competitor-analysis
๐ฏSkillfrom assimovt/productskills
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.
Same repository
assimovt/productskills(18 items)
Installation
npx vibeindex add assimovt/productskills --skill competitor-analysisnpx skills add assimovt/productskills --skill competitor-analysis~/.claude/skills/competitor-analysis/SKILL.mdSKILL.md
More from this repository10
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.
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.
A product management skill for preparing and conducting user interviews that extract real insights instead of validation, built on The Mom Test and Y Combinator's Five Questions framework.
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.
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.
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.
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.
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.
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.
Positions products using April Dunford's Obviously Awesome framework through five sequential steps: identifying competitive alternatives, unique attributes, customer value, best-fit customers, and market category. Grounds positioning in what customers would actually do without your product rather than in features or aspirations.