Diagnose business network effects and classify them into 16 NFX types.
npx skills add https://github.com/risingdream/roundtable --skill rt-currier
Please help me install this Agent Skill. Skill: rt-currier Source: https://github.com/risingdream/roundtable/tree/main/skills/growth/rt-currier Command: npx skills add https://github.com/risingdream/roundtable --skill rt-currier
It helps you determine whether a business has real network effects, identify the specific type, and assess defensibility instead of guessing based on vague moat claims.
Ask the AI to diagnose your product’s moat by identifying the network nodes, the network topology, the applicable network-effect type from Currier’s taxonomy, and the likely strength drivers.
High-intent search queries and answers about installing and using this skill.
To diagnose genuine network effects, identify the network nodes, map the network topology, and verify if value increases as more users participate. This process distinguishes real network effects from vague moat claims by applying a structured taxonomy-driven analysis workflow.
The NFX taxonomy classifies 16 network-effect types across direct, 2-sided, asymptotic, data, and social categories. Mapping your product's observed dynamics to these specific types helps identify what drives defensibility and strength in your platform economics.
Assess moat defensibility for a SaaS platform by mapping observed dynamics to specific network-effect types and evaluating strength factors. This distinguishes network effects from embedding, scale, and brand moats for accurate competitor and market-structure evaluation.
Common moat-killers that erode platform economics include multi-tenanting, disintermediation, cherry-picking, and congestion. Testing for these failure modes during a network-effect diagnosis reveals vulnerabilities that can weaken a product's defensibility.
Network effects differ from scale, brand, and embedding moats because value increases as more users participate. A proper defensibility assessment distinguishes these mechanisms and flags common misconceptions about what constitutes a true network effect.
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