Validates product ideas using Mom Test principles before building.
npx skills add https://github.com/ZawilecxD/trAInR --skill 10x-mom-test-zawilecxd
Please help me install this Agent Skill. Skill: 10x-mom-test Source: https://github.com/ZawilecxD/trAInR/tree/main/.cursor/skills/10x-mom-test Command: npx skills add https://github.com/ZawilecxD/trAInR --skill 10x-mom-test-zawilecxd
High-intent search queries and answers about installing and using this skill.
Validate a product idea by asking about past behavior instead of opinions. This Skill extracts your hypotheses, critiques weak assumptions, and produces an interview guide, survey, and go/no-go criteria based on what users actually did recently, not what they say they would do.
The Mom Test is a method for asking questions that avoid polite false positives. It focuses on recent real occurrences, current workarounds, and costs paid, rather than asking whether people like your idea or would use your product.
Replace hypothetical or approval-seeking questions with behavior-based ones. Instead of "Would you use this feature?", ask "Walk me through the last time you faced this problem." The Skill classifies each draft question as keep, rewrite, or drop with reasoning.
Yes, the Skill works for any idea type including consumer apps, B2B SaaS, features, internal tools, and service businesses. It accepts inline ideas, tickets, incident notes, PRDs, roadmaps, or opportunity maps as input.
Do not build when interviewees cannot recall recent occurrences of the problem, when no measurable cost in time or rework exists, or when an existing tool or manual process already handles it well enough. The Skill defines explicit proceed, narrow, and stop thresholds.
AssamirValidates product ideas using Mom Test principles to produce interview guides, surveys, and go/no-go criteria.
phrazzldConvert backlog items into prioritized GitHub issues with acceptance criteria.
coreymayprayPlan and conduct user research studies for design guidance.
autopus-aiStructures product discovery with opportunity-solution trees, assumption mapping, and experiment design.
