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Help users select, implement, and optimize a modern product tool stack that reduces operational friction and accelerates delivery cycles.
Passed 20 of 22 test cases with the skill, and 17 of 22 without it.
and 5 more in the full report
5 fixed15 passed either way0 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our product team is using nearly a dozen separate software subscriptions for analytics, feature flags, feedback surveys, and task management, which is causing severe context-switching and high costs. Can you review our situation and provide a structured tool audit and consolidation plan? Please outline how we should establish our core stack foundation, streamline data sharing between tools, and highlight operational pitfalls we ought to watch out for.
| Checked | Without skill | With skill |
|---|---|---|
| Recommends consolidating point solutions into an all-in-one analytics and experimentation platform | Passed | Passed |
| Recommends deploying agentic AI tools for administrative PM workflows like ticket drafting | Missed | Passed |
| Warns against building in-house experimentation tools rather than buying third-party tools | Missed | Passed |
| Emphasizes requiring dedicated engineering resources for tracking instrumentation | Missed | Passed |
The instructions your model reads when the skill loads.
Build a high-performance product toolkit by balancing established standards with AI-native speed.
Help the user with product stack strategy using insights from 6 guests and posts across Lenny's Podcast and Newsletter.
From "A year free of PostHog ($16,500 value): The all-in-one analytics, experimentation, feature flag, surveys, session replay, error tracking, data warehouse, LLM analytics platform": "Being able to follow an issue from a session recording, to its impact in analytics, to shipping a fix as a feature flag, to testing a variant, to collecting feedback with surveys—that’s the holy grail."
Moving toward all-in-one platforms reduces the technical and operational friction of managing multiple point solutions, enabling better integration between discovery and shipping.
From "Five steps to starting your product-led growth motion, part 2": "Tools such as Amplitude and Mixpanel are commonly used here, but, as the saying goes, “garbage in, garbage out.” Companies need to dedicate engineering resources to instrument tracking properly. Many B2B companies are significantly lacking in product analytics—watching product usage closely is less important when you sell via human touch—but without a strong foundation of product analytics, PLG will never work."
Product analytics requires dedicated engineering resources for proper instrumentation; successful PLG is impossible without a robust data foundation.
From "Five steps to starting your product-led growth motion, part 2": "The most common mistake I see is that companies skip buying and jump right into building. In other words, they bypass the option of using a third-party experimentation tool, often because the engineering and product teams feel like they can build anything. But building an experimentation platform requires not only engineering resources but also data science and statistical expertise."
Small teams should choose third-party experimentation tools over homegrown platforms to avoid massive engineering and statistical overhead.
test cases passed, vs 17 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.