What problem does it solve? Teams making build, invest, partner, or competitive decisions about consumer AI agents often rely on feature lists and marketing claims instead of verified evidence. This Skill produces a decision-specific market map that separates verified facts from inference and self-reported figures. ## Core Features & Use Cases - Evidence cards per product: Capture target users, interaction surfaces, task completion depth, permission models, privacy posture, pricing, and adoption signals with cited sources and confidence levels. - Structured scoring: Score eight decision-relevant dimensions (problem value, completion depth, reliability, permission safety, context advantage, distribution, monetization, defensibility) on a 0-5 scale with explicit NE handling for missing evidence. - Scenario testing and recommendation: Evaluate base case, platform shift, and trust shock scenarios, then deliver a recommendation with reversal conditions and a 30/90/180-day validation plan. - Use Case: A venture team evaluating whether to invest in a consumer AI companion startup uses this Skill to compare the product against messaging, browser, and wearable agents, identify crowded positions, and test how an OS-level bundling move by a platform provider would affect the thesis. ## Quick Start Use the consumer-agent-landscape skill to map the consumer AI agent market for an investment decision in voice-first companion products over the next 18 months.
