evaluating-trade-offs by refoundai/lenny-skills
npx skills add https://github.com/refoundai/lenny-skills --skill evaluating-trade-offs帮助用户使用来自 40 位产品负责人的框架和心智模型,在相互竞争的选项之间做出更清晰的决策。
当用户请求帮助评估权衡取舍时:
Alex Komoroske:"是 1,000 还是 1,001 其实并不重要,谁在乎呢?它比替代方案大几个数量级,所以它更好。" 在不确定的环境中,不要浪费精力追求虚假的精确度——专注于一个选项是否显著更好,而不是略微更好。
Annie Duke:"如果你今天不会开始做这个,那就意味着你未来投入的一切都是实际的浪费。" 在评估是否继续一个项目时,完全忽略沉没成本。唯一相关的问题是,以你今天的认知是否会开始这项努力。
Anuj Rathi:"大多数实验都应该是思想实验。它们甚至不应该被尝试,因为它们显然会失败。" 不要默认"让我们试试看"——严谨的事前思考可以在消耗工程资源之前就淘汰掉糟糕的想法。
Graham Weaver:"你想要的一切都在先变差的另一面。" 有意义的改变需要接受短期的衰退。问问你 5 年后的自己会想要什么,而不是什么能让明天更轻松。
Bob Baxley:"信条实际上是决策工具……你为自己制定了一个规则。" 识别你的团队反复进行的辩论,并创建一个信条来一次性决定方向。好的信条足够具体,以至于有人可以合理地提出相反的观点。
Ronny Kohavi:"这是我们通过电子邮件产生的收入。这是我们正在损失的长期价值。权衡是什么?" 为负面的用户行为(退订、流失)分配美元价值,以便与短期收益进行客观的权衡。
Nicole Forsgren:"确定对你最重要的标准……给每个标准打分,然后相乘计算。" 创建一个决策电子表格,将选项作为行,加权标准作为列。这个过程通常在计算完成之前就揭示了答案。
Geoff Charles:"要非常清楚地说明权衡取舍……将这些权衡取舍呈现给你的领导团队。这是我们要做的,这是我们不做的。" 清晰地传达团队不做什么,就像传达他们做什么一样。提供一个选项"菜单"来促使做出决定。
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John Cutler:"有些人只局限于'能够'。他们非常务实……另一些人则问'我们在这里应该做什么?'" 不要让可行性约束主导战略思考。明确询问,如果技术债务不是问题,你应该做什么。
Julie Zhuo:"数据不是告诉你应该构建什么的工具……但它可以告诉你是否存在问题。" 使用数据来识别问题和差距,但依靠设计和直觉来创造解决方案。
Stewart Butterfield:"进行分析的成本是这么多。所以它注定是亏损的。" 评估用于分析决策的人时是否超过了改进可能带来的最大收益。
Ramesh Johari:"许多最具影响力的变革都会产生赢家和输家。" 在推出一个功能时,明确识别谁将受损,并判断赢家是否为生态系统提供了更多的净价值。
要查看来自 40 位嘉宾的全部 42 条见解,请参阅 references/guest-insights.md
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Help the user make clearer decisions between competing options using frameworks and mental models from 40 product leaders.
When the user asks for help evaluating trade-offs:
Alex Komoroske: "It doesn't really matter if it's 1,000 or 1,001, who cares? It's orders of magnitude larger than the alternative, and so it is better." Don't waste effort on false precision in uncertain environments - focus on whether one option is dramatically better, not marginally better.
Annie Duke: "If you wouldn't start this today, then that means that everything that you're putting into this going forward is the actual waste." When evaluating whether to continue a project, ignore sunk costs entirely. The only relevant question is whether you'd begin this effort with today's knowledge.
Anuj Rathi: "Most experiments should be thought experiments. They should not even be tried out because they're obviously going to fail." Don't default to "let's just try it" - rigorous upfront thinking eliminates weak ideas before they consume engineering resources.
Graham Weaver: "Everything you want is on the other side of worse first." Meaningful change requires accepting short-term decline. Ask what your 5-year future self would want, not what makes tomorrow easier.
Bob Baxley: "Tenets are really decision-making tools... you sort of make a rule for yourself." Identify debates your team has repeatedly and create a tenet to decide the direction once. Good tenets are specific enough that someone could reasonably argue the opposite.
Ronny Kohavi: "Here's the money that we generate from the emails. Here's the money that we're losing on long-term value. What's the trade-off?" Assign dollar values to negative user actions (unsubscribes, churn) to make objective trade-offs against short-term gains.
Nicole Forsgren: "Identify the criteria that are most important to you... give everything a score, and just multiply it out." Create a decision-making spreadsheet with options as rows and weighted criteria as columns. The process often reveals the answer before the math is finished.
Geoff Charles: "Be very clear with the tradeoffs... present those tradeoffs back to your leadership team. Here's what we're doing and here's what we're not doing." Communicate what the team is NOT doing as clearly as what they are doing. Present a "menu" of options to force a decision.
John Cutler: "Some people are just locked into the can. They're uber pragmatic... others ask 'What should we do here?'" Don't let feasibility constraints dominate strategic thinking. Explicitly ask what you should do if technical debt weren't an issue.
Julie Zhuo: "Data is not a tool that's going to tell you what you should build... but it can tell you if you have a problem." Use data to identify problems and gaps, but rely on design and intuition to invent solutions.
Stewart Butterfield: "The cost of doing the analysis was this much. So it's guaranteed to be a loser." Evaluate whether the person-hours spent analyzing a decision exceed the maximum possible upside of the improvement.
Ramesh Johari: "Many of the changes that are most consequential create winners and losers." When launching a feature, explicitly identify who will lose and decide if the winners provide more net value to the ecosystem.
For all 42 insights from 40 guests, see references/guest-insights.md
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