assumption-vs-measured)
- the GO / NO-GO / PIVOT call with pre-registered demand thresholds
Core workflow
- Scan trends across market-intel (Sensor Tower / AppMagic / data.ai), store charts, Steam tags/wishlists, and TikTok/Reddit/Product Hunt; tag each as durable vs fad vs over-served and note the launch-window/seasonality.
- Source opportunities via review mining, frustration archaeology, monetization-gap, genre recombination, platform-capability arbitrage, and re-segmentation.
- Harvest demand signals as the CHEAPEST kill gate: keyword volume, wishlist velocity, fake-door taps, and social purchase-intent, scored against a pre-registered pass/fail BEFORE the data lands.
- Mine top-charts and RPD to confirm the segment actually spends money, not just attention.
- Tear down 3-6 direct competitors plus adjacents: map paywall placement and LLM-cluster 1-3 star reviews into addressable gaps.
- Build personas + JTBD (functional/emotional/social), anti-personas, and willingness-to-pay by segment.
- Size the market bottoms-up (TAM/SAM/SOM) NET of store fee; name the 15%/30% commission case explicitly.
- Initialize unit-economics model v0 and issue the GO / NO-GO / PIVOT call.
Demand-signal kill gate
- The cheapest kill is a demand test, not a build; spend cents before dollars.
- Pre-register the pass/fail threshold first, then gather the signal; never move the goalpost after taps land.
- A frustrated review crowd with no spend behind it is interest, not demand.
Anti-patterns
- do not size TAM/SAM/SOM on gross revenue or top-down hand-waving
- do not treat attention (views, upvotes) as willingness to pay
- do not skip the demand kill gate because the idea "feels obvious"
- do not advance a fad or over-served trend as if it were durable
- do not relabel a subscription opportunity as a repeated one-off
Handoff and downstream impact
- hand ideation the Opportunity Thesis, the personas/JTBD, and WTP-by-segment
- hand the money gate model v0 with every input labeled assumption-vs-measured
- name the loop-back: a NO-GO returns to trend scanning, a PIVOT re-sources
Completion checklist
- the Opportunity Thesis names who, what job, why now, why pay, why us
- demand thresholds were pre-registered and the result is recorded
- TAM/SAM/SOM is bottoms-up and net-of-fee with the 15/30 case named
- model v0 exists and the GO / NO-GO / PIVOT call is explicit
References
