product-positioning

Guides AI agents in writing evidence-first Product Requirements Documents (PRDs) with concise scope, measurable outcomes, and P0/P1/P2 priority tiers, based on real product management frameworks like Shape Up and Mom Test.

A product management skill that converts raw user interview notes into atomic insights and patterns, helping PMs and founders distill qualitative research into actionable product decisions.

Creates outcome-based roadmaps using Now/Next/Later horizons instead of Gantt charts, organizing by problems to solve rather than feature lists, with Shape Up cycle planning methodology.

Guides hypothesis-driven experiment and A/B test design with proper methodology including hypothesis writing, sample size calculation, guardrail metrics, and pre-committed analysis plans.

AI agent skills for product management covering discovery, strategy, prioritization, and PRD writing. Each skill encodes a real PM framework (Mom Test, Shape Up, Obviously Awesome, Teresa Torres, RICE) as actionable instructions for user interviews, problem validation, competitor analysis, roadmaps, and more.

Evaluates product bets using Shape Up's appetite model and Bezos's Type 1/Type 2 decision framework. Helps classify decisions as reversible or irreversible, structure pitches with problem-appetite-solution-rabbit holes-no-gos, and assess expected value for resource allocation.

Analyzes competitive landscapes by building feature matrices, positioning maps, and strategic gap analyses to identify differentiation opportunities and understand where alternatives fail your target audience.

Prioritizes features and backlog items using RICE scoring (Reach, Impact, Confidence, Effort) combined with Linear's enablers-vs-blockers classification. Forces explicit tradeoffs with visible math rather than opinion-based ranking.

Validates whether a problem is worth solving before building anything by scoring it across four dimensions: frequency, intensity, existing workarounds, and willingness to pay. Provides a quantitative go/no-go decision framework with evidence requirements based on observed behavior rather than opinions.

Analyzes customer motivations using the Jobs-to-be-Done framework and Forces of Progress model. Helps structure job statements, map the four forces driving or resisting product adoption (push, pull, anxiety, habit), and identify trigger events that cause customers to switch products.