Help users identify unique distribution advantages and master the lifecycle of acquisition channels to build a sustainable engine for growth and retention.
日本語の概要は準備中です。原文の説明を表示しています。
Help users de-risk new product bets by testing core assumptions, building low-fidelity prototypes, and gathering high-signal evidence before investing heavy engineering resources.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Stop building things people don't want by moving from opinion to evidence-based development.
Help the user with idea validation using insights from 31 guests and posts across Lenny's Podcast and Newsletter.
Bangaly Kaba: "Someone says, 'Hey, you know what? This would be great to build.' And you go pull data to go justify why that would be great to build. Call that identify, justify, execute. First you have to really understand from first principles what is actually going on."
True de-risking starts with understanding the core problem from first principles before looking for data to justify a specific solution.
Grant Lee: "We would have an idea in the morning, come up with some sort of functional prototype, recruit a bunch of people that are legitimately good prospective users, but have zero skin in the game, ship fast so people can start playing with it. In the afternoon, we're already running pretty full scale experiment."
Build functional prototypes within hours of conceiving an idea to maintain speed and identify usability flaws before wasting development cycles.
Gustaf Alstromer: "If I drill down what makes companies fail, it's quite simple. It's just like they don't talk to users, which means they don't find product market fit. And if they don't find product market fit, nothing else really matters."
Achieve product-market fit by talking to customers directly and immediately to learn if you are building something people actually want.
Oji Udezue: "So the zone of benefit works as a framework because people will not pay for things that don't either really shrink the workflow that they're doing or doesn't give them superpower. So the same amount of time, but twice as much output. But the most important thing is that it has to be noticeable."
A problem is only worth building for if your solution offers a visible and massive compression of the existing customer workflow.
See references/artifacts.md for the full list with details.
For all 33 sourced insights from 31 guests, see references/guest-insights.md
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概要と使いどころ
Help users identify unique distribution advantages and master the lifecycle of acquisition channels to build a sustainable engine for growth and retention.
日本語の概要は準備中です。原文の説明を表示しています。
Help users build functional product prototypes from natural language or visual mocks using AI coding tools. This skill enables product leaders to bypass engineering bottlenecks and validate ideas through hands-on building.
日本語の概要は準備中です。原文の説明を表示しています。
Help users build robust infrastructure for measuring, monitoring, and iterating on AI product performance using human, code-based, and LLM-as-a-judge methodologies.
日本語の概要は準備中です。原文の説明を表示しています。
Help users interact with probabilistic models by designing interfaces that manage fluidity, intent, and agency while maintaining trust and control.
日本語の概要は準備中です。原文の説明を表示しています。
Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data.
日本語の概要は準備中です。原文の説明を表示しています。
Help users convert massive volumes of qualitative and quantitative feedback into high-confidence product decisions through rigorous synthesis and internal immersion.
日本語の概要は準備中です。原文の説明を表示しています。