我厌倦了大型语言模型的技能混乱，因此我自己构建了一个，并进行了回归测试。
我最近尝试了像Garry Tan的GStack这样的技能，花了一周时间使用它，意识到它存在一些缺陷（我会单独发帖讨论这个问题）。
我的问题是：我怎么知道一个技能或提示是否好用（例如，GStack的 /office-hours）？
我该如何比较类似的技能（例如，不同的“深度研究”技能）？
识别出故障软件（相对来说）是简单的——它会崩溃，打印错误信息。而故障的技能却不会。那些经过精心打磨、听起来很自信的技能常常误导我，浪费我的时间，甚至让我希望自己根本不使用LLM（大型语言模型）。
AI技能是软件——它们应该配备回归测试。
LLM团队有大量的提示回归测试。LLM包装的SaaS公司也有大量的提示回归测试。但在开源技能方面，SKILL.md看起来合理，但却没有任何测试（例如，撰写时GStack的 /office-hours没有任何测试）。
Garry Tan，如果你听到我的声音——请考虑为你的 /office-hours、/plan-ceo-review、/plan-eng-review等技能提供回归测试。
回归测试应该：
1. 证明技能正确运行
2. 演示正确和错误的用法
3. 证明技能的价值
4. 附带评分标准，以便进行技能基准测试
5. 最后一条是最有价值的，因为它可以让你将类似技能进行对比。
所以我开始自己做这件事。
以下是一个正在进行中的示例：plan-cmo-review，这是一个补充GStack的技能，因为在撰写时GStack缺少市场评审。我并不是一个市场营销专家；分享这个技能的目的是概述它的回归设置。
简要来说，我的探索过程如下：
- 我在几个产品上使用了GStack，意识到生成的design_document.md让我失败，主要是在市场营销方面。
- 我借助Claude Opus 4.8手动深入分析了技能的失败，最终找到了正确的解决方案。
- 我请Claude构建了一个plan-cmo-review技能，运行后得到了一个有缺陷的解决方案（类似于GStack的输出）。
- 我给Claude提供了正确的（手动）解决方案进行分析，并将其作为回归测试的基准。
- Claude进行了（盲）回归测试——失败了。我们进行了多次迭代，找到了关键问题：Claude盲目相信我的提示是最终真理。Claude认为GStack知道自己在做什么，而GStack相信我知道自己在做什么。但我实际上是在进行产品/初创公司的研究——根据定义，“研究”就是在你不知道自己在做什么时所做的事情。这个信任链就是导致技能失败的原因。
- 我们解决了信任问题，回归测试通过了。我们又添加了几个，结果也通过了。
- 我让Claude多次运行回归测试——出现了裂缝。Claude对技能进行了迭代。现在它们通过了。
- 这种方法论仍然存在缺陷。我想尝试运行不同的LLM，进行跨模型评估，以及更多的回归测试。
技能链接：github.com/remakeai/plan-cmo-review。更多笔记见：iliaov.substack.com。
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I've recently tried skills like Garry Tan's GStack, spent a week with it, and realized it has some flaws (I'll post separately about that).<p>Here's my problem: how do I know if a skill or prompt is any good (e.g. GStack's /office-hours)?<p>How do I compare similar skills (e.g. different "deep research" skills)?<p>Spotting broken software is (relatively) easy — it crashes, prints errors. Broken skills don't. Perfectly polished, confident-sounding skills routinely mislead me and waste my time, to the point where I wish I weren't using an LLM at all.<p>AI skills are software — and they should come with regression tests.<p>LLM teams have tons of prompt regression tests. LLM-wrapper SaaS companies have tons of prompt regression tests. But when it comes to open-source skills, SKILL.md reads reasonable, yet ships with zero tests (e.g. GStack's /office-hours has none at the time of writing).<p>Garry Tan, if you hear me — please consider shipping regression tests for your /office-hours, /plan-ceo-review, /plan-eng-review, and so on.<p>Regression tests should:<p>1. Prove the skill works correctly<p>2. Demonstrate correct and incorrect usage<p>3. Prove the skill's value<p>4. Come with a scoring rubric to allow skill benchmarking<p>5. The last one is the most valuable, because it lets you benchmark similar skills against each other.<p>So I started doing this myself.<p>Here's a work-in-progress example: plan-cmo-review, a skill to complement GStack since GStack is missing a marketing review at the time of writing. I'm not a marketing guy; the point of sharing this skill is to outline its regression setup.<p>Briefly, here's how my exploration progressed:<p>- I used GStack on a couple of products and realized the resulting design_document.md was leading me to failure, mainly marketing-wise.<p>- I dug into the skill's failures manually with Claude Opus 4.8's help and ended up finding the correct solution.<p>- I asked Claude to build a plan-cmo-review skill, ran it, and it arrived at a flawed solution (similar to GStack's output).<p>- I gave Claude the correct (manual) solution to analyze and add as a regression fixture with a scoring rubric.<p>- Claude ran the (blind) regression — it failed. We iterated several times and found the key problem: Claude was trusting my prompts implicitly as the ultimate truth. Claude believed GStack knew what it was doing. GStack believed I knew what I was doing. But I was doing product/startup research — and by definition, "research" is what you do when you don't know what you're doing. That trust chain is what broke the skills.<p>- We fixed the trust problem and the regression test passed. We added a few more. They passed.<p>- I had Claude run the regressions multiple times — cracks appeared. Claude iterated the skill. Now they pass.<p>- This methodology is still flawed. I'd like to try running different LLMs, cross-model judging, and a lot more regression tests.<p>Skill github.com/remakeai/plan-cmo-review . Notes at iliaov.substack.com .