Product Solution Evaluator
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About this skill
What It Solves
Product reviews often start with optimism but lack repeatable decision criteria. A plan may look complete while its target user is unclear, metrics are unmeasurable, or resources are mismatched. This skill turns solution review into a structured evaluation: first check whether the problem is real, then whether the MVP forms a closed loop, and finally decide whether to proceed, pilot, validate more, or stop.
How It Works
It applies Idea Filtering, key assumptions, a 100-point scoring model, and P0/P1/P2/P3 risk levels. Key capabilities include:
- User value: whether the pain is frequent, severe, and clearly scoped
- Business and delivery: whether ROI, cost, resources, and dependencies hold up
- AI checks: data quality, evaluation, human fallback, hallucination, and permission risks
- Engineering review: components, APIs, state machines, tracking, and acceptance cases
When inputs are thin, it asks targeted questions first and marks confidence as high, medium, or low, rather than packaging weak assumptions as confident decisions.
Use Cases
- Review an AI customer-service PRD before scheduling, identifying P0 risks and acceptance cases
- Assess a self-built internal tool against open-source or platform alternatives before greenlighting build, buy, or integrate
- Check a growth campaign for CAC, LTV, refunds, complaints, and fraud controls before scaling spend
- Turn an MVP proposal into a gray-release experiment, metric plan, and validation checklist
Best For
- Product managers preparing a PRD for schedule need to verify MVP scope, observable metrics, and acceptance cases
- Tech leads assessing AI features need checks for data, output quality, human fallback, and safety risks
- Growth or monetization owners need review of CAC, LTV, refunds, complaints, and fraud-control loops
- Product or engineering leads choosing internal tools need Build, Buy, Partner, Integrate, or Abandon guidance
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