fa-product-discovery
fa-product-discovery is a Product strategy & roadmaps skill for AI agents, published by frostaura in the ai.toolkit.gaia repository on GitHub. Skillspace rates fa-product-discovery C (71/100) and it passed the safety scan; the rating is based on static analysis only.
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.
- Grade
- C
- Quality
- 71 / 100
- Safety
- Passed
- GitHub stars
- 30
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 94 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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