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Start and scale networked products using Andrew Chen's "The Cold Start Problem" framework for network effects.
Passed 21 of 22 test cases with the skill, and 17 of 22 without it.
and 5 more in the full report
4 fixed17 passed either way1 still failing0 broke
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One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are running a standard conversion rate optimization test on a single-player e-commerce store product details page. Our baseline conversion rate is 5%, and we want to detect a minimum detectable effect (MDE) of 10% relative uplift at 80% statistical power and 5% significance alpha level. What standard statistical formula determines the required sample size per variant for a two-sample proportion Z-test?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies the sample size formula based on standard normal distribution quantiles ($Z_{\alpha/2}$ and $Z_{\beta}$) and pooled proportions. | Missed | Passed |
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A framework for starting and scaling products that live or die by network effects — marketplaces, social apps, messaging, and collaboration tools — distilled from Andrew Chen's The Cold Start Problem. Use it to launch products that are worthless until other users show up, to sequence growth network by network, and to navigate the five stages: the cold start, the tipping point, escape velocity, hitting the ceiling, and the moat.
Network effects start as a liability, not an asset. Value lives in connections between users, and on day one there are none — the same force that makes a dense network unstoppable makes an empty one useless. You don't escape by launching to a market; you escape by building one tiny, complete, self-sustaining network at a time, solving its hard side first, then tipping adjacent networks with a repeatable playbook until the market follows.
Goal: 10/10. Rate launch plans and growth strategies for networked products 0-10 against the principles below. Report the current score and the specific changes needed to reach 10/10.
Core concept: A networked product connects people with each other — buyers with sellers, creators with audiences, coworkers with coworkers — and becomes more valuable as the right people join. Network effects come in three distinct forms: the acquisition effect (the network pulls in its own new users), the engagement effect (more users make each session more valuable), and the economic effect (density improves monetization and unit economics). A product can be strong in one and weak in the others.
Why it works: Treating "network effects" as a single magic property hides where growth actually comes from and where it breaks. Metcalfe's law (value grows with n²) is an oversimplification — it counts nodes, not active, relevant connections, and a million scattered users can be worth less than five thousand in one dense community. Every large network is really a network of networks: Uber is hundreds of city-level markets, Slack is millions of team-sized networks. Density and quality of each sub-network beat raw user counts.
Key insights:
Applications:
| Context | Application | Example |
|---|---|---|
| Metric design | Replace totals with density measures | Track weekly active networks, not registered users |
| Growth diagnosis | Attribute growth to the three effects separately | Viral factor vs. session frequency vs. conversion, each per network |
| Strategy review | Map the product as a network of networks | A marketplace is one network per city-category pair |
test cases passed, vs 17 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.